fda_resolve_companyResolve a company name to its canonical company_id and list all known aliases. Returns the canonical slug, match confidence, and alias names. Read-only lookup — does not discover new aliases. Related: fda_suggest_subsidiaries (discover potential subsidiaries not yet aliased), fda_company_full (full profile using the resolved name).
| Name | Required | Description | Default |
|---|---|---|---|
| company | Yes | Company name to resolve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, openWorldHint=false, and idempotentHint=true. The description adds value by specifying the return contents (canonical slug, match confidence, alias names) and the limitation that it does not discover new aliases. This goes beyond basic annotations, though it does not mention error behavior or performance.
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 three sentences long, front-loaded with the primary purpose, then return details, then related-tool guidance. Every sentence serves a distinct purpose with no redundancy.
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?
With a single simple parameter, strong annotations, and no output schema, the description includes return fields and related tools, making it complete for a lookup tool. There are no unexplained behaviors or missing crucial context.
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 coverage is 100% with the 'company' parameter described as 'Company name to resolve'. The description restates this concept ('Resolve a company name') but adds no new semantics such as formatting, case sensitivity, or examples. The baseline is 3 due to high schema coverage.
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 resource ('company name to canonical company_id'), and clearly states it lists known aliases. It distinguishes from siblings by explicitly saying it does not discover new aliases and references related tools like fda_suggest_subsidiaries and fda_company_full.
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?
It explicitly names alternative tools and when to use them: fda_suggest_subsidiaries for discovering potential subsidiaries not yet aliased, and fda_company_full for a full profile using the resolved name. The 'does not discover new aliases' exclusion clarifies when not to use this 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.
Most tools have distinct purposes with clear boundaries, such as fda_search_drugs for drug applications and fda_search_510k for device clearances. However, some overlap exists, like fda_device_udi and fda_device_udi_lookup both querying UDI data, which could cause confusion despite differences in scope.
All tool names follow a consistent snake_case pattern with a clear fda_ prefix, using descriptive verbs like search, get, list, and link. This uniformity makes the set predictable and easy to navigate, with no deviations in naming style.
With 48 tools, the count is excessive for a single server, making it overwhelming and difficult for agents to manage. While the domain is broad (FDA data), the toolset feels bloated with many specialized or overlapping tools that could be consolidated.
The toolset provides comprehensive coverage of FDA data domains, including drugs, devices, inspections, compliance, recalls, and facilities. It supports full CRUD-like operations (e.g., search, get, link, save) and lifecycle workflows, with no obvious gaps for the intended purpose.