dsh-algovault

Readme

# 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](https://algovault.com)

## Install

```sh
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](https://dsh.pub) registry, the pinned form is:

```sh
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:

```yaml
- 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](https://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

- [Integration guide](https://algovault.com/integrations/deepseek-harness)
- [Track record](https://algovault.com/track-record)
- [Issues](https://github.com/AlgoVaultLabs/dsh-algovault/issues)

## License

MIT. See [LICENSE](LICENSE).