discover_movieDiscover TMDB movies using advanced filters passed as query params (genre, year, certification, vote average, sort order, etc.). Use genres_movie to get valid genre IDs first.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Current page number | |
| results | No | Discovered movies | |
| total_pages | No | Total pages | |
| total_results | No | Total results |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / additionalPropertiesRemoved value: -trueInput schema / examplesAdded value: +[
+ {
+ "sort_by": "popularity.desc",
+ "with_genres": "28"
+ },
+ {
+ "primary_release_year": 2023,
+ "vote_average.gte": 7
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "page": {
+ "description": "Current page number",
+ "type": "number"
+ },
+ "results": {
+ "description": "Discovered movies",
+ "items": {
+ "properties": {
+ "id": {
+ "type": "number"
+ },
+ "title": {
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "total_pages": {
+ "description": "Total pages",
+ "type": "number"
+ },
+ "total_results": {
+ "description": "Total results",
+ "type": "number"
+ }
+ },
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is read-only, idempotent, and non-destructive (readOnlyHint, idempotentHint, destructiveHint false). The description adds that it uses advanced query params but does not disclose additional behavioral traits beyond what annotations cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences, each adding essential information. The first sentence states purpose and identifies key filters; the second provides a critical prerequisite. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (not shown), the input schema is empty, forcing the description to compensate. While the description lists example parameters, it does not provide a complete list of valid fields, types, or constraints. The agent lacks full understanding of available query params, which is a significant gap for a discovery tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no defined properties (empty), yet the description mentions multiple filter parameters. With schema coverage at 100% (empty schema), the description provides examples (sort_by, with_genres, vote_average.gte, primary_release_year) but lacks formal parameter definitions. This orients the agent but is insufficient for precise parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool discovers TMDB movies using advanced filters, with specific examples of filter types (genre, year, certification, vote average, sort order). It distinguishes from sibling tools like search_movie by focusing on discovery with advanced query params.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly references genres_movie as a prerequisite to obtain valid genre IDs, guiding the agent on correct usage. However, it does not explicitly state when not to use this tool or compare it directly with alternatives like search_movie.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.