Validate and audit an OpenAPI document for missing operation IDs, descriptions, responses, and consistency problems.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | yaml | |
| document | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Structured OpenAPI audit result | |
| meta | Yes | ||
| service | Yes | ||
| version | Yes | ||
| request_id | Yes | Unique request identifier |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds the audit scope but does not detail what happens during execution (e.g., parse errors, limit behavior). No contradiction with annotations.
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 a single sentence, directly front-loaded with the core purpose, and every word adds value. No redundancy or 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?
The tool has moderate complexity given the output schema and annotations, but the description does not address edge cases or clarify what 'consistency problems' encompasses. Parameter semantics are also absent, leaving the overall context somewhat incomplete.
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 has 0% description coverage, and the description does not explain the 'document' or 'format' parameters. While the parameter names and enum hint at their role, the description fails to clarify expected formats or that 'document' is likely an inline string, leaving ambiguity.
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 validates and audits OpenAPI documents, listing specific checks (operation IDs, descriptions, responses, consistency). This distinguishes it from sibling tools like openapi-test-generate and data.schema-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 implies usage for auditing OpenAPI documents but does not explicitly state when to use this over alternatives or provide exclusions. No mention of sibling tools or conditions that make this tool preferable.
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.
Tools are grouped into clear domain prefixes (crypto, data, developer, document, research, web) and each tool name describes a specific function; however, a few umbrella tools like web.full-audit and data.contract overlap with their more targeted counterparts, creating minor ambiguity.
All tool names follow a consistent pattern: a domain prefix, a dot, and a hyphenated lowercase compound name (e.g., crypto.base-block-inspect, web.seo-audit). This makes naming predictable and easy to scan.
At 63 tools, the surface area is very large and exceeds the 50+ threshold for extreme mismatch. While the tools are organized into six domains, the sheer number makes it difficult for an agent to select efficiently, and some tools are bundled combinations of others.
Each domain offers a thorough set of operations: crypto covers address, account, block, contract, events, gas, and transaction inspection; data covers cleaning, conversion, schema, and validation; developer covers code review, dependency/license audits, and test generation; research covers SEC, OFAC, GLEIF, and USAspending; web covers extraction, SEO, security, and performance. No obvious dead ends exist for the read-only/inspection purpose.