@acwink/movies-search-mcp
MCP ServerFreeSmart MCP tool to find and validate movie/tv-show resources with multiple sources support
Capabilities6 decomposed
multi-source movie and tv show search with aggregation
Medium confidenceSearches for movies and TV shows across multiple data sources (IMDb, TMDB, local databases, or custom crawlers) and aggregates results into a unified response format. The MCP server implements a source-agnostic query interface that routes search requests to configured providers, normalizes heterogeneous result schemas, and returns deduplicated matches ranked by relevance and data completeness.
Implements MCP tool protocol for seamless LLM integration with pluggable source adapters, allowing Claude and other MCP-compatible clients to search movies without custom API wrappers or context management
Provides MCP-native movie search vs. generic REST API wrappers, enabling direct LLM tool calling without intermediate orchestration layers
movie and tv show resource validation with source verification
Medium confidenceValidates that found movie/TV show resources exist and are accessible across configured sources by performing existence checks, verifying data consistency between sources, and flagging incomplete or conflicting metadata. The validator cross-references results against multiple providers to ensure the resource is real and returns confidence scores based on source agreement and data completeness.
Implements cross-source validation logic within MCP tool protocol, allowing LLMs to automatically verify search results without external validation services or post-processing steps
Validates movie data at search time vs. post-hoc validation, reducing downstream errors in recommendation or curation pipelines
configurable source adapter system for extensible search backends
Medium confidenceProvides a plugin architecture for adding new movie/TV data sources without modifying core search logic. Each source adapter implements a standard interface (query, parse, normalize) that translates source-specific APIs (IMDb scraping, TMDB REST, local database queries) into the unified result schema. Adapters are registered at server startup and dynamically selected based on availability or configuration.
Uses adapter pattern to decouple source-specific logic from search orchestration, enabling runtime source swapping and custom backend integration without core library changes
Extensible adapter system vs. hardcoded source support, allowing teams to integrate proprietary or custom movie databases without maintaining a fork
normalized result schema mapping across heterogeneous sources
Medium confidenceTransforms raw responses from different movie/TV sources (IMDb HTML, TMDB JSON, custom databases) into a unified, canonical schema with consistent field names, types, and formats. The mapping layer handles optional fields, type coercion, and null-safety, ensuring downstream consumers always receive predictable data structures regardless of source.
Implements schema mapping at the MCP tool boundary, ensuring LLMs always receive consistent data structures without needing to handle source-specific quirks
Normalizes data at search time vs. requiring clients to handle source-specific schemas, reducing downstream complexity in LLM prompts and agent logic
mcp tool protocol integration for llm-native movie search
Medium confidenceExposes movie search and validation capabilities as MCP tools that LLM clients (Claude, other MCP-compatible agents) can invoke directly through the Model Context Protocol. The server implements MCP tool definitions with JSON schemas for input validation, handles tool invocation requests, and returns results in MCP-compliant format, enabling seamless integration into LLM agent workflows without custom API clients.
Implements full MCP server lifecycle (tool definition, invocation handling, result serialization) for movie search, enabling drop-in integration with Claude and other MCP clients without custom wrappers
Native MCP tool vs. REST API wrapper, eliminating the need for LLM agents to manage HTTP clients or parse API responses
streaming and video resource metadata enrichment
Medium confidenceAugments movie/TV search results with streaming availability data (which platforms host the content, subscription requirements, rental/purchase options) and video metadata (runtime, quality, subtitles). The enrichment layer queries streaming availability APIs or local databases and merges results into the canonical schema, providing users with actionable information about where to watch.
Integrates streaming availability as a first-class enrichment step in the search pipeline, allowing LLMs to make watch-location recommendations without separate API calls
Includes streaming data in search results vs. requiring separate availability lookups, reducing latency and complexity for recommendation agents
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with @acwink/movies-search-mcp, ranked by overlap. Discovered automatically through the match graph.
search-movie
搜索电影和电视剧资源，快速找到最匹配的观看链接。验证链接可播放性，确保点开就能看。批量校验多个候选，节省筛选时间。
IMDb Explorer
Discover movies, TV shows, cast, crew, ratings, and box office from IMDb. Browse top charts, upcoming releases, and country-specific lists, with special coverage of Indian cinema. Filter by title, genre, year, language, and more to find exactly what you need.
XFind
Boost efficiency with AI-driven, multi-platform meta-search...
Streaming Options
AI-driven tool for personalized, cross-platform streaming...
Desearch
Decentralized AI search for real time X Twitter and Web...
local-deep-research
Local Deep Research achieves ~95% on SimpleQA benchmark (tested with Qwen 3.6). Supports local and cloud LLMs (Ollama, Google, Anthropic, ...). Searches 10+ sources - arXiv, PubMed, web, and your private documents. Everything Local & Encrypted.
Best For
- ✓LLM agents building movie recommendation systems
- ✓developers integrating movie/TV metadata into applications without maintaining their own database
- ✓teams building multi-source search abstractions with MCP
- ✓recommendation engines that need to filter out false positives
- ✓content curation systems requiring high-confidence metadata
- ✓LLM agents that must validate user queries against real movie databases
- ✓teams building custom movie metadata pipelines
- ✓developers maintaining multiple data sources and needing unified access
Known Limitations
- ⚠Search latency depends on slowest configured source — no timeout-based fallback documented
- ⚠Result deduplication relies on title/year matching which may fail for remakes or international titles with different names
- ⚠No built-in caching layer — repeated searches hit all sources every time
- ⚠Validation speed is O(n) in number of sources — adding sources increases latency linearly
- ⚠Cannot detect typos or misspellings in original query — only validates exact matches
- ⚠Relies on source API availability — if a source is down, confidence scores may be artificially low
Requirements
Input / Output
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Smart MCP tool to find and validate movie/tv-show resources with multiple sources support
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