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Cala

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3 months ago
Cala turns internet chaos into structured, verified knowledge that AI agents and LLMs can call as a tool. Building agentic products means relying on external information, but agents work best with verified, structured, typed data they can call deterministically, not the open web. Cala abstracts away ingestion, normalization, and verification behind a simple API, so you can ship agentic products faster without building brittle data pipelines.
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Overview
- The Knowledge Layer for AI Agents","creator":{"@type":"Organization","name":"Mintlify","url":"https://mintlify.com"}})||!a.startsWith("lang:"))&&(localStorage.removeItem(a),l--)}document.documentElement.setAttribute(d,k?"visible":"hidden")}catch(a){console.error(a),document.documentElement.setAttribute(d,"hidden")}})(\n {},\n "en",\n [],\n "data-banner-state",\n "bannerDismissed",\n)","id":"_mintlify-banner-script"}])

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https://api.cala.ai/mcp/
For more information about MCP and how it works, you can read more about it here.

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Connect your agent to Cala’s MCP

You can use Cala via our MCP. To get started, you need to get an API key to authenticate your MCP client. Go to our Console and create a new API key.

Get Cala's API key

Create a free Cala account and get your API key.
With your API key in hand, you can connect your AI agent such as Cursor, Claude Desktop and others to our MCP server. Cala MCP connects AI agents to our knowledge search, including verified, structured and typed knowledge, as well as entities information.
Add to ~/.cursor/mcp.json:
{
    "mcpServers": {
        "Cala": {
            "url": "https://api.cala.ai/mcp/",
            "headers": {
                "X-API-KEY": "YOUR_CALA_API_KEY"
            }
        }
    }
}
For more information, follow their documentation here.

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Available Tools

Here are the tools available to the Cala MCP:

knowledge_search

Search for verified knowledge using natural language queries. Returns trustworthy, verified knowledge with relevant context and matching entities.

knowledge_query

Search for verified knowledge using structured query syntax such as: startups.location=Spain.funding>10M.funding<=50M. Returns a structured response with matching entities.

entity_search

Search entities by name with fuzzy matching.

entity_introspection

Get the field schema for an entity by its UUID. Returns the available properties, relationships, and numerical observations you can use when querying an entity.

get_entity

Get information about an entity by its UUID.
Try in Playground

Server Config

{
  "mcpServers": {
    "Cala": {
      "url": "https://api.cala.ai/mcp/",
      "headers": {
        "X-API-KEY": "YOUR_CALA_API_KEY"
      }
    }
  }
}
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