| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 12 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
Crawls 11+ heterogeneous platforms (Zhihu, Weibo, Bilibili, Twitter, Reddit, HackerNews, etc.) and RSS feeds using platform-specific scrapers, normalizes disparate data schemas into a unified NewsItem model, and deduplicates content across sources using fuzzy title matching and URL canonicalization. The system maintains platform-specific metadata (rank, heat value, engagement metrics) while presenting a single normalized feed, enabling cross-platform trend detection that would be invisible within individual platform silos.
Unique: Implements platform-specific crawler modules with unified NewsItem schema and fuzzy deduplication across 11+ heterogeneous sources (Chinese + international), rather than relying on single-platform APIs or generic RSS parsing. Maintains platform-specific metadata (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) for weighted hotspot scoring.
vs alternatives: Covers more platforms (especially Chinese social media) with deeper metadata extraction than generic RSS aggregators, and provides unified deduplication across sources unlike single-platform monitoring tools.
Implements a multi-stage filtering pipeline that matches news items against user-defined keywords using regex patterns, required word lists, and excluded word lists. The system applies frequency-based scoring (keyword occurrence count) combined with platform hotspot weights to rank filtered results. Configuration is stored in frequency_words.txt with support for regex patterns, AND/OR/NOT boolean operators, and per-keyword weighting. Filtering occurs at collection time (reducing storage) and again at report generation time (enabling dynamic reconfiguration without re-crawling).
Unique: Combines regex pattern matching with frequency-based scoring and platform hotspot weighting (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) in a two-stage pipeline (collection-time and report-time filtering). Supports dynamic reconfiguration without re-crawling by applying filters at report generation.
TrendRadar scores higher at 58/100 vs fastapi_mcp at 37/100.
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vs alternatives: More flexible than simple keyword matching (supports regex and boolean logic) and more efficient than semantic filtering (no LLM overhead), making it suitable for real-time filtering at scale.
Detects newly emerged topics by comparing current crawl results against historical data stored in the system. Topics are marked as 🆕 (new) if they appear for the first time in the current crawl or if their hotspot rank increased significantly compared to previous crawls. The system tracks topic emergence velocity (how quickly a topic rises in rankings) and flags topics with unusual acceleration. New topic detection is performed at report generation time, enabling dynamic detection without re-crawling. The system maintains a historical hotspot index for comparison.
Unique: Detects new topics by comparing current hotspot rankings against historical data, marking topics with significant rank increases as 🆕. Tracks emergence velocity to distinguish breaking news from sustained trends.
vs alternatives: More efficient than semantic similarity detection (no LLM overhead) and more accurate than simple first-appearance detection (accounts for re-emerging topics), but requires historical baseline data.
Provides a web-based UI for editing TrendRadar configuration files (config.yaml, frequency_words.txt, timeline.yaml) with real-time validation and preview. The editor supports: (1) syntax highlighting for YAML and regex, (2) validation of keyword patterns (regex compilation check), (3) preview of filtered results based on current keyword configuration, (4) drag-and-drop channel configuration, (5) schedule preview (shows next 10 execution times). Changes are validated before saving, preventing configuration errors. The editor is optional; users can edit config files directly.
Unique: Provides web-based configuration editor with real-time validation, regex preview, and schedule visualization. Enables non-technical users to configure TrendRadar without editing YAML files.
vs alternatives: More user-friendly than manual YAML editing and provides validation feedback, but adds operational complexity compared to file-based configuration.
Integrates LiteLLM to provide vendor-agnostic AI analysis and summarization of filtered news items. Users configure their preferred LLM provider (OpenAI, Anthropic, Ollama, local models, etc.) once in config.yaml, and the system automatically routes analysis requests to that provider. The AI analysis capability includes: (1) automated summarization of long articles into key points, (2) sentiment analysis (positive/negative/neutral), (3) trend prediction based on historical patterns, and (4) custom analysis prompts. Analysis results are cached to avoid redundant API calls and can be pushed directly to notification channels.
Unique: Uses LiteLLM abstraction layer to support any LLM provider (OpenAI, Anthropic, Ollama, local models) with single configuration, enabling provider switching without code changes. Caches analysis results to reduce redundant API calls and costs.
vs alternatives: More flexible than hardcoded OpenAI integration (supports any LiteLLM provider) and cheaper than dedicated sentiment analysis APIs (can use local models), but slower than rule-based sentiment analysis.
Leverages LiteLLM to translate news content from source languages (primarily Chinese) to target languages (English, etc.) on-demand. The system detects source language automatically (via langdetect or similar), caches translations to avoid re-translating identical content, and batches translation requests to reduce API calls. Translations are stored alongside original content, enabling bilingual reports and multi-language notification delivery. Translation can be triggered at collection time (all news) or report time (only filtered news).
Unique: Implements provider-agnostic translation via LiteLLM with automatic language detection, content-based caching, and batch request optimization. Stores translations alongside originals for bilingual report generation.
vs alternatives: More flexible than dedicated translation APIs (supports any LiteLLM provider) and cheaper than commercial translation services when using local models, but slower than specialized translation APIs.
Implements a notification abstraction layer supporting 9+ delivery channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.). Each channel has a provider-specific formatter that converts normalized news items into channel-appropriate messages (e.g., WeChat card format, Telegram markdown, email HTML). The system batches notifications atomically—all news items for a report are sent as a single batch to each channel, ensuring consistency and reducing API calls. Message formatting respects channel constraints (character limits, attachment limits, etc.) and supports templating for customization.
Unique: Implements atomic message batching across 9+ heterogeneous channels with provider-specific formatters and constraint-aware truncation. Single configuration enables simultaneous delivery to WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc. without code changes.
vs alternatives: Supports more channels (especially Chinese platforms like WeWork, Feishu) than generic notification services, and batching reduces API calls and spam compared to per-item notifications.
Exposes TrendRadar's data and analysis capabilities as an MCP server, enabling AI agents and LLM applications to query trends, perform analysis, and generate insights through natural language. The MCP server implements tools for: (1) querying filtered news by keyword/date/platform, (2) retrieving trend statistics and hotspot rankings, (3) running custom analysis on news subsets, (4) generating reports in various formats. Clients (Claude, other LLM agents) can invoke these tools via MCP protocol, enabling conversational exploration of trends without direct database access. The server maintains state across multiple requests, allowing multi-turn conversations about trends.
Unique: Implements full MCP server exposing trend data and analysis tools to LLM agents, enabling conversational queries and multi-turn analysis workflows. Maintains state across requests and supports complex tool invocations (filtering, analysis, report generation).
vs alternatives: Enables conversational access to trends (vs. API-only access) and integrates with LLM agent workflows (vs. standalone tools), but adds operational complexity compared to simple REST APIs.
+4 more capabilities