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AlgoChains

AlgoChains MCP Server

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by AlgoChains

get_signal_trade_correlation

Read-onlyIdempotent

Audits signal-to-trade correlation by joining signal traces to trade logs and returning NULL-rate KPIs, including fill coverage and P&L gaps. Excludes unfilled signals to avoid inflating null rates.

Instructions

Read-only signal->trade traceability audit. Joins signals_trace to trade_log and returns NULL-rate KPIs (fill_id_coverage, placed_price_coverage, bracket intent nulls, P&L gap, per-column null rates). Thin wrapper over the control-tower correlation-audit script (runs --json --no-slack) — does not post to Slack. Defaults to filled-only rows so unfilled signal-only rows do not inflate fill-stage NULL rates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of recent signals_trace rows to scan (default 50).
actionNosignals_trace action filter (default submitted).submitted
filled_onlyNoRestrict KPIs to rows with fill evidence (default true, matches the daily launchd run).
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive. The description adds that it defaults to filled-only rows and does not post to Slack, which are useful behavioral details beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the core purpose, and every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides enough detail for an agent to understand the tool's function and output, including specific KPIs. No output schema, but the description lists the key metrics returned.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for all 3 parameters. The description doesn't add new semantic meaning beyond confirming default values, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a read-only audit joining signals_trace to trade_log, returning specific NULL-rate KPIs. It distinguishes itself from sibling tools like bracket_integrity_check by specifying signal->trade traceability.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

While it doesn't explicitly list when to use or alternatives, it mentions it is a thin wrapper over a specific script and that it does not post to Slack, providing context on its non-intrusive nature.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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