Retrieve the durable audit trail for a governed generation: every recorded decision and its reasons.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | GeodesicAI API key (gai_...) | |
| inference_id | Yes | Caller-chosen ID grouping the steps of one generation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe read behavior. The description adds useful context about durability ('durable audit trail') and completeness ('every recorded decision and its reasons'), which goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the verb, and every phrase adds value. It efficiently communicates the tool's purpose and the nature of the returned data without any filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 two well-documented parameters and no output schema, the description provides sufficient context: it defines the audit trail, its scope ('governed generation'), and its content ('every recorded decision and its reasons'). This gives the agent a complete picture of what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds no additional parameter-level semantics. The parameter descriptions in the schema already document the API key format and the inference_id purpose, so the tool description is not required to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Retrieve' with a clear resource: 'the durable audit trail for a governed generation' and specifies the content ('every recorded decision and its reasons'). This clearly distinguishes it from sibling tools like get_execution_trace or recent_inference_decisions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when needing an audit trail of decisions and reasons) but does not explicitly contrast it with alternatives or state when not to use it. There is no mention of sibling tools or exclusionary context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but several pairs overlap heavily (validate vs validate_repair, repair vs repair_path, analyze_anomaly vs check_drift vs decompose_failure). Detailed descriptions help disambiguate, but the large number of analytics and diagnostics tools creates real selection risk.
The vast majority use a consistent snake_case verb_noun pattern (create_blueprint, list_api_keys, verify_certificate). A few single-word or noun-phrase exceptions (validate, forecast, structural_types, recent_inference_decisions) are minor deviations, but overall the pattern is predictable.
At 37 tools, this exceeds the 25+ threshold for 'too many'. While the governance domain is broad, the set could be consolidated (e.g., merging validate_repair into validate, folding repair_path into repair, or trimming diagnostics-tier tools like check_realization and geometric_confidence).
The surface covers the full blueprint lifecycle, validation, repair, API key management, discovery, inference governance, and chain management. Minor gaps exist: no direct get_blueprint (only list with counts), and chain lifecycle lacks delete/list/cancel operations. Overall, agents can accomplish core governance tasks without dead ends.
