$ loading_
Inspect and manage a local Feast feature store with MCP tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp_feast" yet — see the docs or source repo.
List the feature views and feature services in the local Feast store, and briefly explain their purpose.
A list of feature views and services with brief explanations.
Resolve available features for the given entities and check whether they are fresh enough for downstream use.
Resolved features for the entities and freshness results.
Explain the feature values for an entity, including where each value comes from and what it means.
An explanation of feature value sources and meanings.
An ML engineer can inspect available features, resolve entities, and verify freshness before training or inference. The explain capability also helps interpret specific feature values.
Developers can quickly inspect feature views and feature services in the local Feast store to confirm what is exposed. It is useful for integration, troubleshooting, and understanding the feature store structure.
Data analysts or researchers can check whether feature values are fresh enough for experiments or downstream workflows. If needed, they can also trigger materialization to refresh features.
This is an MCP tool for working with a local Feast feature store. It can list feature views and feature services, resolve entities, check freshness, and explain feature values.
According to the description, it supports listing feature views and services, resolving entities, checking freshness, explaining feature values, and optionally pushing swipes or triggering materialization.
You need access to a local Feast feature store. Other setup, runtime, or integration requirements are not provided here; see the source repository.
Access Feishu docs, sheets, wiki, and IM through standardized MCP tools.
Extract image color, texture, and shape features for precise comparison and quality assessment.
Read Feishu docs and wiki pages with content and metadata via MCP.
Mount an MCP server to FastAPI for runtime route and schema inspection.
Manage Featuredeck feature requests and roadmaps with natural-language read and write actions.
Manage Tecton clusters, feature stores, and CLI tasks with natural language.