ModelFetch

Creates Model Context Protocol (MCP) servers that run across multiple JavaScript/TypeScript runtimes (Node.js, Deno, Bun, browsers) without runtime-specific code paths. Abstracts away runtime differences through a unified SDK interface that detects and adapts to the host environment, enabling single-source deployment across heterogeneous execution contexts.

Registers tools/resources with MCP servers using declarative JSON schemas that define input parameters, output types, and tool metadata. The framework validates incoming requests against these schemas and automatically marshals data between the MCP protocol format and native TypeScript types, reducing boilerplate for tool implementation.

Generates deployment artifacts (Docker images, serverless function bundles, standalone binaries) from MCP server code with minimal configuration. Handles dependency bundling, runtime selection, and environment variable injection, enabling one-command deployment to various platforms (Docker, AWS Lambda, Vercel, etc.).

Loads and validates configuration from environment variables with type checking and default values, ensuring MCP servers start only with valid configuration. Supports configuration schemas that define required variables, types, and constraints, with helpful error messages when configuration is invalid.

Abstracts LLM provider APIs (OpenAI, Anthropic, local models) behind a unified SDK interface that normalizes request/response formats, token counting, and streaming behavior. Developers write tool-calling logic once and switch providers by changing configuration, with the framework handling protocol differences internally.

Manages streaming responses from MCP servers with built-in backpressure handling to prevent memory overflow when clients consume data slower than the server produces it. Implements buffering strategies and flow control that adapt to network conditions, allowing long-running operations to stream results without blocking or accumulating unbounded buffers.

Manages MCP server startup, shutdown, and resource cleanup across different runtimes with hooks for initialization and teardown logic. Ensures in-flight requests complete before shutdown, persistent connections close cleanly, and resources (database connections, file handles) are released properly, preventing resource leaks across runtime restarts.

Implements a composable middleware system for intercepting and transforming MCP requests and responses before they reach tool handlers or clients. Middleware can log, authenticate, rate-limit, transform payloads, or inject context, executing in a defined order with early-exit capabilities for rejecting invalid requests.

Provides configurable error handling and automatic retry mechanisms for transient failures (network timeouts, rate limits, temporary service unavailability). Implements exponential backoff with jitter to prevent thundering herd, and distinguishes between retryable and permanent errors, allowing different strategies per error type.

Automatically generates MCP-compliant tool definitions and JSON schemas from TypeScript interfaces and type annotations, eliminating manual schema writing. Uses TypeScript's type system to infer parameter types, optional fields, and constraints, then generates corresponding JSON schemas that are kept in sync with code changes through compile-time validation.

Automatically propagates request context (trace IDs, user IDs, request metadata) through the entire request lifecycle, including across async boundaries and middleware. Integrates with standard tracing libraries (OpenTelemetry) to enable distributed tracing and observability, making it easy to correlate logs and metrics across tool calls.

Manages pools of expensive resources (database connections, HTTP client connections, LLM API connections) with configurable pool sizes, connection reuse, and automatic cleanup. Prevents resource exhaustion by enforcing pool limits and provides metrics on pool utilization for monitoring and tuning.