#specification

A standardized framework for enhancing how LLMs process and respond to prompts through composable decorators, featuring an official open standard specification and Python reference implementation with MCP server integration.

MCP Servers discovery spec

Agent network builder for communicating with openapi apis. Based in autogen

A tool for automatically converting OpenAPI specifications into Model Context Protocol (MCP) Server

MCP server providing token-efficient access to OpenAPI/Swagger specs via MCP Resources for client-side exploration.

DevSpeak bridges the gap between product and engineering. Powered by Gemini AI, it instantly translates ambiguous stakeholder requirements into structured technical specs, Jira tickets, and API designs tailored to your specific tech stack and audience level.

A tool for automatically converting OpenAPI specifications into Model Context Protocol (MCP) Server instance

Agent network builder for communicating with openapi apis. Based in autogen

Zenable cleans up sloppy AI code, prevents vulnerabilities, and automates governance with deterministic guardrails so developers can ship faster, safer, and with confidence. Use Cases: 1. AI coding assistants generate vulnerable code with security flaws and compliance violations. Zenable acts as a real-time safety net that catches SQL injections, hardcoded secrets, and policy violations as code is written - ensuring you ship AI-accelerated code with confidence and zero security compromises. 2. Rapid AI-driven development introduces inconsistent patterns and technical debt. Zenable provides automated governance checks that ensure every AI-generated feature meets your standards, enabling 10x development speed without sacrificing code quality. 3. Bugs slip through reviews and automated testing misses edge cases. Zenable provides AI-powered analysis that identifies subtle bugs and suggests fixes before production, resulting in fewer production incidents.

Catch spec drift before it catches you. Pituitary indexes your specs, docs, and decision records, then detects overlap, contradictions, stale docs, and code that drifts from what was decided. 6 tools for spec-aware AI workflows: - search_specs: semantic search across indexed spec sections - check_overlap: detect specs that cover overlapping ground - compare_specs: side-by-side tradeoff analysis of two specs - analyze_impact: trace which specs, docs, and code paths are affected when a spec changes - check_doc_drift: find docs that contradict accepted specs - review_spec: full composite review (overlap + comparison + impact + drift + remediation) Single Go binary. One SQLite file. No API keys required. Deterministic by default.