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TMDB MCP Server

by ShubhanshuSondhiya
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TypeScript
Remote

TMDB MCP 서버

이 프로젝트는 영화 데이터베이스(TMDB) API와 통합되는 모델 컨텍스트 프로토콜(MCP) 서버를 구현합니다. 이를 통해 클로드와 같은 AI 비서가 영화 데이터와 상호 작용하여 영화 관련 콘텐츠를 검색하고, 세부 정보를 가져오고, 생성하는 기능을 제공합니다.

특징

자원

  • 정적 리소스 :

    • tmdb://info - TMDB API에 대한 정보

    • tmdb://trending - 현재 인기 있는 영화

  • 리소스 템플릿 :

    • tmdb://movie/{id} - 특정 영화에 대한 자세한 정보

프롬프트

  • 영화 리뷰 : 지정된 스타일과 등급으로 맞춤형 영화 리뷰를 생성합니다.

  • 영화 추천 : 장르와 분위기에 따라 개인화된 영화 추천을 받으세요

도구

  • 영화 검색 : 제목이나 키워드로 영화 찾기

  • 인기 영화 보기 : 하루 또는 주간 인기 영화 검색

  • 비슷한 영화 찾기 : 특정 영화와 비슷한 영화 찾기

Related MCP server: TMDB MCP Server

설치 지침

필수 조건

  • Node.js(v16 이상)

  • npm 또는 yarn

  • TMDB API 키

설치

  1. 이 저장소를 복제하세요

    지엑스피1

  2. 종속성 설치

    npm install

  3. TMDB API 키 구성

    • 프로젝트 루트에 .env 파일을 만듭니다(대안: src/config.ts 직접 편집합니다)

    • TMDB API 키를 추가하세요: TMDB_API_KEY=your_api_key_here

  4. 프로젝트를 빌드하세요

    npm run build

  5. 서버를 시작합니다

    npm start

Claude Desktop 설정

  1. 클로드 데스크톱 열기

  2. 설정 > 개발자 탭으로 이동하세요

  3. "구성 편집"을 클릭하여 구성 파일을 엽니다.

  4. 구성에 다음을 추가하세요.

{
  "mcpServers": {
    "tmdb-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/your/tmdb-mcp/build/index.js"]
    }
  }
}

  1. Claude Desktop을 다시 시작하세요

사용 예

정적 리소스 사용

  • "TMDB란 무엇인가요?"

  • "현재 인기 있는 영화 보여줘"

리소스 템플릿 사용

  • "ID 550 영화에 대한 자세한 정보를 얻으세요"(파이트 클럽)

  • "ID 155가 나오는 영화에 대해 말해줘"(다크 나이트)

프롬프트 사용

  • "Inception에 대한 자세한 리뷰를 9/10점으로 작성하세요"

  • "생각이 깊어지는 SF 영화를 추천해 주세요"

도구 사용

  • "우주 탐사에 관한 영화를 검색하세요"

  • "요즘 인기 영화는 뭐야?"

  • "매트릭스와 비슷한 영화 찾기"

개발

프로젝트 구조

tmdb-mcp/
├── src/
│   ├── index.ts                # Main server file
│   ├── config.ts               # Configuration and API keys
│   ├── handlers.ts             # Request handlers
│   ├── resources.ts            # Static resources
│   ├── resource-templates.ts   # Dynamic resource templates
│   ├── prompts.ts              # Prompt definitions
│   ├── tools.ts                # Tool implementations
│   └── tmdb-api.ts             # TMDB API wrapper
├── package.json
├── tsconfig.json
└── README.md

테스트

개발 중에 MCP Inspector를 사용하여 서버를 테스트하세요.

npx @modelcontextprotocol/inspector node build/index.js

특허

MIT

감사의 말

Available Tools

4 tools
get-movie-detailsC

Get detailed information about a specific movie

ParametersJSON Schema
NameRequiredDescriptionDefault
movieIdYesID of the movie to get details for

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves details but doesn't mention whether it's a read-only operation, potential rate limits, error conditions, or what 'detailed information' entails. This is a significant gap for a tool with zero annotation coverage.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with every part contributing essential information.

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

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' includes, potential response formats, or behavioral traits like safety or performance. For a tool with no structured context, this leaves critical gaps for an agent.

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?

The input schema has 100% description coverage, clearly documenting the 'movieId' parameter. The description adds no additional parameter semantics beyond implying the tool requires a specific movie, which is already covered by the schema. This meets the baseline of 3 when the schema does the heavy lifting.

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 verb ('Get') and resource ('detailed information about a specific movie'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get-similar' or 'search-movies', which might also retrieve movie information in different contexts.

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 provides no guidance on when to use this tool versus alternatives like 'search-movies' or 'get-similar'. It lacks context about prerequisites (e.g., needing a movie ID) or exclusions, leaving the agent to infer usage from the name alone.

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

get-similarC

Get similar movies to a given movie

ParametersJSON Schema
NameRequiredDescriptionDefault
movieIdYesID of the movie to find similar movies for

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It doesn't describe response format, error handling, rate limits, or other behavioral traits beyond the basic operation, which is insufficient for a tool with no annotation coverage.

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, efficient sentence with zero waste, making it appropriately sized and front-loaded. Every word contributes directly to the tool's purpose without unnecessary elaboration.

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

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete for a tool that likely returns a list of movies. It doesn't explain what 'similar' means (e.g., genre, director, ratings), the return format, or any limitations, leaving significant gaps in understanding.

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?

The schema description coverage is 100%, so the schema already documents the 'movieId' parameter fully. The description adds no additional meaning beyond what the schema provides, such as examples or constraints, resulting in the baseline score for high schema coverage.

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's purpose with a specific verb ('Get') and resource ('similar movies'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'search-movies' or 'get-trending' beyond the basic concept of similarity, which prevents a perfect score.

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 provides no guidance on when to use this tool versus alternatives like 'search-movies' or 'get-trending'. It lacks explicit context, exclusions, or prerequisites, leaving the agent to infer usage based on the tool name alone.

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

search-moviesC

Search for movies by title or keywords

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query
pageNoPage number for results

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches for movies but doesn't mention key behaviors like whether it's read-only (likely, but not confirmed), how results are returned (e.g., format, pagination details beyond the 'page' parameter), or any limitations (e.g., rate limits, authentication needs). This leaves significant gaps for a tool with no annotation coverage.

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, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core purpose and avoids redundancy, making it highly concise and well-structured for quick understanding.

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

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (search functionality with pagination), no annotations, and no output schema, the description is insufficient. It lacks details on behavioral traits (e.g., read-only status, result format), usage context relative to siblings, and output expectations, leaving the agent with incomplete guidance for proper 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 100%, with both parameters ('query' and 'page') documented in the schema. The description adds minimal value beyond the schema by implying the 'query' parameter is for title/keyword searches, but it doesn't provide additional context like search syntax, result ordering, or default behaviors. This meets the baseline for high schema coverage.

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's purpose with a specific verb ('Search') and resource ('movies'), and specifies the search criteria ('by title or keywords'). However, it doesn't explicitly differentiate from sibling tools like 'get-trending' or 'get-similar', which might also involve movie discovery but through different mechanisms.

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 provides no guidance on when to use this tool versus alternatives like 'get-trending' (for popular movies) or 'get-similar' (for recommendations based on a known movie). It mentions the search criteria but doesn't clarify scenarios where this is preferred over sibling tools, leaving usage context implied rather than explicit.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 4 tool updates
    • First observedget-movie-details
    • First observedget-similar
    • First observedget-trending
    • First observedsearch-movies
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.3/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: get-movie-details retrieves specific movie information, get-similar finds related movies, get-trending lists popular movies, and search-movies performs keyword-based queries. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency5/5

All tools follow a consistent verb-noun pattern using kebab-case (e.g., get-movie-details, get-similar, get-trending, search-movies). The naming is uniform and predictable, with 'get' for retrieval operations and 'search' for querying, enhancing readability and usability.

Tool Count4/5

With 4 tools, the server is well-scoped for basic movie discovery and information retrieval. While it covers core functions like details, similarity, trends, and search, it might feel slightly thin for a full movie database API, but it is reasonable and focused.

Completeness3/5

The tool set covers key read operations for movie data, including details, similarity, trends, and search. However, there are notable gaps such as no update, delete, or creation tools (if applicable to the domain), and missing operations for TV shows, actors, or reviews, which could limit agent workflows in broader contexts.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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