imdb-analytics-mcp

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Get Person

get_person

Read-onlyIdempotent

Retrieve a person's complete IMDb career profile: credit count, career span, top titles, genres, and collaborators under a customizable credit definition.

Instructions

Everything known about one person, recomputed under whatever credit definition you pass.

Answers questions like: "Tell me about Samuel L. Jackson", "How many movies has Eric Roberts actually been in?", "What are Danny Trejo's best-known films?", "Is Bess Flowers mostly an extra?", "How long has James Hong been working, and in what genres?".

Takes an IMDb name id, either "nm0000168" or its numeric form 168. Returns the person's credit count under the definition you gave, career span, top titles by votes with the billing position and characters, genre and decade breakdowns, frequent collaborators, and the precomputed career, recognition and background-work statistics with their components exposed. This is the one tool that returns full component detail; the ranking tools return compact rows.

The recognition and background-work blocks are always the default-definition values and do not move when you change the credit definition, which the response states. Change the definition to answer follow-ups directly: "restrict to theatrical films" is include_tv_series=false, include_tv_movies=false, include_video=false; "count every episode" is include_tv_episodes=true with count_series_as_one=false.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`person_id`	Yes
`top_titles`	No
`credit_definition`	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

A5/5.0

Behavior5/5

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

The description adds meaningful behavioral detail beyond the readOnly/idempotent annotations: recognition and background-work blocks always use default definitions and do not change with the credit definition, and the voice filter is an approximation via Animation genre. These non-obvious behaviors are exactly what an agent needs to avoid misinterpreting results.

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 earns its place: core purpose, example queries, summary of return fields, distinction from siblings, and parameter guidance. It is front-loaded with the essential sentence and uses short paragraphs and examples rather than filler.

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?

For a tool with three parameters and a complex nested credit_definition object, the description fully covers the important semantics, caveats, and usage patterns. With an output schema present, return-value details are not needed. Nothing an agent needs to call this tool correctly is missing.

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

Parameters5/5

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

Although schema coverage is listed at 0%, the description compensates richly: it explains the person_id format (nm string or numeric form), clarifies the credit_definition object with named presets and concrete flag combinations, and reveals behavioral quirks like include_adult skewing lists. It adds practical semantics that the raw schema alone would not convey.

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?

Description states a specific verb and resource ('Everything known about one person') and distinguishes itself from siblings by noting it is the only tool returning full component detail while ranking tools return compact rows. The examples of questions ('Tell me about Samuel L. Jackson') make the use case instantly recognizable.

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?

Description explicitly explains when to use this tool versus ranking tools, gives concrete question types, and shows how to adjust the credit definition for follow-ups with specific flag examples. It even maps natural language requests like 'restrict to theatrical films' to exact parameter settings, leaving little ambiguity.

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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