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AnswerAgent MCP for Answer Agent

This documentation outlines how to use the AnswerAgent Model Context Protocol (MCP) integration with Answer Agent. This MCP server provides comprehensive access to Answer AI's powerful features through standardized MCP tools, allowing you to manage chatflows, document stores, assistants, and more directly from your AI workflows.

Overview​

The AnswerAgent MCP server is a lightweight integration that exposes Answer AI's core functionality through the Model Context Protocol. It provides tools for:

  • Chatflow Management - Create, update, and manage AI chatflows
  • Document Store Operations - Manage knowledge bases and document collections
  • Assistant Management - Create and configure AI assistants
  • Tool Management - Create and manage custom tools
  • Document Loader Operations - Handle document processing and chunking
  • Intelligent Analysis - AI-powered analysis and optimization recommendations

Zero Configuration Setup​

🎉 No Credentials Required!

When using the AnswerAgent MCP within the Answer Agent platform, no manual credential setup is required. The integration automatically:

  • Uses your existing user account and permissions
  • Accesses your organization's data securely
  • Leverages your default API key from the database

Prerequisites​

  • An active Answer AI account
  • At least one active API key in your account

⚠️ Important: If you have deleted all your API keys, you must create a new one in the API Keys screen before using this MCP integration.

How to Create an API Key​

If you need to create an API key:

  1. Navigate to your Account Settings
  2. Go to API Keys section
  3. Click Create New API Key
  4. Give it a descriptive name
  5. Save the key - it will be automatically used by the MCP integration

Available Tools​

The AnswerAgent MCP provides comprehensive tools organized by functionality:

Chatflow Management​

create_chatflow​

Create new AI chatflows with custom configurations.

Required inputs:

  • name (string): Name for the new chatflow

Optional inputs:

  • flowData (string): JSON configuration for the chatflow

Returns: Created chatflow details including ID and configuration

get_chatflow​

Retrieve detailed information about a specific chatflow.

Required inputs:

  • id (string): Chatflow ID to retrieve

Optional inputs:

  • includeFullFlowData (boolean): Include complete flow configuration

Returns: Chatflow details and configuration

update_chatflow​

Update an existing chatflow's configuration or settings.

Required inputs:

  • id (string): Chatflow ID to update
  • updates (object): Update parameters

Returns: Updated chatflow information

delete_chatflow​

Remove a chatflow from your workspace.

Required inputs:

  • id (string): Chatflow ID to delete

Returns: Deletion confirmation

list_chatflows​

Get a list of all available chatflows in your workspace.

Returns: Array of chatflows with basic information

Document Store Management​

create_document_store​

Create a new document store for knowledge management.

Required inputs:

  • name (string): Name for the document store

Optional inputs:

  • description (string): Description of the store's purpose

Returns: Created document store details

get_document_store​

Retrieve information about a specific document store.

Required inputs:

  • id (string): Document store ID

Returns: Document store configuration and metadata

delete_document_store​

Remove a document store and all its contents.

Required inputs:

  • id (string): Document store ID to delete

Returns: Deletion confirmation

list_document_stores​

List all document stores in your workspace.

Returns: Array of document stores with basic information

query_vector_store​

Search for information within a document store using semantic search.

Required inputs:

  • storeId (string): Document store ID to search
  • query (string): Search query

Returns: Relevant documents and content matches

upsert_document​

Add or update documents in a document store.

Required inputs:

  • id (string): Document store ID
  • payload (object): Document data and metadata

Returns: Document operation confirmation

refresh_document_store​

Refresh and re-index all documents in a store.

Required inputs:

  • id (string): Document store ID
  • payload (object): Refresh configuration

Returns: Refresh operation status

Assistant Management​

create_assistant​

Create a new AI assistant with custom configuration.

Required inputs:

  • details (object): Assistant configuration including name, instructions, and capabilities

Returns: Created assistant information

get_assistant​

Retrieve details about a specific assistant.

Required inputs:

  • id (string): Assistant ID

Returns: Assistant configuration and metadata

update_assistant​

Update an existing assistant's configuration.

Required inputs:

  • id (string): Assistant ID to update
  • updates (object): Update parameters

Returns: Updated assistant information

delete_assistant​

Remove an assistant from your workspace.

Required inputs:

  • id (string): Assistant ID to delete

Returns: Deletion confirmation

list_assistants​

List all assistants in your workspace.

Returns: Array of assistants with basic information

Tool Management​

create_tool​

Create a custom tool for use in chatflows.

Required inputs:

  • name (string): Tool name

Optional inputs:

  • description (string): Tool description and purpose

Returns: Created tool details

get_tool​

Retrieve information about a specific tool.

Required inputs:

  • id (string): Tool ID

Returns: Tool configuration and metadata

update_tool​

Update an existing tool's configuration.

Required inputs:

  • id (string): Tool ID to update
  • updates (object): Update parameters

Returns: Updated tool information

delete_tool​

Remove a tool from your workspace.

Required inputs:

  • id (string): Tool ID to delete

Returns: Deletion confirmation

list_tools​

List all available tools in your workspace.

Returns: Array of tools with basic information

Document Loader Operations​

get_loader_chunks​

Retrieve document chunks from a specific loader.

Required inputs:

  • storeId (string): Document store ID
  • loaderId (string): Loader ID
  • pageNo (string): Page number for pagination

Returns: Document chunks and metadata

update_loader_chunk​

Update a specific document chunk.

Required inputs:

  • storeId (string): Document store ID
  • loaderId (string): Loader ID
  • chunkId (string): Chunk ID to update
  • payload (object): Update data

Returns: Update confirmation

delete_loader_chunk​

Remove a specific document chunk.

Required inputs:

  • storeId (string): Document store ID
  • loaderId (string): Loader ID
  • chunkId (string): Chunk ID to delete

Returns: Deletion confirmation

delete_loader​

Remove an entire document loader.

Required inputs:

  • storeId (string): Document store ID
  • loaderId (string): Loader ID to delete

Returns: Deletion confirmation

Intelligent Prompts​

The AnswerAgent MCP includes AI-powered prompts for advanced analysis and management:

analyze_chatflow​

Get intelligent analysis and optimization recommendations for your chatflows.

Required inputs:

  • chatflowId (string): The chatflow ID to analyze

Optional inputs:

  • focusAreas (array): Specific areas to focus analysis on (e.g., "performance", "accuracy", "cost")

Returns: Comprehensive analysis with actionable recommendations

analyze_document_store​

Analyze document store configuration, usage patterns, and optimization opportunities.

Required inputs:

  • documentStoreId (string): The document store ID to analyze

Optional inputs:

  • focusAreas (array): Specific analysis focus areas

Returns: Detailed analysis with optimization suggestions

manage_document_store​

Get step-by-step guidance for complex document store operations.

Required inputs:

  • action (string): Operation type - "setup", "optimize", "troubleshoot", or "migrate"

Optional inputs:

  • documentStoreId (string): Target document store ID
  • context (string): Additional context for the operation

Returns: Detailed guidance and recommended steps

Usage Examples​

Creating and Managing Chatflows​

"Create a new chatflow called 'Customer Support Bot' for handling customer inquiries"

"Analyze my 'Sales Assistant' chatflow and suggest performance improvements"

"List all my chatflows and show me which ones haven't been used recently"

Document Store Operations​

"Create a document store called 'Product Documentation' and add my PDF user manuals"

"Search my 'Customer Contracts' document store for information about payment terms"

"Help me troubleshoot why my document store isn't returning relevant results"

Assistant Management​

"Create an assistant specialized in code review with access to my development documentation"

"Update my 'Research Assistant' to include the latest OpenAI model"

"Show me all my assistants and their current configurations"

Advanced Analysis​

"Analyze my 'Technical Support' chatflow and focus on response accuracy and user satisfaction"

"Give me guidance on optimizing my document store for better search performance"

"Help me set up a new document store for legal documents with proper access controls"

Best Practices​

Chatflow Management​

  • Use descriptive names for chatflows that reflect their purpose
  • Regularly analyze chatflows for optimization opportunities
  • Keep chatflow configurations backed up by exporting them

Document Store Organization​

  • Organize documents into logical stores by topic or department
  • Use descriptive names and descriptions for easy identification
  • Regularly refresh document stores to keep content current

Security Considerations​

  • The MCP integration respects your existing permissions and access controls
  • All operations are logged and auditable through your Answer AI instance
  • Document stores maintain the same security boundaries as your account

Troubleshooting​

Common Issues​

"Unable to retrieve user API key from database"​

Solution: You need to create an API key in your account:

  1. Go to Account Settings → API Keys
  2. Click Create New API Key
  3. Give it a descriptive name and save

"API_HOST environment variable is not set"​

Solution: This indicates a platform configuration issue. Contact your system administrator.

"No Available Actions" in the tool selection​

Solutions:

  1. Check that you have active API keys in your account
  2. Refresh the tool configuration
  3. Verify your account permissions

Permission Denied Errors​

Solutions:

  1. Verify you have the necessary permissions for the operation
  2. Check that you're working within your organization's scope
  3. Ensure your API key has sufficient privileges

Getting Help​

If you continue to experience issues:

  1. Check the API Keys section in your account settings
  2. Review your account permissions with your administrator
  3. Contact support with specific error messages

External Usage​

While this documentation focuses on platform usage, the AnswerAgent MCP server is also available as a standalone package that can be used with external MCP clients like Claude Desktop, Cursor IDE, and others. For external usage, you would need to:

  1. Install via npm: npm install -g @answerai/answeragent-mcp
  2. Configure with your API base URL and JWT token
  3. Use with your preferred MCP-compatible client

See the npm package documentation for external setup instructions.