flyte-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@flyte-mcpshow me how to cache a task with GPU resources"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
claude mcp add flyte -- uvx flyte-mcp{
"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 |
|
Find canonical examples |
|
Pick the right plugin |
|
Port V1 code to V2 |
|
Get oriented |
|
Run on a cluster |
|
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.jsonSources 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-sdkreleaseListing 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.
Resources
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