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Glama

Tmdb

by io.github.pipeworx-io

Tv Episode

tv_episode
Read-onlyIdempotent

Fetch details for a specific TV episode by tv_id, season_number, and episode_number. Returns episode name, overview, air date, runtime, vote average, guest stars, and crew.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tv_idYes
languageNo
season_numberYes
episode_numberYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoEpisode ID
nameNoEpisode name
air_dateNoAir date
overviewNoEpisode overview
vote_averageNoAverage vote rating
season_numberNoSeason number
episode_numberNoEpisode number

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "episode_number": 1,
+    "season_number": 1,
+    "tv_id": 1396
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "air_date": {
+      "description": "Air date",
+      "type": "string"
+    },
+    "episode_number": {
+      "description": "Episode number",
+      "type": "number"
+    },
+    "id": {
+      "description": "Episode ID",
+      "type": "number"
+    },
+    "name": {
+      "description": "Episode name",
+      "type": "string"
+    },
+    "overview": {
+      "description": "Episode overview",
+      "type": "string"
+    },
+    "season_number": {
+      "description": "Season number",
+      "type": "number"
+    },
+    "vote_average": {
+      "description": "Average vote rating",
+      "type": "number"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context on returned fields but no additional behavioral traits beyond what annotations provide.

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?

One sentence, front-loaded with essential information, no redundancy. Every part is necessary.

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?

The tool has an output schema, so return values are covered. The description adequately specifies what is fetched and identifies key fields, making it complete for a fetch operation.

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%. The description explains tv_id, season_number, and episode_number but omits the language parameter, adding partial value but not fully compensating for low coverage.

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 uses a specific verb- Fetch details for a specific TV episode by tv_id, season_number, and episode_number and lists returned fields, clearly distinguishing it from siblings like tv, tv_season, search_tv.

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?

The description implies usage when you have a specific episode, but does not explicitly state when to use versus alternatives like tv_season for listing episodes, nor does it mention when not to use.

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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TDQS

B3.4/5.0
Disambiguation2/5

The tool set blends two unrelated domains (TMDB and Pipeworx). Among Pipeworx tools, several overlap heavily (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, suggest_questions) with vague boundaries, making it hard for an agent to pick the right one. TMDB tools are distinct but the overall mixture creates confusion about which domain a request belongs to.

Naming Consistency3/5

All tools use snake_case, which is consistent. However, naming styles vary widely: TMDB tools use simple noun or verb-first names (movie, search_movie, discover_tv), while Pipeworx tools use longer descriptive phrases with prefixes (ask_pipeworx, polymarket_arbitrage, entity_profile). The pattern is not predictable across the set.

Tool Count2/5

50 tools is excessive for a server named 'Tmdb'. Only about 18 tools are actually TMDB-related; the remaining 32 belong to the Pipeworx ecosystem. This inflates the count and makes the server feel bloated and unfocused, far beyond a well-scoped TMDB server.

Completeness3/5

The TMDB portion is quite complete (search, discover, details, credits, recommendations, trending, genres, configuration). However, the server's overall scope is muddled—it tries to cover two disjoint domains, so no single domain feels fully fleshed out. There are also some missing TMDB features (e.g., upcoming/now playing) that would require extra discovery. The Pipeworx tools cover data broadly but overlap in coverage.

Resources