Ptkmcp – An MCP Server for PTK-Formatted Models

Ptkmcp fills a niche that many AI developers encounter when working with large language models packaged in the PTK (Package Toolkit) format. The PTK format bundles a model’s weights, tokenizer configuration, and inference logic into a single distributable artifact that can be version‑controlled and deployed across heterogeneous environments. However, PTK artifacts lack a standardized runtime interface, making it difficult for AI assistants such as Claude to discover and invoke them automatically. Ptkmcp solves this by exposing a fully‑featured MCP (Model Context Protocol) server that reads PTK bundles and presents them as first‑class resources to any MCP‑compatible client.

The server’s core responsibility is to act as a bridge between the static PTK package and dynamic AI workflows. It parses the PTK metadata, loads the model into an inference engine (e.g., a local GPU or CPU backend), and registers the resulting tool with the MCP protocol. Developers can then call the model as a callable function from within their assistant’s prompt or script, passing arbitrary input text and receiving generated responses without writing custom integration code. This abstraction is valuable because it removes boilerplate, ensures consistent error handling, and guarantees that model updates are reflected immediately in the assistant’s behavior.

Key capabilities of Ptkmcp include:

• Automatic resource registration – The server scans a specified directory, identifies PTK packages, and registers each as an MCP resource with metadata such as name, version, and description.
• Tool invocation – Clients can call the model via a simple JSON‑based request, specifying prompts and optional generation parameters (temperature, max tokens).
• Prompt templating – The server supports embedding the model’s prompt templates directly into MCP prompts, allowing developers to reuse common instruction patterns.
• Sampling configuration – Users can expose multiple sampling strategies (e.g., greedy, top‑k, nucleus) as separate tools, enabling fine‑tuned control over generation quality.
• Health monitoring – Built‑in inspector endpoints provide real‑time status, memory usage, and inference latency metrics, aiding observability in production deployments.

Typical use cases span from rapid prototyping to full‑scale deployment:

• Research labs quickly spin up new PTK models, expose them via MCP, and iterate on prompt engineering without redeploying code.
• Enterprise AI teams integrate proprietary PTK models into internal assistants, ensuring consistent versioning and auditability.
• Open‑source contributors share PTK artifacts on a public MCP server, allowing the community to experiment with cutting‑edge models through any MCP‑enabled assistant.

By standardizing how PTK models are discovered and invoked, Ptkmcp empowers developers to focus on higher‑level AI logic rather than low‑level integration details. Its tight coupling with the MCP ecosystem means that once a PTK model is registered, it becomes instantly usable by any assistant—Claude, Gemini, or future clients—making it a compelling choice for teams that value flexibility, reproducibility, and rapid iteration.