"Reading Local Project Files to Assist Development" matching MCP servers:

A mobile-first AI co-reading MCP server that lets users import novels or comics, sync reading progress with AI, and interact through comments, bookmarks, and notes.

Enables shared reading between humans and AI assistants, including book import, chunked reading, margin annotations, reading progress tracking, and note exchange.

An AI reading companion that curates next books based on the Chicago Plan curriculum and tracks user reading progress with personal tokens. It offers book recommendations, logging, coaching questions, and reading reports.

A local MCP server for low-token co-reading: import a book, split it into chapter-sized chunks, read it chunk by chunk, and keep notes so a session can resume without re-reading the whole book.

Enables AI assistants to discover, retrieve, and compare official international development indicators from sources such as the World Bank, FAOSTAT, WHO, UNICEF, and IMF, while preserving source identifiers, units, and citations.

A local MCP server that gives Claude a durable reading room for EPUB and plain text books, enabling chunk-by-chunk navigation, annotations, progress tracking, and shared margin cards.

Enables a shared reading room where users and Claude can import EPUB or text books, read chunk-by-chunk, annotate passages, search, track progress, and resume from unread sections.

Enables read-only listing and reading of files in an Azure Files SMB share, enforcing each user's NTFS permissions per request.

Provides tools for managing project knowledge graphs, enabling structured representation of projects, tasks, milestones, resources, and team members.

A Model Context Protocol server that enables seamless integration between Claude AI and development tools like VSCode, Augment, Vercel, Airtable, and Square.

Enables cloud agents to securely operate local machine resources (files, commands, screenshots) via standard MCP protocol.

An MCP server that integrates Google Gemini CLI with Claude Code for AI-powered development assistance, enabling code review, bug analysis, feature planning, and code explanation without requiring an API key.

Enables MCP clients to execute commands, edit files, run Git workflows, and perform validated independent reviews in real development environments through Docker-based runtimes with separated developer and auditor permissions.

一个基于FastMCP协议的文件向量化服务，支持自动目录监控、多格式文件处理和高效向量搜索，帮助构建个人或团队知识库。

Enables submitting audio via URL or Base64 for local Whisper transcription, then checking job status and results or deleting transcription jobs. It provides asynchronous job handling and SSRF-protected downloads/callbacks without cloud API calls.

Provides a testable gateway path for running a local MCP-style server in hosted-like conditions, with tenant-isolated reads, approval-denied writes, deterministic failures, and smoke-test verification.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Enables task management (create, list, update tasks with priority and status) using SQLite storage via MCP tools.

Enables agents to quickly find and edit code in a codebase with surgical precision. Find symbols, edit them everywhere with tools for reading code blocks, searching/replacing text, and making precise line-based modifications.