"Information about Telnet Protocol or Usage" matching MCP servers:

An intelligent MCP server that acts as an AI tech lead for coding agents, providing expert validation, impact analysis, and strategic guidance before code changes are made.

A Python-based server for managing and analyzing code context for AI-assisted development. Provides real-time code analysis, project structure analysis, dependency tracking, and secure API endpoints.

Deterministic AI safety policy engine with Z3 formal verification. Write, verify, simulate, and enforce machine-verifiable safety constraints for AI agents. Completely outside the LLM.

Provides comprehensive codebase analysis and semantic understanding through integrated knowledge graphs, enabling AI assistants to understand project structure, patterns, dependencies, and context through multiple analysis tools and format generators.

Enables AI assistants to generate and statically verify IEC 61131-3 Structured Text logic, mutate .csp/.cpj OLE2 project containers, trigger in-GUI Error Check compilation, manage HMI screens, and run deterministic OCS scan-cycle simulations and Modbus telemetry scaling against Cscape 10.2. All physical hardware and firmware-download paths are intercepted fail-closed, so commissioning must be performed manually by authorized field personnel.

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

Enables a coding agent to get short, token-efficient answers about a project through grouped searches, focused symbol or section reads, and bounded read-only script output, with safety limits on paths, secrets, commands, and response size.

A local MCP server that indexes a repository into line-range chunks and serves compact evidence packets to cut token usage when exploring or debugging code.

truth is a deterministic fact-checker for the claims an AI coding agent makes about its own work.

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.

Semantic git queries via MCP. Beyond git log — answer who/what/why about any line, file, or branch with blame, co-change, PR linkage.

Parses Unity text-serialized .prefab, .unity, and .asset files to output only Inspector-visible data in hierarchical YAML, reducing token usage by up to 96%.

Provides access to Ant Design component library documentation and information for automated code generation. Enables searching components by category or keyword, retrieving component props, examples, API documentation, and available icons.

Rarefaction is a Model Context Protocol (MCP) server that gives a coding agent the call graph and code context for one symbol straight from the project's language server (Language Server Protocol, LSP). Ask about a function, method or type and get, in one call: its signature and doc, its body, who calls it (tests marked), what it calls, who implements it, the tests that reach it and the short list

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

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

Enables AI assistants to query and analyze Rell code via Language Server Protocol features like hover information, completions, diagnostics, and code actions. Automatically manages the Rell LSP server and provides resource-based access with real-time subscriptions.

MCP server that brings Octopus AI code review into editors, allowing review of code changes and asking questions about your codebase. Integrates with Cursor, Claude, or any MCP client via natural language.

Enables AI coding agents to reduce token usage by condensing source code into AST skeletons, extracting specific symbols, compressing logs, and estimating token budgets.