Sweep the Blueprint's numeric constraint bounds and report verdict stability: the stable band, the scales where the verdict first flips, and advice. Use before deploying bound changes.
| Name | Required | Description | Default |
|---|---|---|---|
| config | No | Raw blueprint config to profile (used when 'blueprint' is not given) | |
| api_key | Yes | GeodesicAI API key (gai_...) | |
| blueprint | No | Blueprint name (workflow_name) to use |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context about the behavior (sweeping bounds and reporting stability metrics) without contradicting the annotations. It clarifies the analytical, non-mutating nature of the operation and lists what is reported.
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 two sentences, front-loaded with the action verb 'Sweep', and every phrase adds value. The output components are listed cleanly, and the usage instruction is a separate concise sentence.
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?
The tool has no output schema, so the description carries the burden of explaining return values. It lists the three key outputs (stable band, flip scales, advice) and gives a clear use case. While it could elaborate on what 'verdict stability' means, the essential context is present for a read-only analysis tool with good annotations.
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%, so the schema already documents all parameters (config, api_key, blueprint). The description adds minimal parameter-specific meaning beyond the schema, but it does tie 'blueprint' to 'numeric constraint bounds'. Per the rubric, a baseline of 3 is appropriate when schema coverage is high.
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 a specific verb ('Sweep') and clearly identifies the resource ('the Blueprint's numeric constraint bounds') and the outputs ('verdict stability', 'stable band', 'scales where the verdict first flips', 'advice'). This clearly differentiates it from sibling tools like check_blueprint_health or validate.
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 provides a clear usage context: 'Use before deploying bound changes.' This tells when to use the tool, though it does not mention when not to use it or name alternatives. It gives a specific trigger scenario, which is more than a vague implication.
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.
