Resolve a US company, bank or nonprofit to its registry entity. Use when you have a name, ticker context or an identifier and need the entity plus all known registry anchors (CIK, LEI, EIN, UEI, RSSD). IDs beat names — prefer 'cik:0000936468', 'lei:...', 'ein:...', 'uei:...', 'qid:Q7240'.
| Name | Required | Description | Default |
|---|---|---|---|
| name_or_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and idempotent behavior, so the safety profile is covered. The description adds useful behavioral context beyond that: it resolves rather than searches, returns multiple anchor types, and treats IDs as preferred over names with concrete prefix examples.
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?
Three short sentences with no filler. The purpose is front-loaded, the usage condition follows, and the ID-preference guidance with concrete examples closes it efficiently. Every sentence earns its place.
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 single-parameter resolver with no output schema, the description covers the key needs: what the tool does, when to use it, what the result contains, and how to format the input. Remaining details like error behavior or ambiguous-match handling are minor given the read-only, idempotent annotations and simple interface.
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?
The schema has zero description coverage, so the description fully carries the burden for the sole parameter. It explains that name_or_id accepts names, ticker context, or identifiers, and gives exact prefixed formats (cik:, lei:, ein:, uei:, qid:), which is excellent compensation for the sparse 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?
The description uses a specific verb ('Resolve') and clearly identifies the resource and outcome: a US company, bank, or nonprofit resolved to its registry entity with all known anchors. It does not explicitly call out sibling tools, but the resolution-focused purpose is distinct enough from the listed search/template/history siblings.
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 gives an explicit 'Use when...' condition covering name, ticker context, or identifier, and states what the caller gets: the entity plus registry anchors. It does not spell out when not to use it or name alternatives, but the context provided is clear and actionable.
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.
get_facts, query_template, and search_facts all return fact-like results and can overlap when an agent needs a specific reported value; the descriptions attempt to differentiate curated vs. full-text paths, but the boundary is not crisp. lookup_entity, list_templates, and mcp_server_history are clearly distinct.
Most tools follow a verb_noun pattern: get_facts, list_templates, lookup_entity, query_template, search_facts. mcp_server_history breaks the pattern by using a bare noun phrase, which is inconsistent with the rest of the set.
Six tools is a well-scoped size for a fact-registry MCP server. The count is neither bloated nor too thin, though mcp_server_history feels somewhat unrelated to the core fact-querying purpose.
The server covers the main read-side needs of the domain: entity resolution, full-text search, direct fact retrieval, and template-based structured queries. Minor gaps exist, such as no tool to fetch a single fact by fact_url or inspect historical changes to a fact, but core workflows are supported.