Replace an API key with a fresh one. The old key stops working immediately; the new key inherits its bindings.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | GeodesicAI API key (gai_...) | |
| key_to_rotate | Yes | The gai_ key to rotate; it stops working immediately |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true and readOnlyHint=false, but the description adds valuable context: the old key stops working immediately and the new key inherits bindings. This goes beyond the annotation and helps the agent anticipate consequences. It doesn't cover auth requirements or rate limits, but for this tool the disclosed behaviors are sufficient.
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?
Two sentences, front-loaded with the core action, no filler. Each clause contributes: replacement, immediate invalidation, binding inheritance. This is a model of concise, well-structured tool documentation.
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 is simple (2 params, no output schema) and the description covers the main behavior and consequences. However, it does not state what the response contains—specifically, whether and how the new key is returned. Since the new key is essential for use, this omission creates a meaningful gap. Without an output schema, the description should have mentioned the return value.
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 coverage is 100%: both parameters have clear descriptions (api_key is the GeodesicAI API key, key_to_rotate is the target key). The description repeats the effect on key_to_rotate but does not add new meaning beyond the schema. This matches the baseline score of 3 for high schema coverage.
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 'Replace' with the resource 'API key', clearly distinguishing this rotation tool from sibling tools like delete_api_key and list_api_keys. It also states the outcome (fresh one, old stops working, new inherits bindings), leaving no ambiguity about the tool's function.
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 clearly implies when to use this tool (when you want to replace a key while preserving bindings), but it does not explicitly mention when not to use it or name alternative tools. For example, it does not say 'If you want to permanently delete a key, use delete_api_key instead.' This is a gap, so it stops short of full guidance.
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.
