Exa MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| EXA_API_KEY | Yes | Your Exa API key from dashboard.exa.ai/api-keys |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| web_search_exaA | Search the web using Exa AI - performs real-time web searches and can scrape content from specific URLs. Supports configurable result counts and returns the content from the most relevant websites. |
| deep_search_exaB | Searches the web and return results in a natural language format. |
| company_research_exaC | Research companies using Exa AI - finds comprehensive information about businesses, organizations, and corporations. Provides insights into company operations, news, financial information, and industry analysis. |
| crawling_exaB | Extract and crawl content from specific URLs using Exa AI - retrieves full text content, metadata, and structured information from web pages. Ideal for extracting detailed content from known URLs. |
| linkedin_search_exaB | Search LinkedIn profiles and companies using Exa AI - finds professional profiles, company pages, and business-related content on LinkedIn. Useful for networking, recruitment, and business research. |
| deep_researcher_startA | Start a comprehensive AI-powered deep research task for complex queries. This tool initiates an intelligent agent that performs extensive web searches, crawls relevant pages, analyzes information, and synthesizes findings into a detailed research report. The agent thinks critically about the research topic and provides thorough, well-sourced answers. Use this for complex research questions that require in-depth analysis rather than simple searches. After starting a research task, IMMEDIATELY use deep_researcher_check with the returned task ID to monitor progress and retrieve results. |
| deep_researcher_checkA | Check the status and retrieve results of a deep research task. This tool monitors the progress of an AI agent that performs comprehensive web searches, analyzes multiple sources, and synthesizes findings into detailed research reports. The tool includes a built-in 5-second delay before checking to allow processing time. IMPORTANT: You must call this tool repeatedly (poll) until the status becomes 'completed' to get the final research results. When status is 'running', wait a few seconds and call this tool again with the same task ID. |
| get_code_context_exaA | Search and get relevant context for any programming task. Exa-code has the highest quality and freshest context for libraries, SDKs, and APIs. Use this tool for ANY question or task for related to programming. RULE: when the user's query contains exa-code or anything related to code, you MUST use this tool. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| web_search_help | Get help with web search using Exa |
| code_search_help | Get help finding code examples and documentation |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| tools_list | List of available Exa tools and their descriptions |
TDQS
Scored across 8 tools
The tools have overlapping purposes that could cause confusion. For example, web_search_exa and deep_search_exa both perform web searches, with the latter adding natural language formatting, while crawling_exa and web_search_exa both handle URL content extraction. However, descriptions help differentiate some specialized tools like get_code_context_exa and linkedin_search_exa.
Most tools follow a consistent snake_case pattern with a '_exa' suffix, such as company_research_exa and web_search_exa. The main deviation is deep_researcher_start and deep_researcher_check, which use a different naming style without the suffix, slightly breaking the pattern.
With 8 tools, the count is well-scoped for a server focused on web research and data extraction. Each tool serves a distinct function within this domain, such as company research, LinkedIn searches, and code context retrieval, making the set appropriately sized.
The toolset covers core research workflows, including starting and checking deep research tasks, general and specialized searches, and content extraction. A minor gap exists in lacking explicit update or delete operations for research tasks, but agents can work around this by managing task IDs and polling.