Search Verbaterm's judged, citation-checked public policy corpus (companies, products, policy kinds). Returns derived review metadata — findings counts, scores, freshness — never raw policy text.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Company or product name to search. | |
| limit | No | Max results (default 20, max 50). | |
| category | No | Optional audience category slug. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully bears the burden of behavioral disclosure. It clearly states that the tool returns 'derived review metadata — findings counts, scores, freshness — never raw policy text', which is a critical behavioral trait. It does not cover authentication or rate limits, but for a search tool, the key behavior is well-described.
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, well-structured sentence that efficiently conveys the tool's purpose, scope, and return type. Every word adds value, and it is front-loaded with the most important information.
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 the low complexity (3 simple parameters, no output schema), the description provides sufficient context about the corpus type and return value. It does not detail search semantics (e.g., fuzzy matching), but the schema descriptions partially cover that. Overall, it is adequate for agent use.
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 baseline is 3. The description does not add any new meaning beyond what the schema already provides for the three parameters (q, limit, category). It rephrases but does not enhance understanding.
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 verb 'Search', the resource 'Verbaterm's judged, citation-checked public policy corpus', and the scope 'companies, products, policy kinds'. It also specifies what it returns (derived review metadata) and what it does not (raw policy text), making it distinct from siblings like list_policies.
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 this tool (searching for derived metadata) and when not (for raw text), but does not explicitly list alternative tools or conditions. It provides clear context for the tool's purpose, but lacks formal exclusion statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
