Coveo MCP Server
AI Agents Powered by
Your Own Enterprise Content
Bring relevance to agents on any platform with enterprise-grade search, retrieval, and generative answering.
Easily orchestrate and connect agents to all your enterprise content in real time with the Coveo MCP Server. Seamlessly retrieve the most relevant content, extract precise document passages, and respond to natural language questions with relevant, grounded answers. No bloated prompts or brittle integrations.
With the Coveo MCP Server, agents get direct access to the world’s most advanced relevance engine via tools to:
- Retrieve high-relevance results for any query
- Extract pinpoint-accurate passages from enterprise documents
- Answer natural language questions with accurate responses grounded in your own content
Agents can now access information indexed from any content source, securely and at scale, all while respecting enterprise-grade access controls and document-level permissions.
Answer customer questions using your official knowledge base, complete with citations that maintain trust.
Help shoppers find the best product by understanding natural language and retrieving precise information from product catalogs.
A secure chatbot for employees to query internal resources like HR policies or technical docs, respecting all user permissions.
An agent that can quickly summarize and synthesize information from a vast repository of documents and generate a report.
MCP FAQ
MCP is an open standard that facilitates seamless interaction between AI models and external tools, systems, and structured data sources. It enables AI to dynamically retrieve, process, and contextualize information—without embedding it directly—preserving security, control, and scalability in enterprise environments.
The agentic revolution will drive major impacts on enterprises, powering copilots, assistants, and custom generative interfaces. As agents move from labs to the real world, they all require the ability to access and search enterprise data. With so much information to retrieve and deliver, agents need a standard interface between large language models (LLMs) and complex enterprise systems like product catalogs or search indexes.
Model Context Protocol (MCP) provides agents a structured, standard way to access real-time information for smarter, more grounded responses.
A typical flow looks like this:
- An MCP client (like an IDE, chatbot/agentic process, or custom app) sends a request to the MCP server.
- The MCP server fetches relevant data (search results, document passages, answer to a question) from defined resources.
- The AI model (LLM) attached to the MCP client grounds and processes this context to respond to the request.
- The MCP client receives a contextualized, high-fidelity response, grounded in enterprise-approved and secured sources.
Hosts: Interfaces like dev tools, chatbot or agentic processes, and web apps that interact with MCP.
Clients: Gateways that manage communication between host apps and servers.
Servers: Secure endpoints that expose data, tools, or actions.
Resources: The structured content (e.g. docs, knowledge bases, databases, product catalogs).
Tools: Functional extensions (e.g. API calls, scripts) that AI can execute.
MCP helps organizations and technical teams work more efficiently by providing a plug-and-play RAG implementation that minimizes the need for extra coding and configuration. It’s also extensible and scalable, allowing seamless integration with any compatible AI model, development environment, or API.
