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STDERR: GitHub MCP Server running on stdio
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STDOUT: {"jsonrpc":"2.0","id":0,"result":{"capabilities":{"completions":{},"logging":{},"prompts":{"listChanged":true},"resources":{"listChanged":true},"tools":{"listChanged":true}},"instructions":"The GitHub MCP Server provides tools to interact with GitHub platform.\n\nTool selection guidance:\n\t1. Use 'list_*' tools for broad, simple retrieval and pagination of all items of a type (e.g., all issues, all PRs, all branches) with basic filtering.\n\t2. Use 'search_*' tools for targeted queries with specific criteria, keywords, or complex filters (e.g., issues with certain text, PRs by author, code containing functions).\n\nContext management:\n\t1. Use pagination whenever possible with batches of 5-10 items.\n\t2. Use minimal_output parameter set to true if the full information is not needed to accomplish a task.\n\nTool usage guidance:\n\t1. For 'search_*' tools: Use separate 'sort' and 'order' parameters if available for sorting results - do not include 'sort:' syntax in query strings. Query strings should contain only search criteria (e.g., 'org:google language:python'), not sorting instructions. Always call 'get_me' first to understand current user permissions and context. ## Issues\n\nCheck 'list_issue_types' first for organizations to use proper issue types. Use 'search_issues' before creating new issues to avoid duplicates. Always set 'state_reason' when closing issues. ## Pull Requests\n\nPR review workflow: Always use 'pull_request_review_write' with method 'create' to create a pending review, then 'add_comment_to_pending_review' to add comments, and finally 'pull_request_review_write' with method 'submit_pending' to submit the review for complex reviews with line-specific comments.\n\nBefore creating a pull request, search for pull request templates in the repository. Template files are called pull_request_template.md or they're located in '.github/PULL_REQUEST_TEMPLATE' directory. Use the template content to structure the PR description and then call create_pull_request tool.","protocolVersion":"2025-03-26","serverInfo":{"name":"github-mcp-server","title":"GitHub MCP Server","version":"0.24.1"}}}
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STDIN: {"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"_meta":{"agent-session-id":"test-session-id","progressToken":0},"name":"get_file_contents","arguments":{"owner":"block","path":"README.md","repo":"goose","sha":"ab62b863c1666232a67048b6c4e10007a2a5b83c"}}}
STDOUT: {"jsonrpc":"2.0","id":1,"result":{"content":[{"type":"text","text":"successfully downloaded text file (SHA: de9bdde7f260549bf3a083651842f30ab29cf4e9)"},{"type":"resource","resource":{"uri":"repo://block/goose/sha/ab62b863c1666232a67048b6c4e10007a2a5b83c/contents/README.md","mimeType":"text/plain; charset=utf-8","text":"\u003cdiv align=\"center\"\u003e\n\n# goose\n\n_a local, extensible, open source AI agent that automates engineering tasks_\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://opensource.org/licenses/Apache-2.0\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/License-Apache_2.0-blue.svg\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://discord.gg/7GaTvbDwga\"\u003e\n    \u003cimg src=\"https://img.shields.io/discord/1287729918100246654?logo=discord\u0026logoColor=white\u0026label=Join+Us\u0026color=blueviolet\" alt=\"Discord\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/block/goose/actions/workflows/ci.yml\"\u003e\n     \u003cimg src=\"https://img.shields.io/github/actions/workflow/status/block/goose/ci.yml?branch=main\" alt=\"CI\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\u003c/div\u003e\n\ngoose is your on-machine AI agent, capable of automating complex development tasks from start to finish. More than just code suggestions, goose can build entire projects from scratch, write and execute code, debug failures, orchestrate workflows, and interact with external APIs - _autonomously_.\n\nWhether you're prototyping an idea, refining existing code, or managing intricate engineering pipelines, goose adapts to your workflow and executes tasks with precision.\n\nDesigned for maximum flexibility, goose works with any LLM and supports multi-model configuration to optimize performance and cost, seamlessly integrates with MCP servers, and is available as both a desktop app as well as CLI - making it the ultimate AI assistant for developers who want to move faster and focus on innovation.\n\n[![Watch the video](https://github.com/user-attachments/assets/ddc71240-3928-41b5-8210-626dfb28af7a)](https://youtu.be/D-DpDunrbpo)\n\n# Quick Links\n- [Quickstart](https://block.github.io/goose/docs/quickstart)\n- [Installation](https://block.github.io/goose/docs/getting-started/installation)\n- [Tutorials](https://block.github.io/goose/docs/category/tutorials)\n- [Documentation](https://block.github.io/goose/docs/category/getting-started)\n\n\n# a little goose humor 🦢\n\n\u003e Why did the developer choose goose as their AI agent?\n\u003e \n\u003e Because it always helps them \"migrate\" their code to production! 🚀\n\n# goose around with us\n- [Discord](https://discord.gg/block-opensource)\n- [YouTube](https://www.youtube.com/@goose-oss)\n- [LinkedIn](https://www.linkedin.com/company/goose-oss)\n- [Twitter/X](https://x.com/goose_oss)\n- [Bluesky](https://bsky.app/profile/opensource.block.xyz)\n- [Nostr](https://njump.me/opensource@block.xyz)\n"}}]}}
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