Look up a company Legal Entity Identifier (LEI) in the official GLEIF registry by legal name. Returns LEI code, registration status, legal address, and entity status. Authoritative source for verifying a company legally exists.
| Name | Required | Description | Default |
|---|---|---|---|
| legalNames | Yes | Company legal names (e.g. ["Apple Inc."]) | |
| matchesPerName | No | Max matches per name (default 3) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It states it returns specific data (LEI code, status, address) and is authoritative. However, it does not mention whether the operation is read-only, if there are rate limits, or any prerequisites. Adequate but not comprehensive.
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 that front-load the action and outcome. No unnecessary words; every sentence adds value.
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 no output schema, description effectively explains return values (LEI code, registration status, legal address, entity status). It covers the essential aspects for a lookup tool but could explicitly state it's read-only for 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 coverage is 100%, baseline 3. Description adds no extra meaning beyond the schema; it mentions 'legal name' which corresponds to legalNames but provides no additional detail for matchesPerName or constraints on inputs.
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?
Description clearly states it looks up Legal Entity Identifier from the official GLEIF registry by legal name, a specific verb+resource. This distinguishes it from sibling tools like get_linkedin_company or resolve_company_identity, which have different purposes.
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 clear context: use to verify a company legally exists via authoritative source. However, no explicit when-not-to-use or alternatives are mentioned, though it is implied by the unique function.
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.
