Documentation Index
Fetch the complete documentation index at: https://docs.credibledata.com/llms.txt
Use this file to discover all available pages before exploring further.
Credible’s MCP tools use the Credible Context Engine to ground your LLM in governed data definitions. When you ask a question, the get_context tool parses your input into semantic phrases and matches each phrase to data entities (dimensions, measures, views) in your semantic model — searching against the #(doc) descriptions and #(index) annotations declared in your model. Your LLM gets ranked entity matches and Malloy syntax guidance, so it can construct accurate queries without hallucinating field names or misunderstanding your data structure.
LLMs and MCP clients change frequently. If you encounter any issues with these integrations, please contact support. Connecting LLMs
Use your organization’s MCP server URL: https://<your-org>.mcp.credibledata.com/mcp
To restrict analysis to a specific workspace, use the workspace-scoped URL instead: https://<your-org>.mcp.credibledata.com/mcp/workspace/{workspace_name}. You can copy the full workspace-scoped URL from your workspace settings page.
Claude (web)
ChatGPT (web)
VS Code Copilot
Cursor
Claude Code
Gemini CLI
Windsurf
Requires a Pro or Max subscription, OR a business/enterprise account with custom connectors enabled
- Click on user icon (bottom-left)
- Connectors
- Browse
- Click on the link “add a custom one” (note this is not a button)
- Enter Name and URL:
https://<your-org>.mcp.credibledata.com/mcp
- Leave advanced settings alone
- Click “connect” on the next page
If you don’t see the option to add custom connectors on an enterprise account, contact your Claude administrator to enable this feature.
Requires a Plus account or higher
- Click on user icon (bottom-left)
- Settings
- Apps & Connectors
- Click “Create”
- Add a name (e.g., “Credible”) and the server URL:
https://<your-org>.mcp.credibledata.com/mcp
- Leave authentication as “OAuth” and click “Create”
- Follow the OAuth authentication flow
- Now you can ask ChatGPT to use the Credible tool (or whatever you named it)
You may need to enable “Dev mode” in Apps & Connectors settings (scroll to bottom) before you can create custom connectors.
- CMD-SHIFT-P -> “MCP Add Server”
- Select “HTTP”
- Enter your organization’s MCP URL:
https://<your-org>.mcp.credibledata.com/mcp
- Enter a server ID for the server (e.g., “credible”)
- Press Enter and select “Global” or “Workspace” scoped
- A popup should redirect you to the OAuth flow - open in browser and complete the authentication
- The server should now show as “Running” (with a checkbox) in the mcp.json file
- Open Copilot to test it out
- CMD-SHIFT-P -> “Open MCP Settings”
- Click “New MCP Server”
- Add your server configuration:
{
"mcpServers": {
"CredibleData": {
"url": "https://<your-org>.mcp.credibledata.com/mcp",
"type": "http"
}
}
}
- Click “Connect” to complete the OAuth authentication flow
- The server should show as connected with 2 tools enabled
Requires a Pro or Max subscription plan
# Add MCP server
claude mcp add --transport http CredibleData https://<your-org>.mcp.credibledata.com/mcp
# Start Claude Code
claude
# Inside Claude Code prompt, authenticate
/mcp
# Select "authenticate"
# Install Gemini CLI
npm install -g @google/gemini-cli
# Add MCP server
gemini mcp add -t http CredibleData https://<your-org>.mcp.credibledata.com/mcp
# Run gemini to complete OAuth flow
gemini
# This will open a browser window for authentication
# Verify the server is connected
gemini mcp list
- In the agent window, click on the plug icon
- Click “Settings” - this will open the
mcp_config.json file
- Add your server configuration:
{
"mcpServers": {
"CredibleData": {
"serverUrl": "https://<your-org>.mcp.credibledata.com/mcp"
}
}
}
- Save the file - this will automatically open the OAuth authentication flow
- Complete the authentication in your browser
- get_context — Parses your question into phrases, matches each phrase to data entities (dimensions, measures, views) in your semantic model, and returns ranked matches grounded in your model’s
#(doc) and #(index) annotations
- execute_query — Executes Malloy queries against your semantic models and returns JSON results
For complete technical details on parameters and responses, see the MCP Reference.
