flyte-mcp
Provides AI assistants with accurate Flyte V2 knowledge, patterns, and plugin information to help developers write correct Flyte code. It enables tasks like learning the V2 API, finding examples, selecting plugins, and migrating V1 code to V2.
README
<p align="center"> <img src="https://raw.githubusercontent.com/flyteorg/static-resources/main/common/flyte_circle_gradient_1_4x4.png" width="90" alt="Flyte" /> </p>
<h1 align="center">flyte-mcp</h1>
<p align="center"> <strong>Flyte V2 knowledge, patterns, plugins, and runtime — exposed to every AI coding assistant via the Model Context Protocol.</strong> </p>
<p align="center"> <a href="https://pypi.org/project/flyte-mcp/"><img src="https://img.shields.io/pypi/v/flyte-mcp?color=6f2aef&label=PyPI&logo=pypi&logoColor=white" alt="PyPI"/></a> <a href="https://pypi.org/project/flyte-mcp/"><img src="https://img.shields.io/pypi/pyversions/flyte-mcp?color=3776ab&logo=python&logoColor=white" alt="Python"/></a> <a href="https://modelcontextprotocol.io"><img src="https://img.shields.io/badge/MCP-compatible-7c3aed?logo=anthropic&logoColor=white" alt="MCP"/></a> <a href="https://github.com/andreahlert/flyte-mcp/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-blue" alt="License"/></a> <a href="https://github.com/andreahlert/flyte-mcp/stargazers"><img src="https://img.shields.io/github/stars/andreahlert/flyte-mcp?style=flat&color=f59e0b" alt="Stars"/></a> </p>
<p align="center"> <img src="assets/demo.svg?v=2" alt="Claude Code using flyte-mcp" width="820" /> </p>
<h2 align="center">Install in one click</h2>
<p align="center"> <a href="cursor://anysphere.cursor-deeplink/mcp/install?name=flyte&config=eyJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyJmbHl0ZS1tY3AiXX0"> <img src="https://img.shields.io/badge/Install_in-Cursor-000000?style=for-the-badge&logo=cursor&logoColor=white" alt="Install in Cursor"/> </a> <a href="vscode:mcp/install?%7B%22name%22%3A%22flyte%22%2C%22type%22%3A%22stdio%22%2C%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22flyte-mcp%22%5D%7D"> <img src="https://img.shields.io/badge/Install_in-VS_Code-007ACC?style=for-the-badge&logo=visualstudiocode&logoColor=white" alt="Install in VS Code"/> </a> <a href="vscode-insiders:mcp/install?%7B%22name%22%3A%22flyte%22%2C%22type%22%3A%22stdio%22%2C%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22flyte-mcp%22%5D%7D"> <img src="https://img.shields.io/badge/Install_in-VS_Code_Insiders-24bfa5?style=for-the-badge&logo=visualstudiocode&logoColor=white" alt="Install in VS Code Insiders"/> </a> </p>
<p align="center"> <strong>Claude Code</strong> </p>
claude mcp add flyte -- uvx flyte-mcp
<p align="center"> <strong>Claude Desktop / any MCP client</strong> </p>
{
"mcpServers": {
"flyte": {
"command": "uvx",
"args": ["flyte-mcp"]
}
}
}
Add this to ~/.claude.json, ~/.cursor/mcp.json, or your client's config file.
Why it exists
Ask any AI assistant "write a Flyte V2 task with caching and 4 GPUs" and you get confidently wrong code: V1 imports, invented decorators, hallucinated resource kwargs. The assistant has no reliable channel into the Flyte ecosystem, so it fills the gap with training-data guesses.
flyte-mcp is that channel. It ships a versioned knowledge pack built directly from the flyte-sdk source tree and the Flyte Plugin Registry, plus a thin runtime bridge for executing tasks when a cluster is configured. The assistant stops guessing and starts answering.
What your assistant can do
| Capability | Tools |
|---|---|
| Learn the V2 API | get_flyte_symbol · search_flyte_api · list_flyte_symbols |
| Find canonical examples | find_flyte_example_for · get_flyte_pattern · list_flyte_patterns |
| Pick the right plugin | suggest_flyte_plugin_for · list_flyte_plugins · get_flyte_plugin |
| Port V1 code to V2 | migrate_v1_to_v2 |
| Get oriented | get_flyte_overview · get_flyte_features · get_flyte_install_guide · get_flyte_version |
| Run on a cluster | run_flyte_task · get_flyte_execution_status · list_flyte_recent_runs |
All tools are pure Python, stdio transport, zero network calls unless you explicitly use the runtime bridge.
Example prompts that just work
- How do I cache a task and invalidate on input change?
- Show me a distributed PyTorch training example with A100s.
- Which Flyte plugin do I use for Snowflake, and what's the import?
- Migrate this flytekit V1 workflow to V2.
- What's the signature of
TaskEnvironment?
Your assistant picks the right tools and assembles accurate answers.
Rebuilding the knowledge pack
Contributors and release automation can regenerate the pack from source:
python scripts/build_knowledge.py \
--sdk-path /path/to/flyte-sdk \
--registry /path/to/flyte-plugin-registry/src/data/plugins.json \
--out src/flyte_mcp/data/flyte-v2-knowledge.json
Sources used:
flyte-sdk/src/flyte/__init__.py— public symbols via ASTflyte-sdk/examples/*— canonical patterns by themeflyte-sdk/README.md,FEATURES.md,CONTRIBUTING.md— meta docsflyte-plugin-registry— curated plugin catalog
flytesnacks is intentionally excluded: V2 consolidated examples in-tree.
Relationship to other Flyte MCP projects
- wherobots/flyte-mcp — runtime-only. Discovers and executes tasks on a deployed Flyte instance via API key. Complementary, not competing.
- unionai/claude-agents-public — Claude Code custom agents (system prompts, not an MCP server). Compose freely.
This project focuses on authoring: the moment a developer types a prompt asking about Flyte.
Roadmap
- GitHub Action to auto-rebuild the knowledge pack on every
flyte-sdkrelease - Listing in the official MCP Registry
- Local semantic search via small sentence-transformer model
- AST-based migration codemod (replacing the current regex pass)
- Log streaming tool (
get_flyte_execution_logs) with tail support
License
Apache-2.0 — same license as Flyte itself.
Disclaimer
Independent community project. Not officially affiliated with or endorsed by Flyte or Union.ai. The Flyte name and logo are trademarks of their respective owners.
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