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Glama

Tv Recommend

tv_recommend

Generate personalized TV show and movie recommendations from your watch history and trending titles. Filter by mood like chill, action, or kids.

Instructions

Get personalized recommendations based on watch history + trending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
moodNo"chill", "action", "kids", "random", or omit for auto.
limitNoNumber of recommendations (default 5).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.2
  2. Removedv0.3.0
  3. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It conveys a read-only retrieval implied by 'Get' and explains what drives the results (watch history + trending), but says nothing about side effects, permissions, or rate limits. Barely adequate rather than informative.

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?

A single front-loaded sentence with zero filler. Nothing redundant or padded.

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

Completeness3/5

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 no explanation, and the two parameters are schema-documented. However, given the many overlapping siblings, the definition is thin on routing and usage context.

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 100%, so both 'mood' (with its enumerated values) and 'limit' are already fully documented in the schema. The description adds no meaning beyond that, which lands at the baseline 3.

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 states a specific verb (Get) and resource (personalized recommendations) and discloses the data sources (watch history + trending). It does not distinguish itself from plausible siblings such as tv_whats_on or tv_insights, so it falls short of a 5.

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?

There is no when-to-use, when-not-to-use, or alternative-tool guidance. An agent cannot tell from this text alone whether to reach for tv_recommend versus tv_whats_on, tv_history, or tv_insights.

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