"Web Page Content" matching MCP servers:

Provides code context and analysis for AI assistants by extracting directory structures and code symbols using WebAssembly Tree-sitter parsers with zero native dependencies.

Indexes codebases into a SQLite database to provide metadata, exports, dependency graphs, and change tracking for JS/TS projects. It enables users to search for symbols, map internal dependencies, and monitor file changes through MCP tools and a web dashboard.

MCP server for the Tiangong page designer, providing code diagnostics, configuration recommendations, and knowledge search via GitLab API.

Provides comprehensive code analysis (quality, complexity, linting, dependencies, security) and web scraping capabilities (static/dynamic content, API discovery) for development workflows.

Enables any MCP client to detect Claude Code configuration that is silently ignored, such as agent files that fail to load, unknown frontmatter fields, parent-folder settings, conflicting MCP server entries and drift since a baseline. Exposes read-only tools that return findings with exact edits, the fix prompt, and explanations of each check, without modifying any files.

Static codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.

Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.

Treats software units as content-addressed contracts, enabling efficient agent regeneration loops with cached verification and tiny context packets.

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

MCP server for fast local code & content search powered by Hawkeye. Enables AI assistants to search indexed codebases & assets with natural language queries.

Enables AI coding agents to retrieve compact structural codebase context through repository architecture summaries, AST symbol extraction, cyclomatic complexity scoring, and TODO/FIXME hotspot search.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

Enables Codex to gather bounded, read-only source evidence through literal search, optional file inventory, and exact line-range reads instead of automatically receiving entire files.

MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.

Provides VS Code active editor context (open file, cursor position, selection) to MCP-compatible AI tools.

Enables AI coding assistants to automatically scan, store, and query API endpoints from codebases, providing instant lookup and semantic search to reduce context switching and token consumption.

Exposes MCP tools that give desktop agents durable two-tier memory with recall and graph traversal, project code intelligence (symbol lookup, impact walking, notes search), and web search with page-to-Markdown conversion. It also runs a local model gateway that routes Anthropic- and OpenAI-shaped traffic across providers with usage accounting.

Enables AI coding agents to retrieve budgeted, AST-aware edit context and run refactor-safety checks over a local Tree-sitter code knowledge graph, reducing token use by avoiding full-file reads.

Enables AI coding assistants to retrieve a lean, optimized codebase context without lockfile and build bloat, and to analyze which files consume the most tokens.

MCP server for semantic code search and dependency graph analysis. Indexes codebases into a knowledge graph with vector embeddings for AI-powered code understanding.

Enables comprehensive project analysis, intelligent search with regex, multi-file editing with automatic backups, and dependency mapping, all via natural language.

A local code-intelligence engine for AI agents that indexes repositories into a PostgreSQL-backed code graph and serves structured, token-budgeted context over MCP and HTTP, enabling targeted queries on symbols, dependencies, contracts, and impact analysis.

Enables AI to read and modify STARLIMS code, forms, and resources, assisting in page development and configuration through MCP interfaces.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

Enables semantic code search across indexed codebases using natural language queries, with support for CLI and MCP interfaces.

Enables LLMs to progressively explore a project's dependency graph and retrieve only relevant code through a three-layer MCP workflow, with AST skeletons, Prisma schema extraction, token budgeting, and secret redaction.

An MCP server that fixes, validates, and generates visual text content for AI coding assistants.

Enables AI coding agents to automatically compile the most relevant slice of a repository into context for a given task.

Make any LLM a codebase expert instantly. Provides deep code intelligence through semantic search, architecture mapping, security analysis, and smart context that fits perfectly in token windows.

Provides AI agents with secure, read-only file system access to analyze and understand project codebases, enabling multi-repository context aggregation and cross-project code tracing.