Signal Ledger Financial Intelligence

by io.github.blf2097

Author Track Record / 作者历史成绩

get_author_track_record

Read-onlyIdempotent

Paid lookup ($0.02 USDC): return a financial author's or stock commentator's resolved prediction history, sample size, hit rate, Wilson confidence interval, coverage, breakdowns and freshness. Useful for creator credibility, investment-influencer reputation and due-diligence workflows. / 收费查询（0.02 USDC）：返回财经作者或股票博主的历史观点成绩、命中率、样本量、置信区间、覆盖范围和数据新鲜度。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "handle": "<handle>"
    },
    "name": "get_author_track_record"
  }
}

Input Schema

Name	Required	Description	Default
`type`	No	Filter claims by stock, industry or broad-market scope.	all
`handle`	Yes	X/Twitter author handle, with or without @.
`window_days`	No	Historical lookback window in days.

Output Schema

Name	Required	Description	Default
`ok`	Yes
`mode`	No
`tool`	No
`error`	No
`analysis`	No
`scorecard`	No
`request_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already mark it read-only and idempotent; the description adds useful behavioral details beyond those, notably the $0.02 USDC cost and that it returns resolved prediction history with sample size, hit rate, confidence interval, and freshness. No contradictions.

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 compact, front-loads the key purpose and cost, and avoids fluff. The English and Chinese versions are consistent and appear in the same string without clutter.

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?

There is an output schema, and the three parameters are fully documented in the schema. The description adds useful context about cost and typical use cases. It does not need to explain return values, so nothing important is missing.

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%, and the description does not add parameter-level meaning beyond listing result metrics. The schema already documents handle, window_days, and type, so the baseline of 3 applies.

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 uses a specific verb (

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?

The description gives clear context: it is for creator credibility, influencer reputation, and due-diligence workflows. It does not explicitly contrast with the sibling tool analyze_financial_report_risk or state exclusions, so it stops short of a perfect score.

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

All Signal Ledger Financial Intelligence tools Inspect

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