| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 11 decomposed |
| Times Matched | 0 | 0 |
Reads and traverses the complete hierarchical structure of Figma design files via the Figma REST API, exposing the document tree with metadata about pages, frames, components, and layers. Implements recursive tree walking to expose nested layer relationships and properties, enabling programmatic access to design file organization without manual UI navigation.
Unique: Exposes Figma's document tree as a queryable MCP resource, allowing Claude and other LLM clients to directly inspect design file structure without requiring developers to write custom API wrappers or parse raw Figma JSON responses
vs alternatives: Simpler than building custom Figma API clients because it abstracts authentication, pagination, and tree traversal into standard MCP tool calls that work with any MCP-compatible client
Extracts detailed metadata about Figma components including component sets, variants, properties, and documentation. Queries the Figma API to surface component definitions, variant configurations, and property schemas, enabling programmatic discovery of design system components and their configuration options without manual inspection.
Unique: Surfaces Figma component variant schemas and property definitions as structured data, allowing LLM-based agents to reason about component capabilities and generate accurate code bindings without manual schema definition
vs alternatives: More complete than manual component audits because it programmatically discovers all variants and properties, reducing human error and enabling real-time sync with design system changes
Extracts design tokens (colors, typography, spacing, shadows) from Figma files and maps them to semantic names and values. Parses Figma's token plugin format or custom naming conventions to surface design tokens as structured data, enabling synchronization with code-based token systems (CSS variables, design token JSON, etc.).
Unique: Bridges Figma design tokens to code-based token systems by extracting semantic token definitions and mapping them to standard formats (CSS variables, JSON), enabling automated token synchronization without manual copy-paste
vs alternatives: More flexible than Figma Tokens plugin alone because it can extract tokens from custom naming conventions and export to multiple formats, supporting teams with existing token infrastructure
Queries properties of specific frames, layers, and design elements within a Figma file, including dimensions, positioning, fill colors, stroke properties, text content, and constraints. Implements filtered selection logic to retrieve properties for named layers or layers matching criteria, returning structured property objects without requiring manual layer navigation.
Unique: Provides direct property access to Figma layers via MCP tools, allowing LLM agents to query specific design properties without parsing raw API responses or manually navigating the Figma UI
vs alternatives: More efficient than exporting design specs manually because it enables programmatic property queries, supporting automation workflows that would otherwise require human inspection
Registers Figma API operations as MCP tools with JSON schema definitions, enabling Claude and other MCP clients to discover and invoke Figma operations through standardized function calling. Implements schema validation and parameter marshaling to translate MCP tool calls into Figma API requests, with error handling and response transformation.
Unique: Exposes Figma operations as MCP tools with full schema validation, enabling LLM clients to discover and invoke Figma queries through standard function calling without custom API wrappers or authentication handling
vs alternatives: More discoverable than raw Figma API because MCP schema definitions enable LLM clients to understand available operations and parameters, supporting autonomous agent workflows
Handles Figma API authentication via personal access tokens or OAuth, managing token lifecycle and API request signing. Implements token validation and error handling for authentication failures, enabling secure access to Figma resources without exposing credentials in client code.
Unique: Abstracts Figma API authentication into MCP server initialization, allowing clients to invoke Figma tools without managing credentials directly, improving security posture for multi-user deployments
vs alternatives: Simpler than building custom authentication layers because it handles token validation and request signing transparently, reducing security surface area
Supports querying multiple Figma files or resources in a single operation with pagination handling for large result sets. Implements batching logic to reduce API calls and pagination to handle Figma API limits, enabling efficient bulk operations without manual pagination handling.
Unique: Implements transparent pagination and batching for Figma API queries, allowing clients to request large datasets without manual pagination handling or rate limit management
vs alternatives: More efficient than sequential API calls because it batches requests and handles pagination automatically, reducing latency and API call overhead
Normalizes Figma API responses and errors into consistent formats, translating API-specific error codes into human-readable messages. Implements retry logic for transient failures and graceful degradation for partial failures, enabling robust automation workflows that handle API instability.
Unique: Provides automatic retry logic and error normalization for Figma API calls, enabling automation workflows to recover from transient failures without explicit error handling code
vs alternatives: More robust than raw API calls because it implements exponential backoff and error normalization, reducing automation failures due to temporary API issues
+2 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.
Both Figma MCP Server and Tavily MCP Server offer these capabilities:
Wraps Tavily API calls in try-catch blocks and normalizes error responses into MCP-compliant format, converting HTTP errors, API validation errors, and network failures into structured error messages returned to clients. The server catches axios errors, extracts error details from Tavily API responses, and returns them as MCP CallToolResult with error content type.
Tavily MCP Server scores higher at 62/100 vs Figma MCP Server at 60/100.
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