Cohere MCP Server Pricing 2026

Knowledge graph-based persistent memory system. Store and retrieve contextual information.

Dynamic and reflective problem-solving through thought sequences.

Exa's official MCP server connects AI assistants to a search engine purpose-built for AI, using neural embeddings to match on meaning rather than keywords so agents get clean, ready-to-use content instead of a page of blue links to re-parse. The default tool set covers web_search_exa for quick topical lookups and web_search_advanced_exa for full control over domains, date ranges, and content filters, plus specialized tools for code_search (searching real-world code and GitHub), company_research (building company profiles, competitor lists, and financials), crawling/web_fetch (pulling clean content from a specific URL), people_search and linkedin_search (public professional-profile lookups), and deep_researcher_start/check for long-running multi-step research tasks backed by Exa's Research API. The server is hosted at https://mcp.exa.ai/mcp — no local process to run — and connects via one-line setup in Cursor, VS Code, Claude Code, Claude Desktop (available as a native Connector), Codex, OpenCode, Windsurf, and Antigravity, authenticated with an EXA_API_KEY from the Exa dashboard. Tool exposure is tunable per client via a ?tools= query parameter on the endpoint URL, letting teams ship narrow, purpose-built configurations (e.g. company-research-only or LinkedIn-only agents) instead of exposing the full surface, and Exa ships ready-made Claude Skills/agent definitions for common patterns like company research and people search with built-in query-variation and token-isolation guidance.

Search, Query and interact with data in your Milvus Vector Database.

Embeddings, vector search, document storage, and full-text search with the open-source AI application database.

OpenAI does not publish a dedicated, first-party "MCP server" for its own API — a `openai/mcp-server` repo does not exist. Instead, OpenAI's official open-source contribution to the MCP ecosystem is on the client side: openai/openai-agents-python (27,000+ stars), a lightweight framework for building multi-agent workflows with the OpenAI API that ships native support for connecting to MCP servers as a tool source, letting an OpenAI-model-powered agent call out to any MCP server (filesystem, GitHub, databases, etc.) the same way a Claude-based agent would. In other words, OpenAI's MCP investment is "consume MCP tools from an OpenAI agent," not "expose OpenAI itself as an MCP server." Teams that specifically want to call OpenAI's chat, embeddings, or image-generation endpoints as MCP tools from Claude, Cursor, or another MCP client instead rely on small community-built wrapper servers around the OpenAI SDK, authenticated with an `OPENAI_API_KEY`, exposing tools like generate_completion, generate_embedding, or generate_image. Typical use of the Agents SDK side: build a Python agent that uses GPT models for reasoning while pulling live context through an MCP filesystem or web-search server. Update this entry if OpenAI ships a genuine first-party MCP server for its own API in the future.