Check whether recent submissions still match the established pattern for this Blueprint. Returns a stability verdict and observation count.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | GeodesicAI API key (gai_...) | |
| blueprint | No | Blueprint name (workflow_name) to use | default |
| structured_data | Yes | The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide idempotent, non-negative, and non-read-only hints. The description adds that the tool returns a stability verdict and observation count, but it does not disclose potential side effects (consistent with readOnlyHint=false) or explain how the verdict should be interpreted.
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 concise sentences, front-loaded with the primary action and quickly stating the return value. Every sentence adds value with no redundancy or fluff.
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?
Given there is no output schema, the description should more thoroughly explain the 'stability verdict' format and how the observation count is to be used. It provides a high-level return but leaves the agent without enough detail to confidently interpret the results.
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?
The input schema already describes all parameters with 100% coverage. The description does not add parameter-specific details; it only mentions the high-level concept of checking against a blueprint pattern, which helps but is not essential beyond the schema.
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 clearly states the tool's purpose with a specific verb ('Check') and resource ('recent submissions vs established pattern for this Blueprint'). It distinguishes itself from sibling tools like check_blueprint_health by focusing on drift detection rather than overall health.
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 a clear use case (monitoring whether submissions still match a blueprint's pattern), but it does not explicitly mention when to prefer this tool over alternatives or provide exclusions. Sibling tools with overlapping functionality are not referenced.
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.
