tmdb · MCP Server

TMDB MCP — Movie and TV Metadata, Queryable by AI

Improvado's MCP server connects The Movie Database to your AI agent. Query film metadata, ratings, cast data, genres, trending titles, and release schedules in plain English. Works with Claude, ChatGPT, Cursor, and any MCP-compatible tool.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Query Any Movie or TV Metadata Instantly

Ask your AI agent about genre trends, top-rated titles, cast connections, or upcoming releases — without writing API calls or browsing the TMDB interface. The MCP server handles all data retrieval.

Example prompts

"What are the top 20 highest-rated drama series released in the last two years? Include genre tags and vote count."

20 min → 20 sec

"Show me all films where the same director and lead actor have collaborated more than twice."

45 min → 1 min

"What genres have trended upward in average rating on TMDB over the last five years?"

2 hrs → 2 min

Works with Claude ChatGPT Cursor +5

Write

Write: Build and Curate Content Lists Programmatically

Create curated lists, add titles, update metadata tags, and manage watchlists programmatically through your AI agent. The MCP server translates natural language into TMDB API write operations.

Example prompts

"Create a list of the top 50 highest-rated sci-fi films from the last decade and add it to our content catalog."

1 hr → 5 min

"Add all films in our recommendations list that have a TMDB rating above 7.5 to the featured collection."

30 min → 1 min

"Tag all action films from our catalog released between 2015 and 2020 with the 'legacy-action' label."

45 min → 5 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Track Trending Titles and Rating Shifts

Set up your AI agent to watch TMDB data continuously. Get alerts when titles in your catalog enter trending lists, when ratings shift significantly, or when new releases match your content criteria.

Example prompts

"Alert me when any title in our catalog enters the TMDB trending list for the week."

Manual → auto

"Weekly: send a digest of new releases in action, drama, and sci-fi genres with ratings above 7.0."

3 hrs → auto

"Flag any title in our active recommendations where the TMDB rating drops more than 0.5 points in a week."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Content Catalog Enrichment Is Manual and Slow

The problem

Teams building content platforms or recommendation engines need rich metadata — genres, ratings, cast, keywords, similar titles — for every item in their catalog. Fetching this from TMDB, formatting it consistently, and loading it into the catalog is a multi-step manual pipeline that breaks whenever the catalog grows.

How MCP solves it

Ask the MCP server to enrich catalog entries on demand. The AI agent pulls metadata for any title or batch of titles, formats it consistently, and returns structured data ready for ingestion — without custom API scripts or manual enrichment steps.

Try asking

Fetch full TMDB metadata for all 200 titles in our drama catalog — rating, genres, cast, keywords, and similar titles.

Answer in seconds

All data sources, one query

Challenge 2

Genre and Trend Analysis Requires API Scripting

The problem

Understanding what content is trending, which genres are growing in ratings, or which directors are gaining popularity requires querying TMDB's API with custom code, transforming the results, and building visualizations. Most teams don't have bandwidth for this, so strategic content decisions are made without data.

How MCP solves it

Ask the MCP server analytical questions directly. The AI agent queries TMDB data, aggregates across date ranges and categories, and returns trend analysis in seconds — no code, no data pipelines, no waiting.

Try asking

Which genres saw the largest increase in average TMDB rating over the past three years? Break down by year.

Full detail preserved

No data loss on export

Challenge 3

Recommendation Engine Needs Fresh Data Constantly

The problem

Content recommendation systems rely on up-to-date metadata — new ratings, new releases, updated cast data. Keeping a local metadata store synchronized with TMDB requires a scheduled pipeline that someone has to maintain. When it breaks, recommendations degrade silently.

How MCP solves it

The MCP server gives the AI agent direct access to live TMDB data. Set up monitoring queries to detect new high-rated releases or significant metadata changes, and refresh catalog entries automatically when conditions are met.

Try asking

Show me titles released in the last 30 days with a TMDB rating above 7.5 that aren't in our catalog yet.

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

Does TMDB have an official MCP server?

TMDB does not publish an official MCP server. Improvado provides a hosted MCP server that connects TMDB to Claude, ChatGPT, Cursor, and other MCP-compatible AI tools — with pre-authenticated access, normalized metadata, and no local setup required.

What TMDB data is available through the MCP server?

Movie and TV show metadata, ratings, vote counts, genres, cast and crew, keywords, release dates, trending lists, similar titles, collections, and production company data. Improvado normalizes the full TMDB API v3 and v4 surface.

Which AI tools work with the TMDB MCP server?

Any tool supporting the Model Context Protocol: Claude Desktop, ChatGPT, Cursor, Windsurf, Gemini, and custom applications using the MCP HTTP transport. Claude is the most widely used due to its native MCP support — all through Improvado's hosted MCP server.

Can the TMDB MCP server be combined with streaming platform data?

Yes. Improvado connects data from multiple sources in one normalized model. Teams can combine TMDB metadata with streaming analytics, viewership data, or content platform metrics — queryable through the same MCP connection.

Is TMDB data secure through the MCP server?

Yes. TMDB API keys are stored in Improvado's encrypted vault (SOC 2 Type II certified). All queries run through Improvado's secure proxy — your API credentials are never passed to the AI tool.

How quickly can teams start querying TMDB with AI?

TMDB integration in Improvado typically completes in minutes. For Claude Desktop or Cursor, add one configuration line. Once connected, the AI agent can start answering questions about movie and TV data immediately.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms