The ModelRouter MCP server provides advisory LLM model recommendations, task and repository analysis, and model registry management for Cursor. All recommendations are advisory — the user must manually select the model. No user data is sent externally.

• recommend_model: Advisory model recommendation based on plan and repo metadata, with optional alternatives and config overrides.

• analyze_task: Parse a plan document to determine task type, complexity, and requirement vector.

• analyze_repository: Inspect repo metadata (file counts, languages, frameworks, diff stats) without reading source file contents.

• estimate_cost: Estimate token usage and cost for a specific model given a plan.

• list_models: List all models in the registry with basic metadata.

• get_model_profile: Fetch the full profile and provenance for a specific model by ID.

• sync_metadata: Refresh model metadata from external fixture-based adapters (blocked in privacy mode).

• get_telemetry_summary: View local telemetry data — never leaves the machine via outbound HTTP.

• generate_cursor_guidance: Generate advisory Cursor guidance snippets — does not automatically apply or enforce model selection.

Integrates Ollama as a local model registry, enabling recommendations for locally hosted Ollama models.

1. Click on "Install Server".

2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.

3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ModelRouter MCP recommend a model for my Python data science project"

That's it! The server will respond to your query, and you can continue using it as needed.

Here is a step-by-step guide with screenshots.