Tavily MCP Server ranks higher at 62/100 vs yfinance-mcp-server at 25/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | yfinance-mcp-server | Tavily MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 25/100 | 62/100 |
| Adoption | 0 | 1 |
| Quality |
| 0 |
| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Exposes yfinance's stock ticker data fetching through MCP server tools, allowing Claude and other MCP clients to query current and historical stock prices by ticker symbol. Implements MCP tool schema binding that translates natural language requests into yfinance API calls, handling ticker validation and price data serialization back to the client as structured JSON responses.
Unique: Bridges yfinance (Python financial data library) directly into MCP protocol as callable tools, eliminating the need for custom REST API wrappers or direct library imports in client code. Uses MCP's tool schema system to expose yfinance methods as first-class client capabilities.
vs alternatives: Simpler than building a custom REST API wrapper around yfinance; tighter integration with Claude and MCP ecosystem than calling yfinance directly from Python scripts
Retrieves multi-period historical OHLCV (open, high, low, close, volume) data for a given ticker and date range, aggregating yfinance responses into structured time-series format. Handles date range validation, period granularity selection (daily, weekly, monthly), and formats output as JSON arrays or CSV-compatible structures suitable for analysis or downstream processing.
Unique: Exposes yfinance's period-based data fetching (daily, weekly, monthly) as MCP tools with automatic date range validation and format conversion, allowing clients to request historical data without managing yfinance's pandas DataFrame output directly.
vs alternatives: More flexible than static data exports; allows dynamic date range queries within MCP conversations vs. pre-computed CSV files
Fetches company-level metadata and fundamental metrics (market cap, P/E ratio, dividend yield, sector, industry, 52-week high/low) from yfinance's Ticker object, exposing these as MCP tools. Implements lazy-loading of ticker info to minimize network requests, caching metadata within a single MCP session, and serializing complex objects (e.g., company info dictionaries) into JSON-safe formats.
Unique: Wraps yfinance's Ticker.info dictionary (which returns inconsistent, nested JSON) into a normalized MCP tool schema with optional field filtering, allowing clients to request specific fundamentals without handling yfinance's raw data structure.
vs alternatives: Simpler than parsing yfinance's raw info dict in client code; more complete than REST APIs that only expose price data
Retrieves historical dividend payments and earnings dates for a ticker using yfinance's dividends and earnings attributes, formatting them as time-indexed JSON arrays. Handles missing data gracefully (some tickers have no dividend history), validates date ranges, and provides both raw dividend amounts and calculated metrics like dividend yield and payout frequency.
Unique: Exposes yfinance's dividends and earnings Series objects as queryable MCP tools with automatic date filtering and yield calculation, avoiding the need for clients to manipulate pandas Series directly.
vs alternatives: More accessible than raw yfinance API for dividend queries; integrated into MCP workflow vs. separate dividend data source
Retrieves historical stock splits and corporate actions (reverse splits, mergers, spinoffs) from yfinance's splits attribute, providing adjusted share counts and split ratios. Implements date-indexed lookup allowing clients to understand historical share count changes and their impact on price comparisons across split events.
Unique: Surfaces yfinance's splits Series as a queryable MCP tool with automatic ratio calculation and date indexing, enabling agents to understand and adjust for historical corporate actions without manual data wrangling.
vs alternatives: More transparent than pre-adjusted price data; integrated into MCP workflow vs. requiring external corporate action databases
Enables MCP clients to request data for multiple tickers in a single logical operation by composing individual ticker tools into batch queries, handling parallel or sequential fetching depending on MCP client implementation. Implements error handling per ticker (one failure doesn't block others) and aggregates results into a unified response structure suitable for comparative analysis.
Unique: Leverages MCP's tool-calling protocol to enable batch queries without implementing a custom batch endpoint; clients compose multiple ticker tools into a single logical batch operation, with error handling per ticker.
vs alternatives: More flexible than a single batch endpoint; allows clients to mix and match tools (price, fundamentals, dividends) per ticker without predefined batch schemas
Implements the Model Context Protocol (MCP) server specification, automatically generating tool schemas for all yfinance capabilities and exposing them via MCP's tool-calling interface. Handles MCP request/response serialization, tool discovery, and parameter validation according to MCP spec, allowing any MCP-compatible client (Claude, custom agents) to discover and invoke yfinance tools without prior knowledge of their signatures.
Unique: Implements full MCP server specification with automatic tool schema generation from yfinance methods, enabling zero-configuration integration with MCP clients; uses MCP's standardized tool discovery and invocation protocol rather than custom REST or gRPC APIs.
vs alternatives: More standardized than custom REST wrappers; tighter integration with Claude and MCP ecosystem than direct yfinance imports
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 yfinance-mcp-server 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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