| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 13 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Automatically introspects a FastAPI application's OpenAPI schema and converts endpoint definitions into MCP tool schemas without information loss. Uses the convert_openapi_to_mcp_tools() function to parse OpenAPI 3.0 specifications, extracting parameter definitions, request/response schemas, and documentation, then maps them to MCP tool format with preserved validation rules and type information. This enables LLMs to understand and invoke FastAPI endpoints as native tools.
Unique: Uses native FastAPI OpenAPI schema generation rather than generic OpenAPI-to-MCP converters, preserving Pydantic validators, dependency injection metadata, and custom documentation without separate parsing logic. Integrates directly with FastAPI's built-in schema generation pipeline.
vs alternatives: Preserves full type information and validation rules from Pydantic models during conversion, whereas generic OpenAPI converters often lose semantic information about constraints and custom validators.
Translates MCP tool calls directly to FastAPI endpoint invocations using ASGI transport, bypassing HTTP overhead by communicating directly with the FastAPI application instance. The Tool Execution layer (fastapi_mcp/execute.py) reconstructs HTTP requests from MCP tool parameters, invokes the FastAPI ASGI app directly, and streams responses back without serialization/deserialization cycles. This approach preserves middleware execution, dependency injection, and authentication context.
Unique: Implements zero-copy ASGI transport that invokes FastAPI endpoints directly without HTTP serialization, preserving the full FastAPI execution context including middleware, dependency injection, and request lifecycle. Most MCP-to-REST bridges use HTTP clients, adding serialization overhead.
vs alternatives: Eliminates HTTP serialization/deserialization overhead and enables middleware execution that HTTP-based tool execution cannot achieve, resulting in ~50-200ms latency reduction per tool call compared to HTTP-based MCP servers.
Propagates HTTP error responses and status codes from FastAPI endpoints back to MCP clients, preserving error semantics and enabling LLMs to understand and handle failures appropriately. When a FastAPI endpoint returns an error status code (4xx, 5xx), the MCP server translates this into an MCP error response with the original status code and error message. This enables LLMs to distinguish between different error types (validation errors, authentication failures, server errors) and respond accordingly.
Unique: Preserves HTTP error semantics by propagating status codes and error messages from FastAPI to MCP clients, enabling LLMs to understand failure reasons. Most MCP servers treat all errors uniformly without distinguishing error types.
vs alternatives: Enables LLMs to distinguish between validation errors (4xx) and server errors (5xx) and respond appropriately, whereas generic MCP servers often treat all failures as generic tool execution errors.
Manages the complete MCP server lifecycle including initialization, transport mounting, and shutdown. The FastApiMCP class orchestrates server startup, mounts the selected transport (HTTP or SSE), and handles graceful shutdown. The server can be mounted on a FastAPI application (same-app deployment) or run as a standalone process (separate-app deployment). Lifecycle management includes resource cleanup, session termination, and proper transport shutdown.
Unique: Provides explicit lifecycle management for MCP servers including initialization, transport mounting, and graceful shutdown. Supports both same-app (mounted on FastAPI) and separate-app (standalone) deployment patterns.
vs alternatives: Integrates MCP server lifecycle with FastAPI application lifecycle, enabling seamless deployment patterns that alternatives typically require separate orchestration for.
Preserves FastAPI's dependency injection system and middleware execution when invoking endpoints through MCP tools. The ASGI-based tool execution layer reconstructs the full FastAPI request context, enabling dependencies (database connections, authentication, logging) and middleware (CORS, compression, custom handlers) to execute normally. This ensures that MCP-invoked endpoints behave identically to HTTP-invoked endpoints, with all side effects and validations intact.
Unique: Reconstructs the full FastAPI request context including dependency injection and middleware execution by using ASGI transport, enabling MCP-invoked endpoints to behave identically to HTTP-invoked endpoints. Most MCP-to-REST bridges bypass middleware and dependencies.
vs alternatives: Preserves FastAPI's full execution context including dependencies and middleware, whereas HTTP-based MCP servers cannot access or execute FastAPI-specific features.
Manages persistent HTTP client sessions across multiple MCP tool calls using the FastApiHttpSessionManager class, enabling stateful interactions with FastAPI endpoints. Maintains session state (cookies, headers, authentication tokens) across tool invocations, allowing LLMs to authenticate once and execute multiple authenticated requests without re-authentication. Sessions are keyed by client identifier and support concurrent multi-turn conversations.
Unique: Implements session persistence at the MCP layer rather than relying on HTTP client libraries, enabling fine-grained control over session lifecycle and multi-turn conversation state. Sessions are keyed by client identifier and support concurrent interactions.
vs alternatives: Provides explicit session management for MCP clients, whereas generic HTTP clients require manual cookie/header handling. Enables stateful multi-turn interactions that would otherwise require re-authentication per request.
Filters FastAPI endpoints before converting them to MCP tools using configurable inclusion/exclusion patterns, path prefixes, and tag-based filtering. Allows developers to selectively expose only specific endpoints as MCP tools while keeping internal or sensitive endpoints hidden. Filtering is applied during schema conversion, preventing unwanted endpoints from appearing in the MCP tool registry.
Unique: Provides declarative endpoint filtering at the MCP layer using path patterns and tags, enabling selective tool exposure without modifying the underlying FastAPI application. Filtering is applied during schema conversion, not at runtime.
vs alternatives: Allows selective endpoint exposure without modifying FastAPI code or creating separate application instances, whereas alternatives typically require separate API gateways or endpoint duplication.
Forwards authentication credentials from MCP clients to FastAPI endpoints using configurable authentication strategies including OAuth 2.1, JWT tokens, API keys, and custom authentication handlers. The AuthConfig class encapsulates authentication metadata, and the HTTPRequestInfo type carries request context (headers, cookies) through the tool execution pipeline. Supports both bearer token forwarding and header-based authentication, preserving the original FastAPI authentication requirements.
Unique: Implements authentication forwarding at the MCP layer by carrying HTTPRequestInfo (headers, cookies) through the tool execution pipeline, enabling transparent credential forwarding without modifying FastAPI authentication logic. Supports multiple authentication strategies (OAuth 2.1, JWT, API keys) through pluggable AuthConfig.
vs alternatives: Preserves existing FastAPI authentication without duplication, whereas generic MCP-to-REST bridges often require separate authentication configuration or token management.
+5 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
LlamaIndex scores higher at 40/100 vs fastapi_mcp at 37/100. However, fastapi_mcp offers a free tier which may be better for getting started.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities