| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Converts conversational natural language requests into executable stock orders on Zerodha Kite by parsing intent (buy/sell), quantity, price, and order type from user messages and routing them through the Kite API. Uses MCP tool schema to map user utterances to structured order parameters with validation before submission.
Unique: Implements MCP tool schema mapping that translates free-form conversational trading intent directly into Kite REST API calls without intermediate form-filling, with TOTP-based session management baked into the server initialization
vs alternatives: Simpler than building custom Kite integrations because MCP handles protocol negotiation and tool discovery; more direct than web UI automation because it uses native API calls with session persistence
Manages Zerodha Kite login sessions automatically by intercepting TOTP (Time-based One-Time Password) requirements, generating codes from a stored secret, and maintaining authenticated session state across multiple API calls. Eliminates manual re-authentication by storing and refreshing access tokens server-side.
Unique: Embeds TOTP generation directly in the MCP server initialization rather than requiring manual OTP entry or external auth services, using time-synchronized HMAC-SHA1 to generate valid codes on-demand
vs alternatives: More secure than storing plaintext passwords; more convenient than manual OTP entry; avoids dependency on third-party OAuth providers that Zerodha doesn't natively support
Interprets conversational trading requests and maps them to specific trading actions (buy, sell, modify, cancel) with appropriate parameters. Uses LLM context to disambiguate intent, infer missing parameters from conversation history, and validate trading logic before execution.
Unique: Leverages LLM reasoning to parse conversational trading requests and map them to tool invocations, using multi-turn context to infer missing parameters and validate trading logic before execution
vs alternatives: More flexible than form-based trading UIs; enables natural conversation flow; allows LLM to ask clarifying questions if intent is ambiguous
Coordinates execution of multi-leg trading strategies (e.g., buy stock + set stop loss + set profit target) by sequencing dependent orders and handling partial failures. Executes primary order first, then conditionally executes secondary orders based on primary execution status.
Unique: Implements order sequencing logic that executes primary orders first, then conditionally executes dependent orders (stop loss, profit target) based on primary execution status, handling partial failures gracefully
vs alternatives: More reliable than manual order placement because it automates sequencing; more flexible than Kite's native GTT because it supports arbitrary order dependencies; enables complex strategies in conversational interface
Maintains conversation context across multiple trading requests, remembering previously mentioned symbols, quantities, and trading preferences. Enables anaphoric references (e.g., 'sell it' referring to previously mentioned stock) and reduces repetition in multi-turn trading conversations.
Unique: Leverages LLM's built-in context window to maintain trading session state across multiple turns, enabling anaphoric references and reducing parameter repetition without explicit state management
vs alternatives: Simpler than building custom context stores because it relies on LLM's native context; more natural than form-based UIs because users don't repeat parameters; enables fluid multi-turn trading conversations
Fetches current holdings, quantities, entry prices, and P&L metrics from Zerodha Kite API and structures them for natural language querying. Aggregates position data across segments (equity, derivatives) and calculates derived metrics like total portfolio value, unrealized gains, and position-level returns.
Unique: Wraps Kite's holdings endpoint in an MCP tool that normalizes response format and calculates derived P&L metrics on-server, making portfolio state directly queryable by LLM agents without additional client-side processing
vs alternatives: Faster than scraping Kite web UI; more structured than raw API responses because it aggregates and calculates metrics; enables LLM agents to make context-aware trading decisions based on current portfolio state
Fetches real-time stock quotes, OHLC data, and market depth information from Zerodha Kite for specified symbols. Queries the Kite quote API with symbol lookup and returns structured price, volume, and bid-ask spread data suitable for order decision-making.
Unique: Integrates Kite's quote API as an MCP tool that accepts natural language symbol references and returns normalized quote objects, enabling LLM agents to fetch live prices without parsing Kite's raw response format
vs alternatives: More reliable than web scraping; lower latency than polling external data providers; integrated with Kite's native data feed so prices align with order execution prices
Creates Good-Till-Triggered (GTT) conditional orders on Zerodha Kite that automatically execute when specified price conditions are met. Accepts natural language trigger conditions (e.g., 'buy if price drops to 2000') and converts them into GTT rule objects with order parameters.
Unique: Parses natural language conditional expressions ('if price < X then buy Y shares') and maps them to Kite's GTT API schema, which requires explicit trigger type, trigger value, and nested order parameters — abstracting away the schema complexity
vs alternatives: Simpler than building custom monitoring bots because Zerodha handles trigger evaluation server-side; more reliable than client-side price monitoring because it doesn't depend on continuous agent uptime
+5 more capabilities
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 kite-mcp at 31/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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.
+3 more capabilities