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Willis","email":"admin@ag-llama.com"},"license":"MIT","homepage":"https://agllama.onrender.com","keywords":["mcp","model-context-protocol","agllama","ag-llama","claude","anthropic","ai","project-management","agile","issue-tracking","sprint-planning"],"repository":{"type":"git","url":"git+https://github.com/WillisK-0/J-Killer.git","directory":"packages/mcp-server"},"description":"MCP server for Llama project management - connect Claude to your agile workflow","maintainers":[{"name":"williskeith04","email":"williskeith04@gmail.com"}],"readme":"# @agllama/mcp\n\n[![npm version](https://img.shields.io/npm/v/@agllama/mcp.svg)](https://www.npmjs.com/package/@agllama/mcp)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n[![Node.js Version](https://img.shields.io/node/v/@agllama/mcp.svg)](https://nodejs.org)\n\nConnect your AI client to [AG-Llama](https://agllama.onrender.com) project management using the Model Context Protocol (MCP). Manage issues, sprints, and boards directly from Claude Code, Claude Desktop, Codex CLI, Cursor, Gemini CLI, or VS Code/Copilot.\n\n> **Not affiliated with LlamaIndex, LlamaCloud, or Meta's Llama models.** \"AG-Llama\" is an independent agile project-management tool. Published and maintained by Keith Willis (`admin@ag-llama.com`); source at [github.com/WillisK-0/J-Killer](https://github.com/WillisK-0/J-Killer) under `packages/mcp-server`.\n>\n> **Supply-chain verification:** Releases are published from GitHub Actions with [npm provenance](https://docs.npmjs.com/generating-provenance-statements). Each version on npm shows a \"Built and signed on GitHub Actions\" attestation linking back to the exact commit and workflow that produced it — so a stolen npm token alone cannot publish a counterfeit. Verify locally with `npm audit signatures`. Full details, including maintainer-identity history and vulnerability reporting, are in [SECURITY.md](./SECURITY.md).\n\n## What is AG-Llama?\n\nAG-Llama is a modern, lightweight Jira alternative for agile teams. This MCP server lets your AI client interact with your projects, create issues, manage sprints, and automate workflows without leaving your conversation.\n\n## Installation\n\nNo installation required! Run directly with `npx`:\n\n```bash\nnpx @agllama/mcp\n```\n\n## Quick Start\n\n### 1. Get Your API Key\n\n1. Sign up at **[agllama.onrender.com](https://agllama.onrender.com)**\n2. Navigate to **Profile → MCP Integration**\n3. Click **\"Generate API key\"**\n4. Copy the key (shown only once)\n\n### 2. Configure your MCP client\n\nAG-Llama is MCP-native — pick your client below. Six clients work today: **Claude Code, Claude\nDesktop, Codex CLI, Cursor, Gemini CLI, and VS Code/Copilot.** Every client but VS Code uses an\n`mcpServers` JSON block; VS Code uses `servers` (no `mcpServers` fallback); Codex CLI uses TOML.\nThe Claude config stays byte-identical to past versions; the other sections are additive.\n\n> Config formats verified 2026-05-24:\n> - Cursor: https://cursor.com/docs/context/mcp\n> - Codex: https://developers.openai.com/codex/mcp\n\n#### For Claude Desktop App\n\nEdit your Claude Desktop config file:\n\n- **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`\n- **Windows**: `%APPDATA%\\Claude\\claude_desktop_config.json`\n- **Linux**: `~/.config/Claude/claude_desktop_config.json`\n\nAdd this configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"llama\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@agllama/mcp\"],\n      \"env\": {\n        \"LLAMA_API_URL\": \"https://api.ag-llama.com\",\n        \"LLAMA_API_KEY\": \"llm_xxxxxxxx\"\n      }\n    }\n  }\n}\n```\n\nReplace `llm_xxxxxxxx` with your actual API key.\n\n> **Windows users:** Replace `\"command\": \"npx\"` with `\"command\": \"cmd\"` and prepend `\"/c\", \"npx\"` to the `args` array:\n> ```json\n> \"command\": \"cmd\",\n> \"args\": [\"/c\", \"npx\", \"-y\", \"@agllama/mcp\"]\n> ```\n\n#### For Claude Code (CLI)\n\nIf you're using Claude Code, add to your project's `.mcp.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"llama\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@agllama/mcp\"],\n      \"env\": {\n        \"LLAMA_API_URL\": \"https://api.ag-llama.com\",\n        \"LLAMA_API_KEY\": \"llm_xxxxxxxx\"\n      }\n    }\n  }\n}\n```\n\nOr use the Claude CLI:\n\n```bash\nclaude mcp add llama -- npx -y @agllama/mcp\n```\n\nThen set environment variables:\n\n```bash\nexport LLAMA_API_URL=https://api.ag-llama.com\nexport LLAMA_API_KEY=llm_xxxxxxxx\n```\n\n#### For Codex CLI\n\nEdit `~/.codex/config.toml` (user-wide) or `.codex/config.toml` (trusted project):\n\n```toml\n[mcp_servers.llama]\ncommand = \"npx\"\nargs = [\"-y\", \"@agllama/mcp\"]\n\n[mcp_servers.llama.env]\nLLAMA_API_URL = \"https://api.ag-llama.com\"\nLLAMA_API_KEY = \"llm_xxxxxxxx\"\n```\n\n#### For Cursor\n\nEdit `~/.cursor/mcp.json` (user-wide) or `.cursor/mcp.json` (project):\n\n```json\n{\n  \"mcpServers\": {\n    \"llama\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@agllama/mcp\"],\n      \"env\": {\n        \"LLAMA_API_URL\": \"https://api.ag-llama.com\",\n        \"LLAMA_API_KEY\": \"llm_xxxxxxxx\"\n      }\n    }\n  }\n}\n```\n\n#### For Gemini CLI\n\nEdit `~/.gemini/settings.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"llama\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@agllama/mcp\"],\n      \"env\": {\n        \"LLAMA_API_URL\": \"https://api.ag-llama.com\",\n        \"LLAMA_API_KEY\": \"llm_xxxxxxxx\"\n      }\n    }\n  }\n}\n```\n\n#### For VS Code / GitHub Copilot\n\nEdit `.vscode/mcp.json` in your project (or run the Command Palette action **MCP: Open User\nConfiguration** for a user-wide config). VS Code's top-level key is **`servers`**, not\n`mcpServers` — there is no `mcpServers` fallback, so pasting a Claude-style config here fails\nsilently. Requires **VS Code 1.102 or later**, the release where MCP support went GA and the\n`servers` key became supported.\n\n```json\n{\n  \"servers\": {\n    \"llama\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@agllama/mcp\"],\n      \"env\": {\n        \"LLAMA_API_URL\": \"https://api.ag-llama.com\",\n        \"LLAMA_API_KEY\": \"llm_xxxxxxxx\"\n      }\n    }\n  }\n}\n```\n\n#### For any other MCP client (Generic)\n\nAny MCP client that supports stdio transport can connect with the raw command and env vars:\n\n```bash\nnpx -y @agllama/mcp\n\n# Environment:\nLLAMA_API_URL=https://api.ag-llama.com\nLLAMA_API_KEY=llm_xxxxxxxx\n```\n\n#### ChatGPT isn't supported yet\n\nChatGPT can only reach MCP servers over HTTPS from OpenAI's own servers — it can't spawn a local\n`npx` process the way the clients above do. Connecting it would require a hosted connector, which\nAG-Llama doesn't offer yet.\n\n### 3. Restart your client\n\nRestart Claude Desktop, Claude Code, or whichever client you configured to load the MCP server.\n\n### 4. Test the Connection\n\nIn your AI client, try:\n\n```\nUse llama to test the connection\n```\n\nYou should see your user info and available organizations.\n\n## Usage Examples\n\n### Get Project Overview\n\n```\nUse llama to get the context for my-team/PROJ\n```\n\nYour AI client will show you the active sprint, backlog, workflow statuses, and team members.\n\n### Create Issues\n\n```\nUse llama to create a high-priority story in my-team/PROJ:\n- Summary: \"Add user authentication\"\n- Description: \"Implement JWT-based auth with refresh tokens\"\n```\n\n### Manage Sprints\n\n```\nUse llama to:\n1. Show me the backlog for my-team/PROJ\n2. Create a new sprint called \"Sprint 5\"\n3. Add the top 3 highest-priority issues to it\n4. Start the sprint\n```\n\n### Search Issues\n\n```\nUse llama to find all critical bugs assigned to me in my-team\n```\n\n### Board Operations\n\n```\nUse llama to show me the board for my-team/PROJ\n```\n\n### Automate Workflows\n\n```\nUse llama to suggest a workflow for \"sprint planning\"\n```\n\nYour AI client will find and execute saved workflows from your organization.\n\n## Available Tools\n\nThe MCP server provides 40+ tools organized into categories:\n\n### Core Operations\n- **Context & Connection**: Test connection, get project snapshots\n- **Organizations**: List, create, and manage orgs\n- **Projects**: Create and configure projects\n- **Issues**: Full CRUD operations, status transitions, assignments\n- **Sprints**: Create, start, complete sprints, manage sprint backlog\n- **Boards**: View kanban boards, move issues between columns\n\n### Collaboration\n- **Comments**: Add, update, delete comments on issues\n- **Issue Links**: Create relationships (blocks, relates to, duplicates)\n- **Labels**: Create and manage issue labels\n- **Members**: List team members\n\n### Automation\n- **Search**: Find issues with filters\n- **Workflows**: Create and run reusable workflows\n- **Help**: Built-in documentation (`llama_help`)\n\nSee `MCP_TOOLS.md` for the full tool reference.\n\n## Environment Variables\n\n| Variable | Required | Description |\n|----------|----------|-------------|\n| `LLAMA_API_URL` | Yes | API endpoint (production: `https://api.ag-llama.com`) |\n| `LLAMA_API_KEY` | Yes | Your API key from [AG-Llama Profile → MCP Integration](https://agllama.onrender.com/profile) |\n\n## Self-Hosted Setup\n\nRunning your own AG-Llama instance? Configure the MCP server with your local or custom API URL:\n\n```json\n{\n  \"mcpServers\": {\n    \"llama\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@agllama/mcp\"],\n      \"env\": {\n        \"LLAMA_API_URL\": \"http://localhost:3001\",\n        \"LLAMA_API_KEY\": \"your-api-key-here\"\n      }\n    }\n  }\n}\n```\n\n## Troubleshooting\n\n### Windows: \"spawn npx ENOENT\" or tools not loading\n- On Windows, `npx` must be invoked through `cmd`. Use `\"command\": \"cmd\"` with `\"args\": [\"/c\", \"npx\", \"-y\", \"@agllama/mcp\"]`\n\n### \"Connection failed\" error\n- Verify your API key is correct\n- Check that `LLAMA_API_URL` is set to `https://api.ag-llama.com`\n- Ensure you've restarted your client after configuration changes\n\n### \"Invalid API key\" error\n- Generate a new API key at [agllama.onrender.com/profile](https://agllama.onrender.com/profile) → MCP Integration\n- Make sure the key starts with `llm_`\n\n### Tools not showing up\n- Confirm the MCP server is configured correctly in your client's config\n- Check your client's MCP server logs for errors\n- Try running `npx @agllama/mcp` directly to test\n\n### Rate limiting\nThe production API has rate limits. If you're hitting limits, consider:\n- Batching operations\n- Using project context (`llama_context`) to cache information\n- Self-hosting AG-Llama for unlimited access\n\n## Links\n\n- **Web App**: [agllama.onrender.com](https://agllama.onrender.com)\n- **API Documentation**: [api.ag-llama.com](https://api.ag-llama.com)\n- **Full Tool Reference**: `MCP_TOOLS.md`\n\n## License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n## Support\n\n- **Questions**: Use the `llama_help` tool in your AI client for built-in documentation\n\n---\n\nBuilt with the [Model Context Protocol](https://modelcontextprotocol.io) by Anthropic.\n","readmeFilename":"README.md"}