MCP server exposing Flyte V2 SDK knowledge (symbols, patterns, examples) to AI assistants.
Project description
flyte-mcp
Flyte V2 knowledge, patterns, plugins, and runtime — exposed to every AI coding assistant via the Model Context Protocol.
Install in one click
Claude Code
claude mcp add flyte -- uvx flyte-mcp
Claude Desktop / any MCP client
{
"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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