Mount the AlgoVault MCP server in DeepSeek Harness with one command.
This bundle ships a preconfigured @deepseek-ai/dsh-mcp-client row pointed at
https://api.algovault.com/mcp. Your agent gets composite BUY / SELL / HOLD
trade calls, market regime, cross-venue funding arbitrage and the live track
record, as native tools.
Built by AlgoVault Labs — algovault.com
dsh plugin --profile <name> add github:AlgoVaultLabs/dsh-algovaultThen restart that profile. Bundle membership is read at start, not hot-reloaded.
From the dsh.pub registry, the pinned form is:
npx dshpub add AlgoVaultLabs/dsh-algovault --ref <commit>There is no build step and no key to configure. pnpm must be on PATH;
dsh plugin forwards to it.
Every tool arrives namespaced as mcp__algovault__<tool>.
| Tool | Returns |
|---|---|
get_trade_call |
Composite BUY / SELL / HOLD verdict for one perpetual futures asset, with confidence and regime |
scan_trade_calls |
Ranked verdicts across the top perps by open interest, in one call |
get_market_regime |
TRENDING_UP / TRENDING_DOWN / RANGING / VOLATILE, with a strategy hint |
scan_funding_arb |
Ranked cross-venue funding spreads for delta-neutral carry |
get_track_record |
Aggregated PFE win rates by call type, timeframe and asset tier, plus the methodology |
search_knowledge |
Ranked snippets on tool parameters, response shapes and integration patterns |
chat_knowledge |
A synthesized answer with citations over the same knowledge bundle |
get_trade_signal |
Back-compat alias of get_trade_call. Prefer get_trade_call in new work |
The bundle also ships a skill at skills/algovault-verdicts/SKILL.md that
teaches the model which tool answers which question. Copy it into
~/.dsh/skills/ to load it.
The free tier is anonymous. Install, restart, call — no key, no signup.
Paid tiers raise the quota and unlock the full funding-arb result set. Add the
header in your profile's own cordis.patch.yml, not here, so an update to
this bundle never overwrites your key:
- id: mcp-algovault
name: '@deepseek-ai/dsh-mcp-client'
config:
serverName: algovault
transport: streamable-http
url: https://api.algovault.com/mcp?src=dsh-bundle
headers:
Authorization: !!js `Bearer ${process.env.ALGOVAULT_API_KEY}`Set ALGOVAULT_API_KEY to your key, which looks like av_live_.... A patch
replaces the whole config, so restate every field above, not only headers.
Current quotas and tiers: api.algovault.com/signup.
The model sees the tools above under the mcp__algovault__ prefix. Each returns a
structured verdict rather than raw indicator values, so the model reads a
decision and its confidence instead of assembling one.
A verdict of HOLD is a real answer, not a failure. The model should report it and stop, rather than retrying with different parameters until a directional call appears. Confidence and market regime belong in the reply beside every verdict; a BUY in a VOLATILE regime is a weaker claim than a BUY in a trending one.
AlgoVault supplies the thesis. It places no orders and holds no funds. The model should never present a verdict as an instruction to execute.
Win rates and coverage figures change. The model should quote them from a
get_track_record response, never from memory.
DeepSeek Harness is a developer preview and its own README warns of
compatibility-breaking changes. Every published version is a release candidate.
This bundle is deliberately thin for that reason: one client row, no wrappers
around harness internals. Verified against @deepseek-ai/dsh@0.1.1-rc.2 and
@deepseek-ai/dsh-mcp-client@0.1.1-rc.2 on 2026-08-30.
The bundled skill is not auto-discovered. The harness scans project, custom and user skill roots, and a bundle's own directory is none of those, so the copy step above is required.
MCP resources and prompts are not bridged by the harness. Tools only.
The endpoint is a hosted HTTP service. If it is unreachable at startup the harness still boots and logs an error, and the AlgoVault tools are absent for that session.
MIT. See LICENSE.