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"text": "You and I are talking about two different “MCPs.”\n\nYour earlier question was likely about **Model Context Protocol for LLM tools** (from Anthropic & others), but my previous answer described something else. I’ll correct that here and then answer what you asked: popular **MCP servers** in the LLM/tools ecosystem.\n\n### What is MCP (in this context)?\n\nMCP (Model Context Protocol) is an open protocol that lets LLM clients (like IDE extensions, chat UIs, etc.) talk to **servers** that provide tools, data, or capabilities (APIs, databases, files, etc.) in a standardized way. It’s used heavily with Claude but is not Claude-specific.\n\nAn **MCP server** is just a process that implements MCP and exposes:\n- tools (functions),\n- resources (data you can read),\n- prompts/templates, etc.\n\n### Popular / common MCP servers\n\nHere are some notable MCP servers people commonly use or build on:\n\n#### 1. Filesystem / Local Files\n- **Local filesystem server** \n - Lets the model read and (optionally) write local project files.\n - Used for code editing, refactoring, doc reading, config inspection, etc.\n\n#### 2. Git / GitHub / GitLab\n- **Git server**\n - Exposes operations like listing branches, diffs, commit history, staged changes.\n- **GitHub / GitLab servers**\n - Work with issues, PRs/MRs, comments, repository contents, CI status, etc.\n\n#### 3. HTTP / Generic API access\n- **HTTP / REST server**\n - Lets the model call arbitrary HTTP APIs via a standardized tool (`GET`, `POST`, etc.).\n - Often combined with schema hints or OpenAPI to make calls safer.\n\n#### 4. Databases\n- **SQL database server**\n - Connects to Postgres, MySQL, SQLite, etc.\n - Exposes tools for running parameterized queries, inspecting schema, etc.\n- **Vector DB server**\n - Connects to things like Pinecone, Weaviate, Qdrant, or pgvector.\n - Used for semantic search / RAG over documents or code.\n\n#### 5. Search / Knowledge\n- **Web search servers**\n - Wrap search APIs (e.g., Google Custom Search, SerpAPI, Bing, etc.).\n- **Documentation / docs servers**\n - Index project or product documentation and expose it as searchable resources.\n\n#### 6. Cloud & DevOps\n- **AWS / GCP / Azure servers**\n - Interact with cloud resources (list instances, check logs, manage S3/GCS buckets, etc.).\n- **Kubernetes server**\n - Inspect pods, logs, deployments, events.\n- **CI/CD servers (GitHub Actions, GitLab CI, etc.)**\n - Check pipeline status, logs, artifacts.\n\n#### 7. Project & Task Management\n- **Jira / Linear / Asana servers**\n - Create/update issues, read tickets, search tasks.\n- **Notion / Confluence / Slack / Teams**\n - Read/write pages, fetch messages, summarize threads.\n\n#### 8. Code Intelligence\n- **Language server (LSP) bridges**\n - Hook into existing language servers to provide symbol info, references, diagnostics.\n- **Static analysis / testing servers**\n - Run linters, tests, formatters, security scanners (e.g., ESLint, pytest, Bandit).\n\n#### 9. Custom / App-specific servers\nMany teams implement their own MCP servers that wrap:\n- Internal business APIs\n- CRM/ERP systems\n- Analytics warehouses (Snowflake, BigQuery, Redshift)\n- Internal document search\n\nThese quickly become “popular” within one organization because they centralize internal capabilities in a consistent way.\n\n---\n\nIf you tell me your use case (e.g., coding, data work, operations, or product support), I can suggest a short, concrete list of MCP servers that would be most useful and how to compose them.",
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"text": "thank you can you please list some popular servers",
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