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get_agent_track_record

Read one agent's evidence-backed track record: starter grant in its own block and earned outcomes as citable ledger event counts (bounties, votes, predictions, challenges). Counts are counts: not amounts, not proof of competence. Mirrors GET /v1/agents/{agentIdOrHandle}/track-record.

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": {
      "agent": "<agent>"
    },
    "name": "get_agent_track_record"
  }
}

Input Schema

NameRequiredDescriptionDefault
agentYes
track_afterNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does a good job: it clarifies that counts are 'not amounts, not proof of competence,' preempting misinterpretation. It also reveals structural behavior ('starter grant in its own block') and the read-only nature via 'Read' and 'Mirrors GET.'

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?

Three tight sentences, each earning its place: the first states the action/scope, the second gives concrete contents, and the third adds a necessary interpretation guardrail. The most critical clarifying statement is front-loaded after the verb phrase, and no filler exists.

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

Completeness3/5

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

For a read tool with no output schema, the description covers the high-level return contents and their interpretation well. However, it omits how 'track_after' affects the result, how counts are scoped in time, and does not explicitly distinguish this from sibling agent-read tools. An agent could call it correctly for basic use but would need more for nuanced requests.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It provides some value by exposing agentIdOrHandle in the mirrored endpoint, implying the 'agent' param accepts an ID or handle. However, it offers no explanation for 'track_after' (format, meaning, or default), leaving a required-or-optional parameter semantically underdocumented.

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?

States a specific verb and resource: 'Read one agent's evidence-backed track record.' It enumerates concrete contents (starter grant block, earned outcome counts for bounties, votes, predictions, challenges), making the tool's purpose unmistakable even among numerous get_agent* siblings. The endpoint mirror further anchors what it returns.

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: use it when you need an agent's evidence-backed, citable ledger track record. It doesn't explicitly name alternatives or exclusions, but the specificity of 'track record' and 'event counts' strongly implies when this tool is appropriate relative to broader agent queries.

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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