Resolve a company name to its authoritative identifiers across registries in one call: GLEIF LEI, legal name, country, entity status, and SEC EDGAR CIK plus last filing date. Use to ground company facts before trusting them.
| Name | Required | Description | Default |
|---|---|---|---|
| companies | Yes | Company names (e.g. ["Apple Inc", "Lockheed Martin"]) | |
| countryCode | No | Optional ISO country code filter (e.g. "US") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool resolves company names to authoritative identifiers and lists the returned fields, but does not mention important traits such as behavior on missing names, rate limits, or authentication requirements. This is adequate but incomplete.
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 two sentences long, front-loads the key information (verb, resource, outputs) in the first sentence, and provides usage context in the second. Every sentence adds value with no wasted words.
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 lack of output schema and annotations, the description does a good job listing the identifiers returned and giving a usage context. It could be more complete by addressing potential error scenarios or batch behavior, but overall it is sufficient for an agent to understand the tool.
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 schema already documents both parameters. The description adds minimal meaning beyond what the schema provides, as it merely restates the function and lists outputs. The baseline of 3 is appropriate.
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 'resolve' and the resource 'company name to authoritative identifiers across registries'. It lists specific outputs (GLEIF LEI, legal name, country, entity status, SEC EDGAR CIK plus last filing date), distinguishing it from siblings like lookup_lei and search_sec_filings by emphasizing the bundling of multiple registries in one call.
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 provides a usage recommendation ('Use to ground company facts before trusting them') but lacks explicit guidance on when not to use this tool or how it compares to alternatives like lookup_lei or search_sec_filings. The context is implied but not elaborated.
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 target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
