AWS Qindex MCP Server

What is AWS Qindex MCP Server?

AWS Qindex MCP Server is a specialized server component that exposes the search and data retrieval capabilities of Amazon Q Index via the standardized Model Context Protocol (MCP). It enables AI agents, LLM-based coding assistants, and automation tools to integrate Q Index as a contextual data source, making it easy to perform semantic document searches, answer questions from indexed corpora, and leverage enterprise knowledge in generative AI workflows.

How to Configure

To set up the AWS Qindex MCP Server:

1. Prerequisites:
   • Ensure Python is installed (recommended using uv).
   • Have a configured AWS profile/credentials with permissions to access Q Index.
   • Install the MCP Server using uvx or as a Docker container as per your application's requirements.
2. Add to Your MCP Client Configuration: Add an entry to your MCP client's configuration file (e.g. ~/.aws/amazonq/mcp.json, .cursor/mcp.json, etc.):

   {
     "mcpServers": {
       "awslabs.qindex-mcp-server": {
         "command": "uvx",
         "args": ["awslabs.qindex-mcp-server@latest"],
         "env": {
           "AWS_PROFILE": "your-aws-profile",
           "AWS_REGION": "your-aws-region",
           "FASTMCP_LOG_LEVEL": "ERROR"
         }
       }
     }
   }
   

3. Environment Variables and Permissions:
   • Set values for AWS_PROFILE and AWS_REGION as needed.
   • Ensure your IAM user or role has access to Amazon Q Index APIs.
   • Optionally configure any specific Q Index identifiers or custom environment variables required by your workflow.
4. Test the connection: Use your MCP client’s interface to check if the server is discoverable and returns the available tools under /tools/list.

How to Use

1. Agentic Assistant Integration:
   • Interact through your AI coding assistant or chat interface (e.g. Amazon Q, Cursor, Windsurf, Claude Desktop) with natural language prompts that invoke search or retrieval from Q Index.
   • The LLM uses the MCP protocol to query the Qindex server for relevant documents or answers.
2. Direct Tool Invocation:
   • Listing available functions (tools) with tools/list via your MCP client.
   • Calling Q Index search operations with the tools/call endpoint, passing query parameters like keywords or document filters.
   • Example in chat: "Using the Qindex MCP Server, search for recent architecture guidelines on AWS Lambda."
3. Contextual Completions:
   • When enabled, the results from Q Index can be embedded into the language model’s context window to improve the accuracy and factual grounding of code, documentation, or research outputs.
4. Multi-agent Workflows:
   • Combine responses from Qindex MCP Server with other MCP servers (e.g. AWS Documentation or Cost Analysis) for sophisticated, cross-domain reasoning and automation.

Key Features

• Real-Time Semantic Search: Natural language or keyword-based queries over your enterprise's Q Indexes.
• LLM-Ready Structured Results: Returns data as structured context, ready to be injected into LLM prompts or agent workflows.
• Authorization Inheritance: Utilizes your existing AWS IAM for fine-grained, secure data access.
• Enterprise Knowledge Integration: Seamlessly brings organizational Q Index knowledge to AI-powered coding assistants and agents.
• Tool Discovery and Invocation: Exposes discoverable, composable functions (MCP tools) for searching, filtering, and retrieving indexed content.
• Citations and References: Results can include source/document metadata, enabling accurate tracing and auditing.
• Composable with Other MCP Servers: Flexible integration with complementary context/data providers for full-stack AI development.

Use Cases

• Enterprise Knowledge Search: Instantly answer business, engineering, or compliance questions by searching across curated Q Indexes in natural language.
• Research Assistant: Retrieve up-to-date architectural patterns, security guidelines, or best practices documented across the enterprise for context-aware code generation.
• Conversational Q&A: Power chatbots, virtual assistants, or support agents with the ability to ground responses in authoritative internal documents.
• Automated Code Generation: Supplement LLM code output with organization-specific references and best practices retrieved from Q Index.
• Compliance and Security Auditing: Quickly locate relevant policies and documentation in response to audit or compliance queries.
• Custom Workflows: Enable multi-tool chains, where information retrieved from Amazon Q Index guides or constrains the actions of other MCP-integrated tools.

FAQ

Q1: What permissions are needed to run the AWS Qindex MCP Server?
To access Amazon Q Index data, your AWS profile or credentials must have permissions for the relevant Q Index APIs (such as qindex:Search or similar fine-grained permissions). Consult your security team to ensure compliant access.

Q2: Can I use the Qindex MCP Server with multiple Q Indexes or tenants?
Yes, you can specify different Q Index identifiers or configure multiple entries in your MCP client config to route queries to different Q Index data sources as required.

Q3: How does the AWS Qindex MCP Server handle sensitive or restricted data?
Data access, filtering, and authorization are enforced using your AWS security context. Only users or roles with proper rights to the underlying Q Index and its documents will be able to retrieve matching results.

Q4: What formats or content types does Qindex MCP Server return?
Tool responses include structured JSON payloads with ID, snippet, title, document meta, and optionally full text, suitable for embedding into an LLM context window or downstream processing.

Q5: How do I troubleshoot connectivity or server startup issues?
Check that your AWS credentials are set, the region is correct, and your IAM policy grants the required Q Index permissions. Use the MCP client's logs (set FASTMCP_LOG_LEVEL=DEBUG) for further diagnostics.