Resolve a company or entity to its public record and cross-linked identifiers (LEI, ISIN, ticker, website, country, inception) via Wikidata.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Company or entity name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden, and it does communicate via 'resolve... via Wikidata' that this is a non-mutating lookup. However, it never explicitly discloses read-only semantics, error or ambiguity behavior, or any source caveats, and 'public record' is a slightly misleading label for a Wikidata-derived profile. Adequate for a simple resolver but no proactive disclosure.
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?
A single 27-word sentence with no filler: the action verb leads, the object and output fields follow, and the data source closes it out. Every element earns its place, and the structure mirrors how an agent would parse the pipeline: input → resolution → output.
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 one-parameter tool with no output schema, the description fully covers inputs, the resolution step, and the expected return structure. The only gaps are minor for the complexity level: no statement on unresolved-entity behavior or ambiguity handling, and 'inception' could confuse (does it mean founding date?). Otherwise 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?
Schema coverage is 100% with 'query' described as 'Company or entity name', so the schema does the heavy lifting (baseline 3). The description earns a bump by enumerating the cross-linked identifiers returned, giving the agent meaningful signal about what constitutes an effective query and what outputs to expect.
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 pairs a specific verb ('Resolve') with a defined resource ('company or entity') and a concrete list of output identifiers (LEI, ISIN, ticker, website, country, inception), plus the source ('via Wikidata'). The 'via Wikidata' mention subtly differentiates it from the LEI and KYB siblings, but this is implicit — the somewhat ambiguous phrase 'public record' and the unexplained 'inception' hold it back from a 5.
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 context is only implied — the agent must infer from 'via Wikidata' when to choose this over lei_lookup, kyb_verify_business, or sanctions_screen, which could all accept a company name. There is no explicit when-to-use or when-not-to-use guidance despite a crowded field of overlapping identifier tools. Minimal but acceptable for a simple lookup.
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 are clearly distinct by resource or function, but fx_rates vs us_treasury_fx and entity_resolve vs lei_lookup could both be plausible choices for the same request. Descriptions generally make the boundaries clear, so the overlap is minor rather than systematic.
All names use lowercase_snake_case, but there is no single naming pattern: tools like btc_network, crypto_price, and weather_forecast are noun phrases, while eu_vat_validate, lei_lookup, and sanctions_screen follow an object+operation style. This mixed convention is readable but noticeably inconsistent.
14 tools is within a reasonable range for a multi-provider data server, and most tools cover a distinct data domain. However, kyb_verify_business heavily overlaps the individual VAT, LEI, sanctions, IBAN, and entity tools, and having two FX rate tools makes the set feel slightly broader than necessary.
The main KYB/entity workflow is well covered, but the set has notable gaps: sanctions screening covers only OFAC and UK OFSi, IBAN validation is structural only, and there are no UBO or adverse-media checks. For the broader data-lookup domain, the coverage is decent but not exhaustive.