blender-ai-mcp

A production-shaped MCP server for Blender.

blender-ai-mcp lets Claude, ChatGPT, Codex, and other MCP clients control Blender through a stable tool API instead of ad-hoc Python generation. The result is a safer, smaller, and more reliable surface for real modeling work: goal-first routing, curated public tools, deterministic inspection, and verification that does not depend on guesswork.

Why This Exists

Most "AI + Blender" setups still ask the model to write raw bpy scripts. That breaks exactly where production work gets interesting:

1. Blender APIs drift across versions.
2. Context-sensitive operators fail when the active object, mode, or selection is wrong.
3. Raw scripts give weak feedback when something goes wrong.
4. Vision can describe a result, but it cannot be trusted as the final authority.

blender-ai-mcp takes the opposite approach: treat Blender control as a product surface, not a code-generation stunt.

Why This MCP Server Instead of Raw Python

• Stable contracts over script synthesis. The model calls tools with validated parameters instead of improvising Blender code.
• Goal-first orchestration. Normal guided sessions start from router_set_goal(...), so the system knows what the model is trying to build before it starts calling low-level actions.
• Small public surface. The default llm-guided profile exposes a tiny, search-first bootstrap layer instead of flooding the model with the whole runtime inventory.
• Truth-first verification. Inspection, measurement, and assertion tools determine what is actually true in Blender.
• Safe execution boundaries. The Blender addon executes operations on Blender's main thread while the MCP server handles routing, validation, discovery, and structured responses.

The Product Approach

The business idea formalized in TASK-113 is simple:

• Atomic tools are the implementation substrate. They stay small, precise, and mostly hidden from the normal public surface.
• Macro tools are the preferred LLM-facing layer for meaningful task-sized work.
• Workflow tools are bounded multi-step process tools with explicit reporting, not open-ended "do anything" endpoints.
• Goal-first orchestration keeps sessions anchored to an active intent instead of making the model rediscover context on every turn.
• Vision assists interpretation, while deterministic measurement and assertions provide the final truth layer.
• Pluggable vision runtimes now cover local MLX plus external OpenRouter and Google AI Studio / Gemini provider paths, with model-family-specific external contract profiles for prompt/schema/parser behavior.

This is what turns the project from "Blender tools exposed over MCP" into a usable AI control product for modeling pipelines.

LLM-Guided Public Surface

llm-guided is the default production-oriented surface. It is intentionally small, search-first, and designed around goal-aware sessions.

Normal guided flow:

1. router_set_goal(...)
2. browse_workflows, search_tools, or call_tool
3. use grouped/public tools such as check_scene, inspect_scene, or configure_scene
4. verify with inspection plus scene_measure_* and scene_assert_*

Prompting rule:

• use the prompt-library assets in _docs/_PROMPTS/README.md as the canonical guided operating instructions
• when a client drifts, prepend guided_session_start as the generic search-first stabilizer
• if a tool is not already directly visible on the current surface/phase, use search_tools(...) before call_tool(...)

When a bounded modeling intent matches, the default public working layer should be the macro layer:

• macro_cutout_recess for recesses, openings, and cutter-driven cutouts
• macro_relative_layout for align/place/contact-gap part layout
• macro_attach_part_to_surface for seating one part onto another object's surface/body
• macro_align_part_with_contact for minimal repair nudges on pairs that almost fit
• macro_place_symmetry_pair for mirrored pair placement/correction around an explicit mirror plane
• macro_place_supported_pair for mirrored pair placement/correction against one shared support surface
• macro_cleanup_part_intersections for bounded pairwise overlap cleanup without free-form collision solving
• macro_adjust_relative_proportion for bounded ratio repair between related objects
• macro_adjust_segment_chain_arc for bounded arc adjustment on ordered segment chains
• macro_finish_form for preset-driven bevel/subdivision/solidify finishing
• reference_images for goal-scoped reference intake before bounded visual comparison
• reference_guided_creature_build as a native prompt asset for staged generic creature work on llm-guided
• recommended_prompts can now steer creature-oriented guided sessions toward that prompt path by using active goal/session context
• guided_reference_readiness on router_set_goal, router_get_status, and staged reference compare/iterate payloads so clients can see whether reference-driven stage work is actually ready
• reference_compare_stage_checkpoint for deterministic multi-view stage comparison against attached references during manual iterative work
• reference_iterate_stage_checkpoint for a session-aware staged correction loop that remembers prior focus, can escalate into inspect/validate when the same correction repeats, and can now target one object, many objects, a collection, or the full assembled silhouette
• stage compare/iterate now also expose deterministic silhouette_analysis metrics, typed action_hints, and an advisory-only part_segmentation placeholder that stays disabled unless a separate sidecar is explicitly enabled
• scene_scope_graph for one explicit read-only structural scope artifact with anchor/core/accessory role hints
• scene_relation_graph for one explicit read-only pair-relation artifact derived from the current truth layer
• scene_view_diagnostics for one explicit read-only view-space artifact with projected extent, frame coverage, centering, and visible/partial/occluded/off-frame verdicts for named cameras or USER_PERSPECTIVE
• those spatial graph/view diagnostics tools are now part of the default visible llm-guided support set so the model can keep one explicit 3D orientation layer available instead of inferring spatial state only from names, screenshots, or partial loop payloads

Current guided bootstrap surface:

• router_set_goal
• router_get_status
• browse_workflows
• reference_images
• scene_scope_graph
• scene_relation_graph
• scene_view_diagnostics
• search_tools
• call_tool
• list_prompts
• get_prompt

Current guided utility prep path:

• bootstrap/planning search can now reach:
  • scene_get_viewport
  • scene_clean_scene
• these utility actions stay bounded and do not reopen the full legacy surface
• the canonical guided discovery wrapper is call_tool(name=..., arguments=...)
• the canonical cleanup argument shape on llm-guided is keep_lights_and_cameras; older split flags are compatibility-only and should not be used as the documented public form
• reference_images(action="attach", source_path=...) is one-reference-per-call; batch-like shapes now fail with guided recovery guidance instead of raw schema noise
• collection_manage(action=..., collection_name=...) stays the canonical public form; legacy name is only a narrow compatibility alias
• modeling_create_primitive(...) stays limited to primitive_type, radius/size, location, rotation, and optional name; unsupported shortcuts such as scale, segments, rings, subdivisions, or primitive-time collection_name now fail with actionable guidance on both direct and proxy guided paths
• build goals should still start from router_set_goal(...), but screenshot / viewport / scene-reset requests should use the guided utility path instead
• if stale scene state is discovered only after entering the guided build surface, scene_clean_scene(...) is also available there as a bounded recovery hatch; cleanup before the goal is still the preferred path
• build-phase cleanup is still allowed when recovery is needed

Current public aliases on llm-guided:

Internal tool	llm-guided public name	Public arg changes
scene_context	check_scene	action -> query
scene_inspect	inspect_scene	object_name -> target_object
scene_configure	configure_scene	settings -> config
workflow_catalog	browse_workflows	workflow_name -> name, query -> search_query

Why that matters:

• the guided profile starts from 8 visible tools instead of the full catalog
• grouped/public tools stay easy to discover
• hidden atomic tools remain available as infrastructure, not as the default public mental model
• specialist families stay out of the normal guided entry layer until the macro surface is broader

Atomic Foundations And Docs

The root README.md is intentionally not the full tool catalog anymore.

The detailed tool inventory and atomic family docs should stay in docs, not on the front page. That is the right long-term structure after TASK-113.

Use these docs depending on what you need:

• Tool Layering Policy
  • Canonical policy for atomic / macro / workflow, hidden atomic tools, goal-first usage, and vision/assert boundaries.
• MCP Server Docs
  • Surface profiles, guided aliases, versioned contracts, and runtime/platform guidance.
• MCP Client Config Examples
  • Ready-to-paste local MCP client config examples for guided/manual surfaces plus MLX, OpenRouter, and Gemini vision variants.
• Vision Layer Docs
  • Runtime/backends, capture bundles, reference images, macro/workflow vision integration notes, and repo-tracked real viewport eval bundles for both direct user-view and fixed camera-perspective captures.
• LLM Guide v2
  • Strategy doc for a typed spatial-intelligence layer, compact relation state, and bounded next-step handoffs for guided operation.
• Spatial Intelligence Research Brief
  • External research handoff for LLM/VLM spatial reasoning, multi-view reasoning, and geometry-aware planning.
• Spatial Intelligence Upgrade Proposal
  • Research-driven upgrade proposal for scene graphs, symbolic relation notation, and supporting geometry-library choices.
• Available Tools Summary
  • Full inventory and grouped/public tool overview.
• Tool Architecture Index
  • Maintainer-facing map of the tool families underneath the MCP surface.

If you want to see the atomic families the server is built on, start here:

• Scene Tool Architecture
• Modeling Tool Architecture
• Mesh Tool Architecture
• Mega Tool Architecture

Recommended interpretation:

• keep /_docs/TOOLS/ as the maintainer-facing atomic/grouped architecture map
• keep README.md product-facing and compact
• keep /_docs/AVAILABLE_TOOLS_SUMMARY.md as the runtime inventory

Provider Notes

Current short version:

• Local default: mlx_local with a Qwen VL 4B-class model path; current repo-validated baseline is mlx-community/Qwen3-VL-4B-Instruct-4bit
• External iterative compare candidate: OpenRouter with x-ai/grok-4.20-multi-agent
• External Google-family compare path: OpenRouter-hosted Google-family models plus Google AI Studio / Gemini now share the same narrow staged-compare contract through resolved vision_contract_profile routing

External vision runtime note:

• VISION_EXTERNAL_PROVIDER selects the transport/provider branch
• VISION_EXTERNAL_CONTRACT_PROFILE optionally overrides the prompt/schema/parser contract for external compare flows
• when the override is unset, the runtime auto-matches Google-family model ids such as gemma / gemini / learnlm, then falls back to provider defaults

Detailed per-provider table:

• Vision Layer Docs -> Provider Notes

Architecture

The system is split on purpose:

• MCP server (server/): FastMCP surface, public tool definitions, transforms, discovery, and response contracts.
• Router (server/router/): goal interpretation, safety/correction policy, workflow matching, session context, and guided execution behavior.
• Blender addon (blender_addon/): actual bpy execution, RPC handlers, and Blender main-thread-safe operation scheduling.

Communication happens through JSON-RPC over TCP sockets.

More detail:

• ARCHITECTURE.md
• Router Docs
• Runtime Responsibility Boundaries
• Addon Docs

Structured Contract Baseline

The server is moving critical surfaces toward machine-readable payloads instead of prose-heavy JSON strings.

Current structured-contract baseline includes:

• macro_cutout_recess
• macro_finish_form
• macro_attach_part_to_surface
• macro_align_part_with_contact
• macro_place_supported_pair
• macro_cleanup_part_intersections
• macro_relative_layout
• scene_create
• scene_configure
• mesh_select
• mesh_select_targeted
• mesh_inspect
• scene_snapshot_state
• scene_compare_snapshot
• scene_measure_distance
• scene_measure_dimensions
• scene_measure_gap
• scene_measure_alignment
• scene_measure_overlap
• scene_assert_contact
• scene_assert_dimensions
• scene_assert_containment
• scene_assert_symmetry
• scene_assert_proportion
• router_set_goal
• router_get_status
• workflow_catalog

That is important for automation, auditing, and future macro/workflow composition.

Contact Truth Semantics

For contact-sensitive checks on curved or rounded forms, the truth layer now distinguishes:

• mesh-surface contact/gap semantics when a bounded mesh-aware path is available
• bbox fallback semantics when a mesh-aware path is not available

That means a pair can still show bbox contact while the main measured relation remains separated if the real mesh surfaces still have a visible gap. Guided hybrid truth follow-up now carries that distinction forward in operator-facing summaries instead of collapsing it into a generic "contact passed/failed" claim.

When the mesh-aware path finds a real overlap, the main measured relation also stays overlapping, so overlap rejection in scene_assert_contact(...) still works as a separate truth condition instead of collapsing into plain contact.

Structured Clarification Flow

The guided surface supports missing-input handling as part of the product contract, not as an afterthought.

• Model-first clarification is the default for router_set_goal(...) on llm-guided: missing workflow parameters return a typed needs_input payload to the outer model first.
• Typed fallback payloads keep the same flow usable on tool-only or compatibility clients.
• Human/native clarification is reserved for later/fallback policy rather than the default first step of workflow execution.
• router_set_goal(...) can ask for constrained choices, booleans, enums, or workflow confirmation.
• partial answers survive across follow-up turns.
• workflow_catalog import conflicts reuse the same clarification model.

Guided Handoff Contract

The guided surface now treats workflow fallback as an explicit typed contract instead of a phase side effect hidden in prose.

• router_set_goal(...) returns guided_handoff on bounded continuation paths such as continuation_mode="guided_manual_build" and continuation_mode="guided_utility".
• guided_handoff names the target_phase, direct_tools, supporting_tools, and discovery_tools for the next step on llm-guided.
• workflow_import_recommended stays False on these fallback paths unless the user explicitly asks for workflow import/create behavior.
• router_get_status(...) preserves the active guided_handoff in session diagnostics so clients can recover the intended continuation path.

Server-Driven Guided Flow State

The guided surface now carries one explicit machine-readable guided_flow_state contract in addition to guided_handoff.

• router_set_goal(...), router_get_status(...), reference_compare_stage_checkpoint(...), and reference_iterate_stage_checkpoint(...) can expose guided_flow_state for the active llm-guided session
• guided_flow_state reports:
  • flow_id
  • domain_profile
  • current_step
  • completed_steps
  • active_target_scope
  • spatial_scope_fingerprint
  • spatial_state_version
  • spatial_state_stale
  • last_spatial_check_version
  • spatial_refresh_required
  • required_checks
  • next_actions
  • blocked_families
  • allowed_families
  • allowed_roles
  • completed_roles
  • missing_roles
  • required_role_groups
  • required_prompts
  • preferred_prompts
  • step_status
• current domain overlays are:
  • generic
  • creature
  • building
• early guided build sessions now start from a step-gated spatial-context phase instead of exposing the whole build surface immediately
• scene_scope_graph(...) binds the active guided target scope when no active scope exists yet; spatial refresh checks must keep using that already-bound target scope instead of rebinding to a different object set
• unrelated view checks such as scene_view_diagnostics(target_object="Camera", ...) do not satisfy a creature/building spatial check by themselves
• if reference images are attached for the active guided goal, treat them as the primary grounding input before deciding the first body/head/tail masses and rough silhouette
• use full semantic object names such as Body, Head, Tail, ForeLeg_L, and HindLeg_R instead of opaque abbreviations like ForeL / HindR, because guided seam/role heuristics are more reliable on readable names
• on llm-guided, the server can now warn on weak role-sensitive names and block clearly opaque placeholder names such as Sphere / Object when they are used as semantic part names
• do not call scene_scope_graph(...), scene_relation_graph(...), or scene_view_diagnostics(...) with no explicit scope and assume that means “inspect the whole scene”
• during an active guided spatial gate or spatial refresh re-arm, all three of those spatial helpers should be treated as explicit-scope tools, not as whole-scene probes
• those pinned read-only spatial helpers remain callable while visible on llm-guided; guided family blocking must not reject scene_scope_graph(...), scene_relation_graph(...), or scene_view_diagnostics(...) simply because the current build step's allowed_families omits spatial_context
• outside that guided gate, the scope/relation graph builders still require an explicit target_object, target_objects, or collection_name; a bare call now fails instead of silently returning an empty scene scope
• default placeholder scopes such as a stock Cube or the generic root Collection are no longer treated as meaningful guided target/workset bindings by themselves
• but for the earlier “is this scene already non-empty?” bootstrap decision, Blender's stock Cube plus stock camera/light helpers still enters the empty-scene primary-workset bootstrap path
• this non-empty decision is intentionally name-light after startup: real multi-object rough blockouts with default primitive names such as Cube or Sphere still count as existing geometry, while helper-only scenes can still enter bootstrap_primary_workset
• explicit guided scopes now bind from caller intent instead of name heuristics, so real objects named like Cube, Sphere, or Sunflower can still become the active guided workset when the operator targets them
• after material scene changes such as scene_clean_scene(...), scene_duplicate_object(...), scene_rename_object(...), modeling_create_primitive(...), modeling_transform_object(...), modeling_join_objects(...), modeling_separate_object(...), or bounded attachment/alignment macros, the guided runtime can mark the spatial layer stale and re-arm the required checks
• that same dirty-state update now reapplies FastMCP visibility immediately, so clients see the required spatial support tools as soon as spatial_refresh_required is persisted
• guided mesh edit tools such as mesh_extrude_region(...), mesh_loop_cut(...), and mesh_bevel(...) are now mapped to the secondary_parts family, so they are blocked during spatial-context gates and re-arm spatial checks after successful geometry edits
• when one of those required spatial checks completes and advances the guided flow, the server now reapplies FastMCP visibility immediately instead of waiting for a later status/search refresh
• support/symmetry-aware relation pairs now preserve support and symmetry annotations even when they share the same (from_object, to_object) key as a generic primary-target pair, so later guided planners still see support/symmetry semantics instead of only a generic edge
• relation graphs that include required creature seams still add fallback primary_to_other pairs for non-seam objects in the requested scope, so unclassified objects do not disappear from mixed guided diagnostics
• healthy support/symmetry pairs no longer count as failing just because their centers differ or they are not literal contact pairs; only unsupported / asymmetric support/symmetry verdicts count as failures there
• when guided_flow_state.spatial_refresh_required == true, treat next_actions=["refresh_spatial_context"] as authoritative server state, not advisory prose; refresh with scene_scope_graph(...) against the already-bound target scope first, then rerun the remaining required spatial checks on that same scope
• scene_view_diagnostics(...) only counts toward the guided spatial gate when it returns real available view-space evidence; a headless/unavailable probe stays read-only and does not satisfy the required check by itself
• if stage compare/iterate finds important issues while the current guided role/workset slice is still incomplete, the governor can now keep the session in bounded build continuation instead of escalating too early into inspect_validate
• when that incomplete-stage hold returns loop_disposition="continue_build", the persisted guided_flow_state remains on the same current step and does not mark the unfinished role slice as completed; keep following missing_roles before relying on later-stage visibility
• this incomplete-stage hold also applies when stage iterate has no correction_focus or action_hints; a no-action compare result must not advance a guided build with required missing roles to finish_or_stop
• after the flow reaches a later step such as place_secondary_parts, the server can still keep missing primary masses available when they are part of the same bounded workset, instead of forcing a squirrel/building run to abandon an unfinished core mass immediately
• for creature blockout seams, intersecting can still be acceptable for embedded ear/head or snout/head placement, but floating_gap on head/body, tail/body, or limb/body remains actionable
• if a needed tool family is hidden/blocked-by-flow, inspect router_get_status().guided_flow_state, complete the listed required_checks, and follow next_actions instead of guessing hidden tool names into call_tool(...)
• if an explicit guided goal stayed on a manual/no-match path, a strong pattern-suggested workflow can still expand; what remains suppressed in that state is the lower-confidence heuristic reopening path
• exact tool-name searches on the guided surface are now shaped to return a tighter, smaller result set instead of flooding the model with a full expanded payload for simple lookups
• for role-sensitive build steps, treat allowed_roles and missing_roles as part of the execution contract, not as advisory prose
• housekeeping/workset operations such as collection_manage(...) should stay available for already-created objects even when their semantic role was registered in an earlier step
• bounded refinement of an already-registered primary object can remain possible after the session moves into the next step; later steps are not meant to freeze all earlier masses completely
• use guided_register_part(object_name=..., role=...) as the canonical way to tell the server what semantic part one object represents; optional guided_role=... hints on build tools are convenience-only
• optional role_group=... values must match the server's domain role map; callers cannot reclassify body_core, head_mass, or similar role-sensitive mutating calls as utility or another family to bypass the current guided phase gate
• guided_register_part(...) now validates that the named Blender object actually exists before it can count toward guided role completion; typos do not create completed roles on their own
• if guided object validation cannot read the Blender scene at all, guided_register_part(...) now fails clearly instead of mutating guided session state from an unverified object name
• explicit target names passed into scene_scope_graph(...) / scope-building paths now follow the same Blender-truth validation rule before the guided scope can bind
• those optional guided_role=... hints only auto-register when an active guided flow already exists; outside an active guided flow they do not create persistent role state by themselves
• a failed create call now stays non-mutating for guided role state as well: if modeling_create_primitive(...) returns a failure string, the requested role is not auto-registered just because a semantic name was supplied
• on modeling_create_primitive(...), guided_role=... now also requires an explicit semantic name; guided create does not allow auto-generated Blender names to become semantic part registrations
• when the router prepends corrective steps such as scene_set_mode(...), successful guided create/transform calls still register the resulting role against the final modeling step instead of dropping the convenience registration just because the call became multi-step
• guided-role convenience registration now also handles valid object names containing apostrophes, such as King's Crown, instead of truncating the stored object name
• guided runtime success parsing also treats apostrophes inside quoted object names as part of the object name for create/transform/rename/join results, so stale-state marking and guided registry sync still run after successful mutations
• canonical pair names such as ForeLeg_L, ForeLeg_R, and ForeLegPair now count as strong semantic names for foreleg_pair / hindleg_pair instead of warning or blocking under the stricter naming policy
• on modeling_create_primitive(...), guided-role auto-registration now binds to the actual created object name returned by Blender, so role state stays aligned even when Blender auto-numbers a default name such as Cube.001 or uses a different default object name such as Suzanne
• successful scene_rename_object(...) calls now keep the guided part registry aligned with the renamed Blender object, so later role-sensitive transforms still recover the registered role without manual re-registration
• successful scene_rename_object(...) calls also re-arm guided spatial checks, because the bound target-scope fingerprint is name-based
• successful scene_duplicate_object(...) calls also re-arm guided spatial checks, because duplication changes the visible workset/scope relation facts
• failed plain-string mutation results such as Object 'Missing' not found now stay non-mutating for guided session state; they do not re-arm spatial checks or rewrite guided role registration just because the wrapper returned a string
• scene_clean_scene(...) now clears the guided part registry and returns the guided flow to bootstrap_primary_workset instead of carrying completed parts forward on an empty scene
• starting a different guided goal in the same session now resets guided part registration for that new flow instead of carrying completed roles forward from the previous object
• destructive identity/topology changes such as modeling_join_objects(...) or modeling_separate_object(...) now drop stale guided part registrations; re-register the resulting object(s) explicitly if they should still count toward guided role completion
• those same destructive topology changes also re-arm guided spatial checks, because previously captured scope/view facts are no longer trustworthy after objects were merged away or split apart
• for macro capture/vision artifacts, macro_attach_part_to_surface(...) now refreshes its post-action capture bundle after the extra mesh-surface nudge, so attached images and truth summary describe the final seated pose instead of the pre-nudge intermediate pose
• routed macro reports can be partial and still carry an error; MCP adapters preserve that structured report, including actions_taken, modified objects, verification recommendations, capture/truth data, and follow-up guidance, instead of coercing it into an empty failed envelope
• if the optional segmentation sidecar is enabled on runtime config but not yet executed on the current compare path, staged compare/iterate responses now report part_segmentation.status="unavailable" instead of silently staying disabled
• if the server warns or blocks on guided naming, rename or create the object using one of the suggested semantic names instead of retrying the same weak abbreviation
• guided naming and guided spatial role inference now use token-boundary style matches instead of raw substring hits, so names such as Heart or TruthBodyAnchorHead do not become accidental semantic ear/body/head roles
• the required prompt bundle and preferred prompt bundle named in guided_flow_state are prompt asset names, not a replacement for the server-driven flow; prompts support the flow, they do not become the flow

Guided Reference Readiness

Reference-driven staged work now has one explicit readiness contract instead of hidden ordering assumptions.

• router_set_goal(...) and router_get_status(...) expose guided_reference_readiness.
• the payload reports attached_reference_count, pending_reference_count, compare_ready, iterate_ready, plus machine-readable blocking_reason and next_action
• reference_images(action="attach", source_path=...) can stay pending until the guided goal session is actually ready, then adopt automatically
• if the same goal already has active refs and new ones are staged during needs_input, the staged refs stay separate from the already-active goal references until readiness returns
• if a ready session still carries explicit pending refs for another goal, reference_images(action="list"| "remove"| "clear", ...) now treats that merged visible set consistently instead of leaving broken pending records
• reference_compare_stage_checkpoint(...) and reference_iterate_stage_checkpoint(...) now fail fast when the session is not ready, and echo the same guided_reference_readiness payload
• if reference_iterate_stage_checkpoint(...) returns loop_disposition="inspect_validate", stop free-form modeling and switch to inspect/measure/assert immediately
• if it returns loop_disposition="continue_build" while guided_flow_state.missing_roles is still non-empty, continue the current role slice; the server intentionally keeps the guided step in place instead of advancing to the next stage, even when the compare result itself produced no actionable correction hints
• if staged compare degrades but strong deterministic truth findings still exist, use the same inspect/measure/assert handoff instead of improvising another large free-form correction
• error-stage iterate handoffs that move to inspect_validate or finish_or_stop also reapply guided visibility before returning
• for staged compare/iterate, goal_override is no longer a session substitute; use an active guided goal session instead
• for collection or multi-object staged captures, the capture focus now falls back to the assembled target scope's primary target when no explicit target_object is supplied
• deterministic silhouette metrics prefer the target/focus capture for the requested target_view, not the broad context_wide capture
• reference_compare_current_view(..., persist_view=True, view_name=..., orbit_horizontal=..., zoom_factor=...) keeps the captured user view and does not replay those same view adjustments a second time during compact view diagnostics

Session Diagnostics

Guided/runtime payloads now expose explicit MCP session metadata:

• router_set_goal(...) includes session_id and transport
• router_get_status(...) includes session_id and transport
• reference_compare_stage_checkpoint(...) includes session_id and transport
• reference_iterate_stage_checkpoint(...) includes session_id and transport

Current runtime guidance:

• stateful streamable HTTP is the recommended transport for longer guided runs and for debugging session-aware reference / checkpoint flows
• recent guided-session hardening removed the known router bookkeeping path that could clobber active goal/reference session state during routed tool execution
• if you investigate a future state-loss incident, compare session_id and transport first to distinguish:
  • transport/session reconnects
  • application-level goal resets
  • normal guided readiness blockers such as missing goal or references

Server-Side Sampling Assistants Baseline

The MCP server now has a bounded analytical assistant layer inside an active request.

Current use cases:

• optional assistant_summary on inspection-heavy paths such as scene_snapshot_state, scene_compare_snapshot, scene_get_hierarchy, scene_get_bounding_box, and scene_get_origin_info
• bounded repair_suggestion on router_set_goal, router_get_status, and workflow_catalog

Explicit assistant terminal states:

• success
• unavailable
• masked_error
• rejected_by_policy

The rule is strict: assistants may help summarize or suggest, but they do not override scene truth or router policy.

Versioned Surface Baseline

Public surface evolution is versioned explicitly:

Surface profile	Default contract line
legacy-manual	legacy-v1
legacy-flat	legacy-v1
llm-guided	llm-guided-v2

Compatibility note:

• llm-guided-v1 remains selectable as a rollback line
• workflow_catalog, scene_context, and scene_inspect participate in the guided surface evolution story

Code Mode Decision

Current benchmark baselines:

• legacy-flat
• llm-guided
• code-mode-pilot

Current decision:

• Go decision: keep code-mode-pilot as an experimental read-only surface
• Do not make Code Mode the default path for write-heavy or geometry-destructive Blender work

Support Matrix

• Blender: tested on Blender 5.0 in E2E coverage; addon minimum remains Blender 4.0+ on a best-effort basis.
• Python: 3.11+
• FastMCP task runtime: fastmcp 3.1.1 + pydocket 0.18.2
• OS: macOS / Windows / Linux
• Memory: router semantic features rely on a local LaBSE model and related vector infrastructure

Quick Start

1. Install the Blender addon

1. Download blender_ai_mcp.zip from the Releases page or build it locally with python scripts/build_addon.py.
2. Open Blender -> Edit -> Preferences -> Add-ons.
3. Click Install... and select the zip file.
4. Enable the addon. It starts the local Blender RPC server on port 8765.

2. Run the MCP server on the guided profile

Recommended defaults:

• ROUTER_ENABLED=true
• MCP_SURFACE_PROFILE=llm-guided
• map /tmp if you want host-visible image/file outputs

Example Docker command:

docker run -i --rm \
  -v /tmp:/tmp \
  -e BLENDER_AI_TMP_INTERNAL_DIR=/tmp \
  -e BLENDER_AI_TMP_EXTERNAL_DIR=/tmp \
  -e ROUTER_ENABLED=true \
  -e MCP_SURFACE_PROFILE=llm-guided \
  -e BLENDER_RPC_HOST=host.docker.internal \
  ghcr.io/patrykiti/blender-ai-mcp:latest

docker run --rm \
  -p 8000:8000 \
  -v /tmp:/tmp \
  -e BLENDER_AI_TMP_INTERNAL_DIR=/tmp \
  -e BLENDER_AI_TMP_EXTERNAL_DIR=/tmp \
  -e ROUTER_ENABLED=true \
  -e MCP_SURFACE_PROFILE=llm-guided \
  -e MCP_TRANSPORT_MODE=streamable \
  -e MCP_HTTP_HOST=0.0.0.0 \
  -e MCP_HTTP_PORT=8000 \
  -e MCP_STREAMABLE_HTTP_PATH=/mcp \
  -e BLENDER_RPC_HOST=host.docker.internal \
  ghcr.io/patrykiti/blender-ai-mcp:latest

Example generic MCP client config:

{
  "mcpServers": {
    "blender-ai-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-v", "/tmp:/tmp",
        "-e", "BLENDER_AI_TMP_INTERNAL_DIR=/tmp",
        "-e", "BLENDER_AI_TMP_EXTERNAL_DIR=/tmp",
        "-e", "ROUTER_ENABLED=true",
        "-e", "MCP_SURFACE_PROFILE=llm-guided",
        "-e", "BLENDER_RPC_HOST=host.docker.internal",
        "ghcr.io/patrykiti/blender-ai-mcp:latest"
      ]
    }
  }
}

Network notes:

• macOS / Windows: use host.docker.internal
• Linux: prefer --network host with BLENDER_RPC_HOST=127.0.0.1
• MCP_TRANSPORT_MODE=stdio keeps the current subprocess/stdio MCP mode
• MCP_TRANSPORT_MODE=streamable starts a stateful Streamable HTTP MCP server

For broader profile/config examples, use:

• MCP Server Docs
• MCP Client Config Examples
• .env.example for the full tracked runtime/config variable set

Testing

Unit tests:

PYTHONPATH=. poetry run pytest tests/unit/ -v

Unit collection count:

poetry run pytest tests/unit --collect-only

E2E tests:

python3 scripts/run_e2e_tests.py

E2E collection count:

poetry run pytest tests/e2e --collect-only

Pre-commit:

poetry run pre-commit install --hook-type pre-commit --hook-type pre-push
poetry run pre-commit run --all-files

More detail:

• Test Docs
• Development Docs

Documentation Map

• Architecture
• MCP Server Docs
• Vision Layer Docs
• Router Docs
• Router Responsibility Boundaries
• Addon Docs
• LLM Guide v2
• Spatial Intelligence Research Brief
• Spatial Intelligence Upgrade Proposal
• Available Tools Summary
• Tool Architecture Index
• Prompts
• Tasks

Contributing

Read CONTRIBUTING.md before opening a PR. The repo enforces Clean Architecture boundaries, typed Python, router metadata rules, and pre-commit validation.

Community And Support

If blender-ai-mcp is useful in your workflow, consider sponsoring its long-term development.

Sponsorship helps fund maintenance, docs, testing, and the higher-level reliability work that makes this repo different from raw Blender code generation: goal-first routing, curated tools, deterministic verification, and production-shaped workflow support.

Become a sponsor

• SUPPORT.md
• SECURITY.md
• CODE_OF_CONDUCT.md
• GitHub Sponsors
• Buy me a coffee

Author

Patryk Ciechański

• GitHub: PatrykIti

License

This project is licensed under the Apache License 2.0.

See:

• LICENSE.md
• NOTICE