Refine a prompt before sending to an agent.

Given an original prompt and context, either selects an existing refined prompt or creates a new one optimized for the agent type and language.

Scoping Options (Optional but Recommended): To improve focus and reduce context overload, include in the context dict:

• target_dirs: List of directories the agent should focus on (e.g., ["src/", "tests/"])

• target_files: Specific files to modify (e.g., ["src/main.py", "src/config.py"])

• exclude_paths: Paths to ignore (e.g., [".venv/", "node_modules/"])

• scope: High-level scope description (e.g., "backend API layer only")

• focus: Priority aspects (e.g., ["performance", "security", "error-handling"])

Note: A project_id is required. If not provided, the response will include a list of existing projects or instructions to create one. The agent should:

1. Call list_projects to see available projects, or

2. Call create_project to create a new one, then

3. Resubmit the refine_prompt request with the project_id

Update prompt performance metrics after agent execution.

Records the outcome of using a refined prompt and updates the prompt's statistics for future refinement decisions.

Scoping Feedback: Use files_modified and files_added to record which files were actually touched. This helps the system understand the effective scope of the prompt and can be used to suggest narrower scopes for similar future tasks.

Health check endpoint.

Returns the health status of the SIMPA MCP service.

Examples: Request (no parameters needed): json {}

Returns: Service health status with version and timestamp

Create a new project for organizing prompts.

Creates a project with language and dependency metadata to enable better prompt selection based on project context.

Project Structure & Scoping: Projects can define their default structure via project_structure:

• Default directories agents should focus on (e.g., ["src/", "tests/"])

• Default exclusions (e.g., [".venv/", "node_modules/"])

• Known entry points (e.g., ["src/main.py", "src/app.py"])

This helps downstream agents understand the project layout and scope their work appropriately without needing to explore the entire codebase.

Retrieve project information by ID or name.

Look up a project by either its ID (UUID) or name.

Project Scoping for Agents: Returns project_structure which defines default scoping for this project:

• src_dirs: Recommended directories to focus on

• test_dirs: Test directory locations

• entry_points: Main entry point files

• exclude: Paths to ignore

Use this structure when refining prompts to help agents understand the codebase layout and scope their work appropriately.

List all projects with optional filtering.

Retrieves a paginated list of projects, optionally filtered by programming language.

Project Scoping: Each project may include project_structure metadata that defines:

• src_dirs: Recommended source directories (e.g., ["src/", "lib/"])

• test_dirs: Test directories (e.g., ["tests/"])

• entry_points: Main entry points (e.g., ["src/main.py"])

• exclude: Paths to ignore (e.g., [".venv/", "pycache/"])

Use get_project to retrieve full structure details for a specific project, then use this information when scoping agent work via refine_prompt.

Activate a previously deactivated prompt.

Reactivates a prompt so it can be used in future refinement searches.

Deactivate a prompt so it won't be used in searches.

Soft-deletes a prompt by marking it as inactive. The prompt remains in the database but won't appear in search results or be used for finding similar prompts.