"Information on the Theory of Evolution" matching MCP servers:

A local MCP server for working with Logisim-evolution .circ projects, providing structured project inspection, guarded edits, validation, circuit analysis, test execution, format conversion, and GUI launch.

Enables checking project files like Dockerfile, .nvmrc, and CI workflows to determine whether pinned runtime, database, and OS versions are still supported, providing end-of-life dates, latest patches, and upgrade targets for 470+ products.

Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Enterprise-grade code intelligence server for AI assistants that analyzes codebases for due diligence, tech debt, security vulnerabilities, and code quality.

Enables agents to govern evolving software by measuring trajectory, drift, invariants, and incidents, and by forking and measuring alternative futures before adoption.

Enables agents to audit and safeguard repositories by detecting dependency pinning issues, license compliance problems, hardcoded secrets, and dead code through MCP tools.

An MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.

Enables coding agents to audit a Next.js codebase read-only, checking routes, Prisma models, environment variables, and locale consistency via MCP tools.

MCP server for automatic Storybook story generation, component analysis, and validation. Auto-detects React frameworks and syncs component documentation.

An MCP server that extends AI coding assistants with deterministic, algorithmic capabilities such as code analysis, fault localization, and formal verification, enabling an autonomous engineering team within the IDE.

Validates Mermaid diagrams with comprehensive grammar parsing supporting 28+ diagram types. Processes markdown files, ZIP archives, and direct input with detailed error reporting and enterprise-grade performance capabilities.

Enables MCP clients to inspect, edit, and validate Arma Reforger addons in Workbench, including Enforce Script source, prefab/world files, resource GUIDs, and Workbench startup diagnostics.

Exposes a set of CLI tools (test generation, documentation generation, linting, test running, code search) to AI assistants via MCP, allowing them to perform these tasks through natural language.

An AST-based MCP server that provides token-efficient codebase skeletons to LLM agents, reducing context token usage by 80-95% by exposing structural information instead of full source files.

Gives an AI agent complete knowledge of a specific DevExpress XAF application: its entities, controllers, actions, business rules, Model Editor customizations, custom property editors and the version-gated data migrations that ran once on a production database. Reads the source with Roslyn without compiling it, so it works on a branch that does not build and needs no DevExpress licence.

Enables AI models to run Stylelint on CSS files and fix linting warnings directly through the Model Context Protocol.

MCP server that provides npm package information for AI agents during TypeScript development.

Provides live Apple documentation fetching, Xcode build error analysis, and Swift Evolution proposal checking for Apple development assistance.

A robust, language-agnostic Model Context Protocol (MCP) server that provides AI coding agents with the ability to edit files surgically via Abstract Syntax Trees (AST) instead of relying on token-heavy, brittle search-and-replace or diff operations.