"The official LinkedIn platform or related resources" matching MCP servers:

Analyzes codebases for cross-platform compatibility issues, detecting platform-specific APIs, file path problems, line ending inconsistencies, and shell command incompatibilities across Windows, macOS, and Linux.

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.

Free deterministic security scan of a public git repository (GitHub, GitLab, Codeberg, Bitbucket): vulnerable dependencies via OSV.dev, secret patterns, and config lint, returned as structured JSON. Tools: scan_repository(url), audit_pricing(). Runs locally over stdio (python3 web/mcp_stdio.py or the Dockerfile) and is also hosted at https://project-feldspar.com/mcp. MIT licence, stdlib-only. Buil

Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.

Context compiler for AI coding agents that indexes TypeScript codebases to extract and serve only the relevant symbols and files for a task, reducing token usage and search overhead.

Transforms codebases into a living knowledge graph with AI-powered code analysis, security scanning, and persistent semantic memory, leveraging Oracle 26ai vector and property graph capabilities.

MCP server that enables AI agents to analyze legacy code, plan and execute software migrations, generate modern source code, and validate production-ready artifacts through a multi-agent pipeline.

A high-performance MCP server integrated with a Django analytics dashboard, featuring 19 tools for AI interaction tracking, AST code analysis, and real-time token efficiency metrics.

Gitbub mcp

Adds symbol-aware semantic search to coding agents like Codex CLI and Claude Code, enabling precise codebase context via natural language queries.

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.

Provides LLMs with code intelligence tools like relationship explanation, PR impact analysis, and health reports via the Model Context Protocol.

Local MCP server for .NET developers: lets Claude read real NuGet packages at your pinned version, explore your C# code, and edit and build with approvals and secret hiding - configurable in Admin UI. Windows, SQLite,

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.

Transforms local Git repositories into queryable, context-rich knowledge bases via AST-aware chunking and Git metadata, enabling AI assistants to search and understand codebases with semantic precision.

MCP server for AI-assisted development on the Lampa open-source TV app, providing read-only access to the source tree with 41 tools and 5 resources for structured analysis, planning, editing, and validation.

Code Intelligence Platform for AI Coding Assistants

Enables AI coding assistants to interact with OpenAI's Codex AI through the official CLI. Provides direct integration for code analysis, file review, and batch processing with zero API costs.

Enables token-efficient structural code exploration using tree-sitter AST parsing, with tools for symbol search, outlines, source unfolding, call graphs, and related-file discovery.

MCP server for the Symbols.app platform, enabling AI coding assistants to search docs, generate components, audit code, manage projects, and deploy via the smbls stack.

Automatically converts JoinQuant quantitative trading strategies to Ptrade platform format, supporting direct code input or file paths, with automatic API mapping and detailed conversion reports.

Show an AI coding assistant only the code that matters for a change — callers, callees, and related functions packed into a token budget, with self-grading retrieval.

Enables Codex to retrieve the currently selected file, line numbers, and code snippets from JetBrains IDEs. It leverages the official Claude Code plugin to provide real-time IDE context to the AI.

Enables developers and AI assistants to validate Anime.js v4 code with real-time AST diagnostics, search official documentation, retrieve parameter, migration, installation, and subpath details, and read associated MCP resources.

Enables coding agents to map a multi-repo workspace by resolving which repository a service name refers to, reading short per-repo cards, listing related repos with file-and-line evidence, finding where a table, package, path, or symbol lives across repositories, and refreshing the map. It answers orientation, localization, and cross-repo impact questions without the agent re-exploring every sibling repo.

Helping coding agents never make mistakes working with public or private libraries without wasting the context window.