dsh-algovault
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
Install
dsh plugin --profile <name> add github:AlgoVaultLabs/dsh-algovault
Then 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.
Tools
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.
Tiers
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.
Model Experience
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.
Known Limitations
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.
Links
License
MIT. See LICENSE.
