> ## 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. # Model Context Protocol Reference > Technical reference for the Credible Data consumption MCP server API Technical reference for the Credible Data consumption MCP server, available at `https://.mcp.credibledata.com/mcp`. This page documents the **consumption MCP server** used by LLMs, workspace chat, and custom agents. The **Credible-Modeling MCP server** used by coding agents for building models is a separate, auto-configured server — see [Local Development](/how-to/modeling/local-development). ## Core Tools ### get\_context Parses a natural language question into semantic phrases, then matches each phrase to data entities in your published semantic models. Matches are grounded in the `#(doc)` descriptions and `#(index)` annotations declared in your model — the richer your documentation, the better the matches. This is the core retrieval tool powering Credible's [Credible Context Engine](/concepts/context-engine). **How it works:** 1. **Phrase extraction** — An LLM parses your input into semantic phrases (e.g., "top selling brands by month" becomes phrases like "top selling", "brands", "by month") 2. **Entity matching** — Each phrase is matched against your model's indexed metadata using embedding-based semantic search. This searches `#(doc)` descriptions, field names, and `#(index)` dimensional values. Matching is semantic, not exact — for example, "soccer games" can match a program titled "World Cup Finals" via indexed values and a genre of "Sports" via doc tags 3. **Ranked results** — Returns matched entities (dimensions, measures, views, columns) grouped by phrase, sorted by match score Credible Context Engine matching phrases to semantic model entities **Parameters:** * `natural_language_query` (required): The user's question in natural language (e.g., "What were our top-selling products last year?") * `environment_name` (optional): Environment name to search within. Only use if known from context. * `package_name` (optional): Package name to narrow search scope. Requires `environment_name`. * `model_uri` (optional): Path to a specific `.malloy` model file. Requires `environment_name` and `package_name`. * `source_name` (optional): Specific source within a model. Requires `environment_name`, `package_name`, and `model_uri`. **Parameter Dependencies:** `environment_name` → `package_name` → `model_uri` → `source_name` **Scope Strategy:** Start broad when uncertain, narrow as you discover structure. If results are insufficient, widen scope by removing parameters from right to left. **Response:** * `sources`: Array of matched sources, each containing: * `phrases`: Matched phrases from your input, each with: * `phrase`: The extracted phrase text * `phrase_description`: Extended description of the phrase * `overall_score`: Match confidence score * `entities`: Matched data entities (dimensions, measures, views, columns) with `name`, `field_type`, `data_type`, `description`, `score`, `match_reason`, and `values` (for dimensions with indexed values) * `next_steps`: Instructions for writing Malloy queries using the returned entities * `malloy_documentation`: Malloy syntax reference and common error patterns **Example Request:** ```bash theme={null} curl -X POST "https://your-org.mcp.credibledata.com/mcp" \ -H "Content-Type: application/json" \ -H "Authorization: ApiKey YOUR_API_KEY" \ -d '{ "jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": { "name": "get_context", "arguments": { "natural_language_query": "What are the top 10 products by sales?", "environment_name": "your-environment" } } }' ``` ### execute\_query Executes Malloy queries against published data models and returns JSON results. **Parameters:** * `environment_name` (required): Environment containing the model * `package_name` (required): Package containing the model * `model_uri` (required): Path to the `.malloy` model file * `query` (optional)\*: Custom Malloy query code. Do NOT provide `source_name` when using this. * `query_name` (optional)\*: Name of predefined query/view to execute * `source_name` (optional)\*: Source name. Required when using `query_name`, omit when using custom `query`. * `version_id` (optional): Specific package version to query against **\*Execution Patterns:** Use exactly ONE of: 1. Custom query: Provide `query` parameter only 2. Predefined query: Provide both `query_name` and `source_name` **Response:** Returns query results as JSON with `data`, `totalRows`, `executionTime`, and `metadata` ## Workspace Scoping The MCP server URL can optionally be workspace-scoped by appending `/workspace/{workspace_name}` to the URL. This restricts analysis to only the packages available in a specific workspace, rather than searching across all environments. * **Environment-scoped** (default): `https://.mcp.credibledata.com/mcp` * **Workspace-scoped**: `https://.mcp.credibledata.com/mcp/workspace/{workspace_name}` You can copy the full workspace-scoped URL from your workspace settings page. ## Authentication The MCP server supports two authentication methods: * **OAuth 2.0**: For connecting AI assistants like Claude Desktop or ChatGPT. See [LLMs and MCP Tools](/how-to/analyzing/ai-assistants-mcp). * **API Key**: For building custom agents with server-to-server communication. Pass as `Authorization: ApiKey YOUR_API_KEY` header. See [Custom AI Agents](/how-to/integrating/custom-ai-agents). Configure your MCP client with your organization's endpoint: `https://.mcp.credibledata.com/mcp` ## Error Handling The server returns standard MCP error responses for invalid requests, authentication failures, and query execution errors. Refer to the MCP specification for error code details.