SpecLock AI Constraint Engine

SpecLock AI Constraint Engine is a developer-focused MCP server built to keep AI coding assistants inside the guardrails you define, before code ever reaches production. It ships with 42 MCP tools centered on an AI Patch Firewall — a layer that inspects, validates, and constrains the diffs, patches, and file edits that AI copilots propose. If you're running Claude Code, Cursor, Windsurf, or Cline against a real codebase, SpecLock acts as the policy layer between "the AI wants to change this" and "this change is actually allowed," enforcing spec adherence, dependency rules, and change scope so autonomous coding sessions don't silently drift outside intended boundaries.

At its core, SpecLock is an AI constraint engine: you define specs, invariants, and boundaries (which files, which modules, which architectural rules are off-limits or require review), and the engine evaluates every proposed patch against those constraints before it's applied. This is especially valuable for teams doing multi-file refactors, agentic coding loops, or long-running autonomous sessions where a coding agent might otherwise touch code outside its assigned task. The AI Patch Firewall specifically targets the failure mode where an LLM-based coding tool makes a plausible-looking but out-of-scope or unsafe edit — catching it at the patch level rather than after a broken PR lands.

Hosted through BusinessMCP.com, SpecLock becomes one tool among many exposed through a single unified MCP endpoint at /api/mcp, authenticated with your mcph_* Bearer key. Instead of wiring SpecLock separately into Claude, Cursor, Windsurf, and Cline and managing four different integrations, you connect it once through BusinessMCP and every model-agnostic AI agent your team uses can call its 42 tools consistently. That unification is the point: your constraint policies, patch history, and firewall decisions become visible in the same business-intelligence dashboard where you already track usage across your other connected tools, databases, and revenue sources — so engineering leadership can see how often AI-proposed patches were blocked, modified, or approved, alongside the rest of company activity, without standing up separate infrastructure.

Teams typically reach for SpecLock when AI coding agents are given broad repo access but need scoped permission to specific directories, modules, or change types — for example, allowing an agent to modify test files freely while requiring human review for anything touching authentication or billing code. It's also useful for enforcing coding standards and architectural specs across a team using multiple AI coding tools, since the constraint rules live centrally rather than being re-implemented per editor. Because it's cookieless and GDPR-friendly by design, it fits naturally into engineering environments with data-handling requirements, and because it's model-agnostic, switching from one coding assistant to another (or running several in parallel) doesn't require re-establishing your constraint policies — they travel with the hosted MCP server, not the client.