ChatGPT connector fetch: the full company document for one CUI id, as returned by a prior search call. Identical to get_company with no include= (never officers — use get_officers for that, separately). Use get_company instead unless you are the ChatGPT Apps SDK connector, which requires this exact name.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
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?
With no annotations, the description carries the burden and does so well: it states the return scope ('full company document'), the id source ('as returned by a prior search call'), and the exclusion of officers. It does not cover auth, errors, or rate limits, but the behavioral contract is meaningfully transparent.
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 compact and front-loaded: the core behavior appears first, followed by the relationship to similar tools and the exact exception requiring this tool. No sentence is wasted.
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 simple one-parameter fetch tool, the description covers the main need: what it fetches, where the id comes from, and when to prefer get_company. The lack of an output schema is mitigated by 'full company document', though explicit error/not-found behavior would make it fully complete.
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 schema provides no description for 'id', but the tool description compensates by defining it as a CUI id returned by a prior search call. This tells an agent where the value comes from and what kind of identifier is expected.
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 identifies the operation: fetching the full company document for a CUI id returned by a prior search call. It also distinguishes the tool from siblings by stating it is identical to get_company without include= and explicitly excludes officers, which get_officers handles.
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?
Usage guidance is explicit: use get_company instead unless the caller is the ChatGPT Apps SDK connector, which requires this exact name. It also names get_officers as the correct tool for officers, providing clear routing among alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct domains: company identity, financials, legal, officers, procurement, stats, and CAEN lookup. The only ambiguity comes from the redundant connector aliases (fetch vs get_company, search vs search_companies), but their descriptions explicitly call out the duplication and direct agents to the canonical tools.
The set mostly follows a clear verb_noun convention: get_company, get_financials, get_officers, list_companies, search_companies, resolve_caen. Minor deviations are company_stats (noun_verb) and the bare connector aliases fetch and search, but all names are lowercase and underscore-separated, so the pattern remains predictable.
With 12 tools, the surface is well-scoped for a company information/registry API. Each tool covers a meaningful slice of the domain—search, company details, financials, legal, officers, procurement, stats, metadata, and CAEN resolution—without redundant or trivial additions beyond the two explicitly labeled connector aliases.
For a read-only company data API, the coverage is thorough: name/CUI resolution, company identity with optional includes, full financial history, legal records, officers, procurement, and aggregate statistics are all present. There are no obvious dead ends; even data freshness and completeness are addressed by get_meta.