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get_inference_trace

Read-onlyIdempotent

Retrieve the durable audit trail for a governed generation: every recorded decision and its reasons.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesGeodesicAI API key (gai_...)
blueprintNoBlueprint namespace used when recording the tracedefault
inference_idYesCaller-chosen ID grouping the steps of one generation
blueprint_versionNoOptional historical blueprint_version hash returned by govern_inference

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / blueprint
      Added value: +{
      +  "default": "default",
      +  "description": "Blueprint namespace used when recording the trace",
      +  "title": "Blueprint",
      +  "type": "string"
      +}
    • addedInput schema / properties / blueprint_version
      Added value: +{
      +  "default": "",
      +  "description": "Optional historical blueprint_version hash returned by govern_inference",
      +  "title": "Blueprint Version",
      +  "type": "string"
      +}
  2. Changed2 schema fields changed
    • addedInput schema / properties / api_key / description
      Added value: +"GeodesicAI API key (gai_...)"
    • addedInput schema / properties / inference_id / description
      Added value: +"Caller-chosen ID grouping the steps of one generation"
  3. Added

TDQS

B3.4/5.0
Behavior3/5

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

The annotations already declare read-only, idempotent, and non-destructive behavior, and the description is consistent with those traits. It adds the useful 'durable' qualifier and states that all recorded decisions with reasons are returned, but it does not disclose operational details such as empty-trace behavior, retention semantics, or ordering.

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 and front-loaded: the action is the first word, the resource follows immediately, and the content of the returned result is summarized in a brief subordinate clause. There is no filler or repeated information.

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?

For a simple read-only retrieval tool with fully annotated parameters and no output schema, the description gives the agent the essential resource and the nature of the result. It is complete enough to form a correct call, and a fuller treatment would only need to add a small caveat about empty traces or return-shape expectations.

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?

The input schema has 100% parameter description coverage, so the baseline is 3 even though the description itself defines no parameter specifics. The reference to a 'governed generation' and 'recorded decision/reasons' weakly relates to inference_id and blueprint_version, but it adds no semantic value beyond what the schema already provides.

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

Purpose4/5

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

The description uses a specific verb ('Retrieve') and a specific resource ('durable audit trail for a governed generation') with a clear statement that the result includes every recorded decision and its reasons. It is clear in isolation, though it does not explicitly differentiate itself from get_execution_trace or recent_inference_decisions by name.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus related siblings such as get_execution_trace, recent_inference_decisions, or handoff_audit. The phrase 'for a governed generation' implies an audit context, but there are no explicit preconditions, alternative routes, or when-not-to-use conditions.

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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