| 0 |
| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Fetches current stock prices, bid-ask spreads, and market data from Yahoo Finance API through the Model Context Protocol, enabling LLM agents to query live market data without direct API integration. Implements MCP tool schema binding to expose Yahoo Finance endpoints as callable functions that Claude or other MCP-compatible models can invoke with ticker symbols as parameters.
Unique: Exposes Yahoo Finance as an MCP tool, allowing LLM agents to call financial data retrieval as a native function without requiring the model to handle HTTP requests or API authentication directly. Uses MCP's standardized tool schema to abstract away API complexity.
vs alternatives: Simpler integration than building custom REST API wrappers for each LLM framework — MCP standardization means one server works with Claude, any MCP-compatible model, and multiple agent frameworks without code duplication.
Queries Yahoo Finance historical price data (OHLCV: open, high, low, close, volume) for a given ticker and date range, returning time-series data suitable for technical analysis or backtesting. Implements date parameter validation and pagination to handle large datasets, exposing the data through MCP tool interface for agent-driven historical analysis workflows.
Unique: Integrates historical data retrieval as an MCP tool, allowing agents to autonomously fetch and analyze multi-year price histories without requiring manual data downloads or external data pipeline setup. Abstracts pagination and date validation logic within the MCP server.
vs alternatives: Faster agent iteration than manual CSV imports or direct API calls — agents can request historical data inline during reasoning, enabling dynamic analysis without context switching to external tools.
Retrieves company financial metrics (P/E ratio, market cap, dividend yield, earnings per share, debt-to-equity, etc.) from Yahoo Finance for fundamental analysis. Implements MCP tool binding to expose these metrics as queryable data, enabling agents to perform valuation comparisons and fundamental screening without manual data aggregation.
Unique: Exposes Yahoo Finance fundamentals as MCP tools, allowing agents to query financial metrics inline during analysis without requiring separate data sources or manual metric aggregation. Standardizes metric naming and format across the MCP interface.
vs alternatives: More accessible than building custom SEC filing parsers or maintaining multiple financial data subscriptions — agents get standardized fundamental data through a single MCP interface.
Enables agents to request data for multiple tickers in a single MCP tool invocation or through sequential calls with optimized batching. Implements parameter arrays or loop-friendly tool schemas to allow agents to analyze portfolios or compare multiple securities without individual API calls per ticker. Handles response aggregation and formatting for comparative analysis.
Unique: Implements batch-friendly MCP tool schemas that allow agents to request data for multiple tickers efficiently, reducing round-trip overhead compared to single-ticker tools. Aggregates responses in a format optimized for comparative analysis.
vs alternatives: Reduces agent latency for portfolio analysis compared to sequential single-ticker calls — agents can analyze 10-stock portfolios in one tool invocation instead of 10 separate API calls.
Implements the Model Context Protocol server specification, handling client connection negotiation, tool schema registration, and request routing. Manages the server process lifecycle (startup, shutdown, error handling) and exposes Yahoo Finance capabilities as standardized MCP tools with JSON schema definitions. Handles protocol versioning and capability advertisement to ensure compatibility with MCP clients.
Unique: Implements full MCP server lifecycle management, handling protocol negotiation, tool schema registration, and request routing according to the MCP specification. Abstracts away JSON-RPC complexity from tool developers.
vs alternatives: Standardized MCP implementation means the server works with any MCP-compatible client without custom adapters — Claude Desktop, custom runners, and future MCP tools all connect to the same server without modification.
Implements error handling for Yahoo Finance API failures (rate limits, timeouts, invalid tickers, network errors) with appropriate MCP error responses. Provides fallback strategies such as cached data, partial results, or clear error messages that agents can interpret and act upon. Distinguishes between transient errors (retry-able) and permanent failures (invalid ticker).
Unique: Implements MCP-compliant error responses that distinguish between transient API failures and permanent errors, allowing agents to make intelligent retry decisions. Provides structured error information that agents can parse and act upon.
vs alternatives: Better agent resilience than naive API wrappers that crash on errors — agents receive clear error codes and can implement retry logic or fallback strategies without manual error parsing.
Fetches dividend history, upcoming dividend dates, and earnings information from Yahoo Finance for a given ticker. Returns structured data including dividend per share, ex-dividend dates, earnings dates, and EPS estimates. Enables fundamental analysis and income-focused portfolio evaluation through Claude's reasoning capabilities.
Unique: Exposes dividend and earnings data as MCP tools, enabling Claude to incorporate fundamental factors into investment analysis without requiring separate financial data sources. Structures data to support both historical analysis and forward-looking decision-making.
vs alternatives: More comprehensive than price-only analysis because it includes income and earnings factors; more accessible than building custom fundamental analysis tools because data is pre-fetched and structured.
Retrieves information about stock splits, reverse splits, mergers, and other corporate actions from Yahoo Finance. Enables accurate historical price analysis by accounting for adjustments and helps Claude understand why historical prices may appear discontinuous. Integrates with historical price retrieval to provide context for price anomalies.
Unique: Provides corporate action history as a dedicated MCP tool, enabling Claude to automatically detect and explain price discontinuities in historical data. Integrates with historical price retrieval to provide context for analysis.
vs alternatives: More transparent than pre-adjusted historical prices because it shows the actual corporate actions; more useful than ignoring splits because Claude can explain historical anomalies to users.
Executes web searches via the Tavily API and returns structured results with relevance scoring, source attribution, and clean text extraction optimized for LLM consumption. The MCP server marshals search queries through an axios HTTP client configured with the Tavily API key, parses JSON responses containing ranked results with URLs and snippets, and formats output for direct consumption by language models without additional preprocessing.
Unique: Tavily's search results are specifically optimized for LLM consumption with relevance scoring and clean formatting, rather than generic web search results. The MCP server wraps this via StdioServerTransport, enabling seamless integration into Claude Desktop and other MCP clients without custom HTTP handling.
vs alternatives: Returns LLM-ready formatted results with relevance scores out-of-the-box, whereas generic search APIs (Google, Bing) require additional parsing and ranking logic to be LLM-friendly.
Extracts clean, structured content from specified URLs using the Tavily extract endpoint, handling HTML parsing, boilerplate removal, and content normalization automatically. The server sends URLs to Tavily's extraction service via axios, receives parsed markdown or structured text, and returns content ready for LLM ingestion without requiring the client to manage web scraping libraries or HTML parsing.
Unique: Tavily's extraction service is optimized for LLM-ready output (markdown formatting, boilerplate removal, semantic structure preservation) rather than generic web scraping. The MCP server exposes this as a tool that agents can call directly without managing external scraping libraries.
vs alternatives: Handles boilerplate removal and content normalization automatically, whereas Puppeteer or Cheerio require custom logic to identify main content and remove navigation/ads.
Tavily MCP Server scores higher at 62/100 vs yahoo-finance-mcp at 25/100.
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Provides pre-built configuration templates and integration guides for popular MCP clients (Claude Desktop, Cursor, VS Code, Cline), including JSON configuration snippets for claude_desktop_config.json, cursor settings, VS Code extensions, and Cline agent configuration. Each integration template specifies the MCP server command, environment variables, and client-specific setup steps.
Unique: Official Tavily MCP provides pre-built integration templates for major MCP clients (Claude Desktop, Cursor, VS Code, Cline), reducing setup friction. Each template includes specific configuration syntax and environment variable requirements for that client.
vs alternatives: Pre-built templates eliminate guesswork in client configuration, whereas generic MCP documentation requires users to adapt examples for Tavily-specific setup.
Crawls websites starting from a seed URL and recursively follows internal links up to a specified depth, extracting content from each page and returning a structured collection of crawled pages. The server manages crawl state through Tavily's crawl endpoint, controlling recursion depth and link-following behavior, and returns all discovered pages with their extracted content and metadata for bulk analysis or knowledge base construction.
Unique: Tavily's crawl service is designed for LLM-friendly bulk extraction with automatic content normalization across multiple pages, rather than generic web crawlers that return raw HTML. The MCP server exposes depth control and link-following as tool parameters, enabling agents to autonomously decide crawl scope.
vs alternatives: Handles content extraction and normalization across all crawled pages automatically, whereas Scrapy or Selenium require custom pipelines to extract and normalize content from each page individually.
Analyzes a website's structure and generates a semantic map of URLs organized by topic or content type, enabling agents to understand site organization without manual exploration. The tavily_map tool sends a seed URL to Tavily's mapping service, which crawls the site, clusters pages by semantic similarity, and returns a hierarchical structure of discovered URLs grouped by inferred topic or purpose.
Unique: Tavily's map tool uses semantic clustering to organize URLs by inferred topic rather than just crawling and returning a flat list. This enables agents to navigate large sites intelligently without exhaustive crawling.
vs alternatives: Provides semantic site structure discovery out-of-the-box, whereas generic crawlers return unorganized URL lists requiring post-processing to identify topic-relevant pages.
Orchestrates multi-step research workflows where an agent autonomously decides which search, extraction, and crawling steps to perform based on intermediate results. The tavily_research tool wraps the other four tools and manages state across multiple API calls, allowing agents to refine queries, follow promising leads, and synthesize findings without explicit step-by-step instruction from the user.
Unique: The research tool enables agents to autonomously orchestrate search, extraction, and crawling steps based on intermediate findings, rather than requiring explicit tool calls for each step. This leverages the agent's reasoning to decide research strategy dynamically.
vs alternatives: Enables autonomous research workflows where agents decide next steps based on findings, whereas manual tool-calling requires explicit user or system prompts to specify each search or extraction step.
Implements the Model Context Protocol (MCP) server specification using TypeScript and StdioServerTransport, enabling the Tavily tools to be exposed as MCP tools callable by any MCP-compatible client. The server registers tool handlers via setRequestHandler(ListToolsRequestSchema, ...) and CallToolRequestSchema, marshaling tool calls from clients through to Tavily API endpoints and returning results in MCP-compliant format.
Unique: Official Tavily MCP server implementation using StdioServerTransport for direct process communication, enabling zero-configuration integration into Claude Desktop and other MCP clients. Supports both remote (hosted) and local deployment models.
vs alternatives: Official MCP implementation ensures compatibility and feature parity with Tavily API, whereas third-party MCP wrappers may lag behind API updates or lack full feature support.
Supports both remote deployment (hosted at https://mcp.tavily.com/mcp/) and local self-hosted deployment (via NPX, Docker, or Git), with different authentication models for each. Remote deployment uses URL parameters or Bearer token headers for API key passing, while local deployment uses TAVILY_API_KEY environment variable. Both expose identical tool capabilities through the same MCP interface.
Unique: Official Tavily MCP provides both remote (zero-setup) and local (self-hosted) deployment options with identical tool capabilities, enabling users to choose based on security, latency, and infrastructure requirements. Remote uses OAuth and Bearer tokens; local uses environment variables.
vs alternatives: Dual deployment model provides flexibility that single-deployment solutions lack; users can start with remote for quick testing and migrate to local for production without code changes.
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