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MCP Tools

The RAG server exposes MCP (Model Context Protocol) tools that enable AI agents to search, fetch, and explore the knowledge base. These tools provide a standardized interface for LLMs to access organizational knowledge.

For configuration details, see the Server README.

What is MCP?​

Model Context Protocol (MCP) is an open standard for connecting AI models to external tools and data sources. Instead of custom integrations, MCP provides:

  • Standardized tool interface for any MCP-compatible client
  • Streaming responses via Server-Sent Events (SSE)
  • Tool discovery so agents know what capabilities are available

Connecting to CAIPE RAG​

MCP Endpoint​

http://localhost:9446/mcp

Compatible Clients​

Any MCP-compatible client can connect:

  • Claude Desktop
  • VS Code with GitHub Copilot
  • Cursor
  • Continue
  • Custom LangChain/LangGraph agents

Example: Claude Desktop Configuration​

Add to your Claude Desktop config:

{
  "mcpServers": {
    "caipe-rag": {
      "command": "npx",
      "args": ["-y", "@anthropic/mcp-client", "http://localhost:9446/mcp"]
    }
  }
}

Search + Fetch Pattern​

The MCP tools follow a search + fetch pattern that mirrors how humans use search engines:

  1. Search returns truncated snippets (500 chars) with metadata
  2. Scan results to identify relevant documents
  3. Fetch full content of specific documents

This pattern is token-efficient: agents scan many results quickly, then fetch only what they need.

Available Tools​

Core Search Tools​

Hybrid semantic and keyword search across all indexed content.

Parameters:

  • query (required): Search query string
  • top_k: Number of results (default: 10)
  • bias: Search strategy - "semantic" or "keyword" (default: balanced)
  • filters: Metadata filters (see below)

Returns: List of results with truncated content (500 chars), scores, and metadata.

Use when: Finding relevant documents, exploring a topic, answering questions.

fetch_document​

Retrieve full content of a specific document by ID.

Parameters:

  • document_id (required): Document ID from search results

Returns: Complete document content and metadata.

Use when: Need full context after identifying a relevant document via search.

fetch_datasources_and_entity_types​

List all available datasources and entity types in the knowledge base.

Parameters: None

Returns: List of datasources with their entity types.

Use when: Discovering what data is available, building filters.

Graph Exploration Tools​

These tools are available when Graph RAG is enabled (ENABLE_GRAPH_RAG=true).

graph_explore_ontology_entity​

Explore entity type schemas and their relationships.

Parameters:

  • entity_type (required): Type name (e.g., "Pod", "Deployment")
  • hops: Relationship depth (1-3, default: 1)

Returns: Entity type schema with properties and connected relationship types.

Use when: Understanding the knowledge graph schema, discovering what relationships exist.

graph_explore_data_entity​

Explore a specific entity instance and its neighborhood.

Parameters:

  • entity_type (required): Type name
  • entity_id (required): Entity identifier
  • hops: Relationship depth (1-3, default: 1)

Returns: Entity with properties and related entities.

Use when: Investigating a specific resource and its connections.

graph_fetch_data_entity_details​

Get complete properties and all relations for an entity.

Parameters:

  • entity_type (required): Type name
  • entity_id (required): Entity identifier

Returns: Full entity details including all properties and relationships.

Use when: Need complete information about a specific entity.

graph_shortest_path_between_entity_types​

Find relationship paths between two entity types.

Parameters:

  • source_type (required): Starting entity type
  • target_type (required): Ending entity type

Returns: Path in Cypher notation showing relationship chain.

Use when: Understanding how entity types are connected, planning graph queries.

graph_raw_query_data / graph_raw_query_ontology​

Execute custom read-only Cypher queries.

Parameters:

  • query (required): Cypher query string (read-only)

Returns: Query results (limited to configured max results).

Use when: Complex queries that can't be expressed with other tools.

Note: Queries are automatically scoped to the correct tenant labels (NxsDataEntity or NxsSchemaEntity).

Filtering​

Search and exploration tools support metadata filters:

Filter KeyDescriptionExample
datasource_idFilter by data source"aws-production"
ingestor_idFilter by ingestor"k8s-ingestor"
is_graph_entityOnly graph entitiestrue
graph_entity_typeFilter by entity type"Pod"
document_typeFilter by document type"runbook"

Filters are combined with AND logic.

Example Agent Workflow​

Here's how an AI agent might use these tools to answer "What pods are running on node worker-1?":

  1. Discover schema:

    fetch_datasources_and_entity_types()
    → Sees "Pod" and "Node" entity types from k8s datasource

  2. Explore relationships:

    graph_explore_ontology_entity(entity_type="Pod", hops=1)
    → Sees Pod has "RUNS_ON" relationship to Node

  3. Find the node:

    search(query="worker-1", filters={"graph_entity_type": "Node"})
    → Gets Node entity ID

  4. Explore node's pods:

    graph_explore_data_entity(entity_type="Node", entity_id="worker-1", hops=1)
    → Gets all Pods connected to this Node

Configuration​

Enable/Disable MCP​

ENABLE_MCP=true  # default

Result Truncation​

SEARCH_RESULT_TRUNCATE_LENGTH=500  # Characters per result in search

Graph Query Limits​

MAX_GRAPH_RAW_QUERY_RESULTS=100   # Max entities per query
MAX_GRAPH_RAW_QUERY_TOKENS=80000  # Max tokens in results

Further Reading​