imdb-analytics-mcp

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Search People

search_people

Read-onlyIdempotent

Find people by name and identify the correct person using birth year, profession, and credit counts. Get IMDb IDs to disambiguate lookalikes before further actor or movie queries.

Instructions

Find people by name and pick the right one before spending another call on them.

Answers questions like: "Who is James Hong?", "Is there more than one Michael Caine?", "Find the Danny Trejo who acts", "Which Sarah Douglas has the most credits?", "What is Bess Flowers' IMDb id?".

Returns each match with its imdb_name_id, birth and death years, listed professions, credit count under the default definition, prominence score and best-known titles, ordered exact match first, then prefix, then credit count. Use the imdb_name_id it returns for every later call rather than passing the name again. filters accepts the same person filter as rank_people, which is how you disambiguate two people with the same name by birth year or profession.

This is lookup, not ranking: it never scans the whole catalogue, so do not use it to answer "who has the most credits" or any question with a superlative in it. It cannot search by nationality, gender or award, which need enrichment that does not land until M4, and it matches on the primary name only, not alternate spellings or credited-as names.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes
`limit`	No
`filters`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 9, 2026

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent annotations, the description discloses real behavioral traits: exact-match-first ordering, the fields returned, the fact that it never scans the whole catalogue, primary-name-only matching, and M4-dependent filter limitations. No statement contradicts the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence carries information: concrete examples, return semantics, ordering, cross-tool filter reuse, and explicit non-goals are all packed in without filler. The most decision-relevant claim—lookup, not ranking—appears early.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists and the description already covers return fields, ordering, filter semantics, and limitations, an agent has everything needed to select and invoke this tool correctly. The only omitted item, limit behavior, is minor and inferable from the schema default.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Yet 0% top-level schema description coverage, the desc">ription compensates for the required 'name' parametir and 'filters' object, e"plaining that filters mirror rank_people and are used to disambiguate same-name people by birth year or profession. It does not e"plicitly e"plain 'limit', though the schema's default of 20 and the return-ordering sentence make its role inferable, so the compensation is strong but not total.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource—'Find people by name and pick the right one'—and immediately separates the tool from ranking tools by declaring 'This is lookup, not ranking.' Example questions make the intended use unmistakable, and the final sentence names concrete unsupported dimensions (nationality, gender, award) that distinguish it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use context ('before spending another call on them'), tells the agent to use the returned imdb_name_id for later calls, and issues clear exclusions: do not use for superlative questions, nationality/gender/award searches, or alternate-name matching. It also points to rank_people for the shared filter vocabulary, giving the agent a route to the sibling tool's semantics.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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