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rayaneferr

mcp-cinema

by rayaneferr
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Python
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search_films

Find movies by plot, theme, or mood using natural language, then filter by genre, director, or release year. Use it to find matching film records from Wikipedia synopses.

Instructions

Recherche sémantique de films par intrigue, thème ou ambiance.

Args:
    query: description libre de ce qu'on cherche (ex. « un homme revit la même journée en boucle »).
        Les synopsis sont en anglais ; une requête en anglais donne souvent de meilleurs résultats.
    genre: filtre texte sur le genre (ex. « comedy », « horror »).
    director: filtre texte sur le réalisateur (ex. « Nolan »).
    year_min: année de sortie minimale (seulement si l'utilisateur précise une période).
    year_max: année de sortie maximale (seulement si l'utilisateur précise une période).
    limit: nombre de films à retourner (1-20).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
genreNo
limitNo
queryYes
directorNo
year_maxNo
year_minNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the burden and discloses meaningful behavioral traits: this is semantic (not keyword) retrieval, the underlying corpus is English, and English queries improve results, plus the 1-20 limit bound. It omits ranking behavior, empty-result handling, and pagination, so not quite a 5.

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

Conciseness4/5

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

The purpose is front-loaded in one sentence, followed by a compact per-argument list where every line adds information absent from the schema. Slightly verbose formatting for what is essentially a 6-param list, but nothing is redundant.

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

Completeness4/5

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

An output schema exists, so return values need not be described, and all six parameters plus the semantic-search behavior are covered. The main gaps are the absence of any exclusion relative to get_film and no guidance on result handling, which for a simple search tool is a minor shortfall.

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?

Schema description coverage is 0%, so the description must compensate, and it documents all six parameters with examples ('un homme revit la même journée en boucle', 'comedy', 'Nolan'), clarifies that year bounds apply only when a period is given, and states the limit range 1-20. This fully covers the schema gap.

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

Purpose4/5

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

States a specific verb ('Recherche sémantique') plus resource ('films') and scoping modes ('par intrigue, thème ou ambiance'), which clearly marks it as a semantic search rather than a fetch-by-id. It does not name the sibling get_film, so an agent must infer the distinction, keeping it below 5.

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

Usage Guidelines3/5

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

Offers useful invocation hints (queries in English work better since synopses are English; year filters only when the user specifies a period), but never states when to use this tool versus get_film or what to do when no results are found. Usage is implied rather than contrasted with alternatives.

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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