lei_lookup
Global legal-entity lookup via GLEIF open data: pass 'lei' (exact) or 'q' (name search). Compact due-diligence records. Free.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| lei | No |
Global legal-entity lookup via GLEIF open data: pass 'lei' (exact) or 'q' (name search). Compact due-diligence records. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| lei | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description must cover behavior. It mentions 'Compact due-diligence records' and 'Free', but omits read-only status, error handling, rate limits, or output structure. Sparse behavioral disclosure for an unannotated tool.
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?
Exceptionally concise: two short clauses, front-loaded with core purpose. Every word adds value, 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?
Covers purpose, parameters, and output type, but lacks return-field specifics and error scenarios. Since there's no output schema and no annotations, the description could be slightly more complete for agent decision-making.
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 only shows two string fields with no descriptions. Description clearly explains that 'lei' is for exact legal-entity identifier match and 'q' for name search, providing essential meaning beyond the schema.
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?
States it's a global legal-entity lookup via GLEIF open data, with explicit parameter roles ('lei' exact, 'q' name search). Clearly distinguishes from sibling tools like listings, rates, or tracking.
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 and explains which parameter to use for which lookup type. Does not explicitly mention alternatives or when not to use, but the domain is distinct enough among siblings.
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 distinct resources, but fx_rates and kzt_rates both provide exchange rate data and could be easily confused. The remaining tools are clearly separated by purpose.
Tool names are consistently snake_case, but follow two patterns: verb_noun (get_listing, search_listings) and noun_noun (fx_rates, kzt_rates, lei_lookup), with 'weather' being a bare noun. This mix is readable but lacks a unified convention.
With 10 tools, the count is within the ideal range. However, several tools (weather, exchange rates, parcel tracking) seem tangential to a 'marketplace' server, making the scope slightly broad.
The marketplace core is covered with search_listings/get_listing and verify_agent, but there are gaps such as no direct agent search or way to create/update listings. The Kazakhstan-specific utilities are each complete in isolation, but the overall surface lacks a cohesive lifecycle.