crypto-quant-signal-mcp v1.20.0 ships composite verdict layer for AI trading agents
`crypto-quant-signal-mcp` `v1.20.0` from AlgoVault.com introduces structured validation errors, formalized `DRYRUN_MODE` support, and tighter MCP protocol compliance to its composite signal verdict tool for AI trading agents.
Score breakdown
The release replaces multi-indicator reconciliation inside the agent loop with a single opinionated verdict output, removing the regime-modeling and data-hygiene burden that the post describes as the structural cause of agent coordination failures in production trading pipelines.
- 01v1.20.0 adds structured validation errors on `get_trade_signal`, formalized `DRYRUN_MODE`, and tighter MCP protocol compliance
- 02The `get_trade_signal` tool returns a single composite verdict — quant weighting, regime classification, and cross-venue data fusion — without exposing intermediate indicator state to the agent
- 03AlgoVault claims a 91.6% PFE win rate across 237,570+ verified calls, described as Merkle-anchored on Base L2
AlgoVault.com's `crypto-quant-signal-mcp` `v1.20.0` addresses what the post frames as a structural coordination problem in AI trading pipelines: a glut of raw indicators — RSI, MACD, volume, funding rate divergence, order flow imbalance — that fire independently and leave the agent loop to arbitrate contradictions with no shared ground truth. The post identifies three compounding failure modes in typical in-process pipelines: indicator disagreement as the baseline state (requiring a regime model most teams skip), single-venue data that makes cross-venue microstructure signals invisible, and silent error propagation with no audit trail when indicator calculations fail.
The solution AlgoVault ships is a boundary between signal interpretation and agent execution.
The solution AlgoVault ships is a boundary between signal interpretation and agent execution. The `get_trade_signal` tool returns one composite verdict — encoding quant weighting, regime classification, and cross-venue data fusion — along with a confidence score and regime context, without exposing intermediate indicator state to the agent. The post cites a claimed track record of a 91.6% PFE win rate across 237,570+ verified calls, described as Merkle-anchored on Base L2. `v1.20.0` specifically adds structured validation errors on `get_trade_signal`, formalizes `DRYRUN_MODE` for quota-free integration validation, and tightens MCP protocol compliance. The server requires no build step, starts via `npx` on first invocation, and integrates into Claude Desktop, Claude Code, and Cursor through a standard MCP JSON config block. A free tier offering 100 calls/month is described, alongside a Telegram bot for live signal access with no API key required. The source text is truncated before full details of the `v1.20.0` validation error changes are provided.
Key facts
- 01v1.20.0 adds structured validation errors on `get_trade_signal`, formalized `DRYRUN_MODE`, and tighter MCP protocol compliance
- 02The `get_trade_signal` tool returns a single composite verdict — quant weighting, regime classification, and cross-venue data fusion — without exposing intermediate indicator state to the agent
- 03AlgoVault claims a 91.6% PFE win rate across 237,570+ verified calls, described as Merkle-anchored on Base L2
- 04`DRYRUN_MODE` allows integration validation without consuming quota or triggering live side effects
- 05The server installs with no build step via `npx` and uses a standard MCP JSON config compatible with Claude Desktop, Claude Code, and Cursor
- 06A free tier of 100 calls/month is available for programmatic access; a Telegram bot offers live signals with no API key required
- 07The post identifies three failure modes in typical in-process trading pipelines: indicator disagreement, single-venue data blindness, and silent/unauditable error propagation
Topics
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