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by Crawlora-org
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JavaScript
Remote

letterboxd_film_reviews

Retrieve a Letterboxd film's popular reviews using its slug, with reviewer, rating, date, text, like/comment counts, and spoiler flags. Access public review data without credentials.

Instructions

Get a Letterboxd film's popular reviews. Returns a film's popular reviews (reviewer, rating, date, text, like/comment counts, spoiler flag). Credential-free public Letterboxd data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesLetterboxd film slug
limitNoMax reviews, default 10, max 50

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It notes that the data is 'credential-free public Letterboxd data,' indicating no authentication. However, it does not disclose rate limits, error handling, pagination behavior, or what happens with invalid slugs. Adds moderate value beyond the schema.

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?

Two efficient sentences: first sentence clearly states purpose and return fields, second adds credential-free context. No fluff, front-loaded with key information. Excellent conciseness.

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?

No output schema exists, so description should explain return values, which it does (listing fields). It mentions 'popular reviews' implying sorting, but doesn't explicitly state ordering or pagination mechanism. For a tool with moderate complexity (2 params, no nested objects), this is fairly complete but could be slightly more explicit.

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

Parameters3/5

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

Schema coverage is 100%, with both 'slug' and 'limit' described in the schema. The description adds no new meaning beyond what the schema already provides (e.g., 'Letterboxd film slug' is repeated). Baseline score of 3 is appropriate.

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 clearly states the tool gets a Letterboxd film's popular reviews, specifying the returned fields (reviewer, rating, date, text, like/comment counts, spoiler flag). It distinguishes from sibling tools like letterboxd_film (film details) and letterboxd_film_rating_histogram (rating distribution) by focusing on reviews.

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

Usage Guidelines4/5

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

The description implies when to use (for popular reviews) but does not explicitly exclude alternatives or state when not to use. However, the sibling tools are distinct enough that an agent can infer the appropriate context. No explicit 'use this for reviews, other tools for other data' guidance.

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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