cpv_lookupResolve a plain-English procurement category ("construction", "hospital equipment", "IT") to the CPV codes TED indexes it under, or explain what a CPV code covers. Use before search_notices when you know the subject but not the code.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | Plain-English category ("construction") or a CPV code ("45000000", "45"). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds minimal behavioral context (e.g., TED indexing) but does not disclose return format or limitations. No contradiction.
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 concise sentences, front-loaded with the primary action, and no filler. Every word earns its place.
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?
For a single-parameter lookup tool with full schema coverage, safe-read annotations, and no output schema, the description adequately covers purpose, usage, and operational scope. The reverse mode ('explain what a CPV code covers') adds completeness.
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% for the single parameter 'term', with examples and meaning already provided. The description's mention of plain-English and CPV codes largely duplicates the schema, adding no new parameter-level semantics.
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 'Resolve' and specifies the resource (CPV codes) with a bidirectional capability: plain-English to codes, and code explanation. It also distinguishes from sibling search_notices by explicitly naming it and advising usage before it.
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?
Provides explicit guidance with 'Use before search_notices when you know the subject but not the code', giving a clear when-to-use scenario and naming an alternative tool.
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.
Each tool has a clearly distinct purpose described in detail. Even tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are differentiated by hallucination resistance, account requirements, and use cases. Polymarket tools are each specialized (arbitrage, edges, fill risk, etc.). No two tools appear to do the same thing.
All tool names follow snake_case consistently. They use descriptive verb-noun patterns (e.g., ask_pipeworx, bet_research, compare_entities, subscribe). No mixing of conventions or ambiguous names.
32 tools is on the higher side but justified by the server's broad scope covering company research, prediction markets, data lookups, memory, subscriptions, and more. Each tool serves a distinct function, though the count might feel slightly heavy for a single server.
The toolset covers core workflows for company analysis, prediction market operations, data retrieval, and system management (memory, subscriptions). Minor gaps exist (e.g., no direct tool for non-company entity profiles beyond drugs), but the overall surface is comprehensive for the intended multi-purpose assistant.