open-apia

open-apia appears to implement the APAI (Architecture Protocol for Artificial Intelligence) MCP server. It aims to describe, document, and validate AI systems and their components (models, prompts, constraints, workflows, and evaluation metrics) in a clean, machine- and human-readable format. The server likely exposes tooling to register APAI specifications, validate configurations, and orchestrate automation workflows in integration with external platforms. Users can leverage APAI-compliant specs to generate documentation, API clients, and deployment artifacts, and to configure multi-environment deployments. Once running, you can use the server to upload or reference APAI YAML specifications, validate their structure, and generate or serve documentation and integration guides for your AI systems. Tools and capabilities typically include: validation of APAI specs against the schema, generation of documentation, code and deployment artifacts, and orchestration hooks for automation platforms such as n8n or Zapier. To use it, start the MCP server with the provided command, point it at your APAI specs, and then access its endpoints or CLI to validate specs, render docs, or export artifacts. If the server exposes a REST or GraphQL API, you can programmatically query for available specifications, schemas, and validation results, and integrate those capabilities into your CI/CD pipeline.

Prerequisites:

• Node.js (recommended if using a Node-based server entry) or the runtime specified by the MCP server implementation
• npm or yarn (for dependency installation if applicable)
• Git (to clone the repository or fetch dependencies)

1. Clone the repository:

git clone https://github.com/fabioguin/open-apia.git
cd open-apia

2. Install dependencies (adjust if a different runtime is used):

npm install

3. Configure environment variables (examples):

# Copy example env and edit credentials
cp .env.example .env

Edit .env to set API keys, storage endpoints, and any APAI-specific paths.

4. Run the MCP server:

npm start

5. Verify the server is running by listing capabilities or performing a basic validation call as documented in the repository (e.g., via a local URL like http://localhost:3000/health).

Notes:

• If this MCP server uses a different runtime (e.g., Python or Docker), follow the runtime-specific steps in the repository's docs and replace the start command accordingly.
• Ensure your APAI spec files are accessible to the server (local path, mounted volume, or remote URL depending on configuration).

Tips and common issues:

• Environment variables: Ensure API keys and storage endpoints are correctly configured; missing credentials will cause validation or storage operations to fail.
• Validation: Use the server's built-in validator to ensure APAI YAML files conform to the specification before attempting generation of docs or artifacts.
• Environment separation: Use dev/staging/production configurations to avoid leaking test specs into production endpoints.
• UUIDs and identifiers: Keep consistent IDs for models, prompts, and tasks to avoid schema mismatches when composing hierarchical specifications.
• Networking: If running behind a firewall or in a containerized environment, expose the correct ports and configure CORS as needed for your tooling.
• Documentation generation: If the server can render interactive docs, consider exporting or publishing them to a docs portal for your team.