MCP-PIF-CLJS: Self‑Modifying, Reasoning‑Enabled MCP Server

MCP‑PIF‑CLJS tackles a common pain point for developers building AI‑driven applications: the need to evolve tooling on the fly while preserving safety and formal guarantees. Traditional MCP servers expose a static set of tools that must be defined at startup, which forces developers to pre‑declare every operation they might need. In contrast, MCP‑PIF‑CLJS embraces ClojureScript’s homoiconicity to allow models such as Claude to generate, validate, and execute new tools during runtime. This self‑modifying capability eliminates restart cycles, reduces operational overhead, and keeps the AI’s workflow fluid.

At its core, the server offers a rich toolkit that spans basic data storage, meta‑programming, and formal reasoning. Developers can store key‑value pairs with and retrieve them later, while the journal tools let them audit recent activity. The meta‑programming layer provides , which accepts code snippets and materialises them as new tools—arithmetic, string manipulation, or even typed functions—without touching the server’s core. Because new tools are treated as first‑class citizens, they can be queried through and invoked via the generic , ensuring compatibility with existing MCP clients.

Formal reasoning is where MCP‑PIF‑CLJS truly shines. With , developers can run beta reductions on arbitrary lambda calculus expressions, gaining insight into functional transformations. The tool implements Hindley‑Milner inference, allowing the AI to reason about polymorphic types and detect type errors before execution. For propositional logic, offers automated theorem proving, enabling the assistant to verify logical implications and construct proofs on demand. These capabilities empower developers to build AI assistants that can not only perform calculations but also understand and reason about code, logic, and data.

Real‑world scenarios for MCP‑PIF‑CLJS abound. A data scientist might ask the assistant to create a custom statistical tool, have it validated by type inference, and then apply it to a dataset—all without restarting the server. A software engineer could request a theorem prover to confirm that a refactoring preserves correctness, while a product manager could dynamically generate reporting tools tailored to new metrics. Because the server is written in ClojureScript, it runs on Node.js and integrates seamlessly with existing JavaScript tooling, making it a natural fit for modern web‑based AI workflows.

What sets MCP‑PIF‑CLJS apart is its combination of self‑modification, formal guarantees, and runtime safety. By leveraging the host language’s code‑as‑data paradigm, it allows AI assistants to evolve their own toolset while still maintaining rigorous type and logical checks. This unique blend of flexibility and assurance gives developers a powerful platform to prototype, iterate, and deploy AI‑enhanced applications with confidence.