Self-description of the curated query path: all templates with parameters, required fields, allowed enum values and plausibility bounds. Call once before the first query_template use.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already cover readOnly and idempotent behavior, so the description correctly avoids repeating those. It adds useful behavioral context by describing what the listing contains, including schema details and plausibility bounds, which is valuable given there is no output schema.
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 compact: one sentence front-loads the tool's purpose and contents, and a second gives actionable usage guidance. The phrase 'Self-description of the curated query path' is slightly abstract, but otherwise 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 zero-parameter, read-only listing tool without an output schema, the description adequately explains what will be returned and when to call it. It could be even clearer about response format or whether the list is sorted, but nothing essential is missing for correct invocation.
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 tool has zero parameters and the schema fully covers parameter semantics, so the description carries no parameter-documentation burden. The mention of parameters and enum values in the description refers to the contents of the returned templates, not this tool's own parameters.
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 a specific verb ('list') and resource ('templates'), and specifies the content: parameters, required fields, allowed enum values, and plausibility bounds. It also distinguishes itself as the setup step for query_template, which sets it apart from the sibling tools.
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 explicitly says 'Call once before the first query_template use,' which gives a concrete timing condition and a clear context for use. It does not enumerate when-not-to-use alternatives, but the stated prerequisite role is sufficient for such a simple zero-parameter tool.
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.