Skip to main content
Glama

search_title

Search OMDb by title with fuzzy matching and optional year filter to identify media accurately for your torrent pipeline.

Instructions

Fuzzy-search OMDb by title, optionally narrowed by year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNo
titleYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral disclosure burden. It does reveal that matching is fuzzy and that year narrows results, which is useful. However, it does not mention whether results are returned in a particular order, any rate limits, or whether the search can fail on ambiguous titles.

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 a single focused sentence that front-loads the core behavior and the optional modifier. There is no redundant filler or repetition of the tool name.

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?

For a simple two-parameter search tool with an output schema, the description covers the essential call semantics: what to search and an optional filter. It is slightly incomplete in not explaining expected year formatting or how this differs from related title lookup tools, but overall it provides enough context for a basic correct invocation.

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 description coverage is 0%, but the description adds meaningful semantics by labeling the title search as fuzzy and the year as an optional narrowing filter. It does not provide detailed format expectations for the year string or clarify how fuzzy matching behaves at boundaries.

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?

The description clearly states the tool performs a fuzzy search on OMDb by title, with an optional year filter. It identifies the resource (OMDb) and the operation (fuzzy-search), but it does not explicitly distinguish itself from sibling tools such as lookup_title or search_torrents.

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

Usage Guidelines2/5

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

The description gives no guidance on when to prefer this tool over alternatives like lookup_title, identify_title, or search_torrents. The only usage hint is the optional year narrowing, which implies filtering but does not explain when this search should be chosen.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/luisriverag/torrent_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server