AgentQL

Translates natural language prompts into structured web data extraction by implementing the Model Context Protocol (MCP) specification as a middleware bridge to the AgentQL API. The server receives MCP tool calls with URL and natural language description parameters, forwards them to AgentQL's backend extraction engine, and returns structured JSON results back to MCP-compatible clients. This enables AI agents to query unstructured web content using conversational intent rather than CSS selectors or XPath expressions.

Implements the Model Context Protocol server specification by registering the extract-web-data tool with MCP-compatible clients, handling tool discovery requests, and managing the request-response lifecycle. The server exposes tool metadata (name, description, input schema) to clients on startup, validates incoming MCP requests against the schema, and returns responses in MCP-compliant format. This enables clients to discover available capabilities and invoke them through a standardized interface.

Provides standardized configuration patterns for deploying the MCP server across multiple client applications (Claude Desktop, VS Code, Cursor, Windsurf) through environment variables and client-specific config files. The server reads AGENTQL_API_KEY from environment, supports both direct env variable injection and interactive prompts depending on client, and can be invoked via npx without global installation. This enables teams to deploy the same server binary across heterogeneous IDE environments with minimal configuration drift.

Manages secure authentication with the AgentQL backend by reading the AGENTQL_API_KEY from environment variables and including it in all API requests. The server acts as a proxy, translating MCP tool calls into AgentQL API requests with proper headers and authentication, then marshaling responses back to MCP format. This pattern isolates API credentials from client applications and enables centralized request logging and error handling.

Packages the MCP server as a Docker image with Node.js runtime, enabling deployment to container orchestration platforms (Kubernetes, Docker Compose, cloud services). The Dockerfile specifies the build process, dependencies, and runtime configuration, allowing the server to be deployed as a containerized service with environment variable injection for API keys. This enables teams to run the MCP server as a managed service rather than a local process.

Implements GitHub Actions workflows for automated testing, code quality checks, and security scanning on every commit. The pipeline runs linting (ESLint), code formatting checks (Prettier), secret scanning, and dependency vulnerability scanning before allowing merges. This ensures code quality and security standards are maintained across contributions without manual review overhead.

Configures Git pre-commit hooks that automatically run linting (ESLint) and code formatting (Prettier) before commits are created. If code fails checks, the commit is blocked until issues are resolved. This prevents poorly formatted or non-compliant code from entering the repository, reducing CI/CD feedback cycles and maintaining consistent code style across contributors.

Provides TypeScript configuration and development tooling for type-safe implementation of the MCP server. The project includes TypeScript compiler configuration (tsconfig.json), type definitions for MCP protocol and AgentQL API, and development dependencies for building and testing. This enables developers to catch type errors at compile time and provides IDE autocomplete for MCP and AgentQL APIs.

Publishes the AgentQL MCP server to the npm registry as the agentql-mcp package, enabling installation via npm install or execution via npx without cloning the repository. The package.json defines metadata, dependencies, build scripts, and entry points. This enables users to install and run the server with a single command (npx -y agentql-mcp) without managing source code or build processes.