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Glama
tecton-ai

Tecton MCP Server

Official
by tecton-ai
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PYENV_VERSIONNoPython version to use (example value '3.9.11')3.9.11

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
query_example_code_snippet_index_toolA
Finds relevant Tecton code examples using a vector database.
It is always helpful to query the examples retriever before generating Tecton code.

Input query examples:
- "examples of an Entity"
- "examples of a KinesisConfig"
- "examples of a KafkaConfig"
- "examples of a batch feature view"
- "examples of a count distinct aggregation feature view"
- "examples of a percentile aggregation feature view"
- "examples of a stream feature view"
- "examples of an aggregation stream feature view"
- "examples of a realtime feature view"
- "examples of a realtime feature view that transforms data from another feature view"
- "examples of a fraud feature"
- "examples of a recsys case"
- "examples of a test"

The output will be a collection of python code examples that use Tecton to implement features, ranked by relevance.
query_documentation_index_toolA
Retrieves and formats Tecton documentation snippets based on a query.
Each snippet includes the TECTON DOCUMENTATION URL (Source URL), 
the section header, and the relevant text chunk.

Tell the user what documentation URL they can open up to get more information.

Input query examples:
- "How do I unit test a Feature View?"
- "What are Entities in Tecton?"
- "Explain Batch Feature Views."
- "How to connect to a Kafka data source?"
- "Show me how to construct training data."
- "Tutorial for building realtime features."
- "How does `tecton apply` work?"
- "Information about Tecton data types."
- "What is a Feature Service?"
- "Scaling the online feature server."
- "Monitoring materialization jobs."
get_full_tecton_sdk_reference_toolA

Fetches the full Tecton SDK reference. Use this only if you need to get the full SDK reference for all classes/functions. If you care only about a subset, use the query_tecton_sdk_reference_tool tool instead.

query_tecton_sdk_reference_toolA

Fetches the Tecton SDK reference for a specific list of classes/functions.

IMPORTANT: The class_names list MUST only contain names from the 'Available classes/functions' list below. Providing any names not in this list will result in an error or empty output.

Use this tool when you need information about specific Tecton components from the allowed list.

Output Format:

  • Starts with a bulleted list of the found public classes/functions matching the query.

  • Followed by details for each item, including:

    • Type (Class/Function)

    • Name

    • Recommended import path (e.g., tecton or tecton.types)

    • The definition header (e.g., class FeatureView(...) or def batch_feature_view(...))

    • The full docstring.

Available classes/functions: Aggregate, AggregationFunction, AggregationLeadingEdge, Array, Attribute, AutoscalingConfig, BatchFeatureView, BatchSource, BatchTriggerType, BigQueryConfig, BigtableConfig, CacheConfig, Calculation, ComputeMode, DataFrame, DataSource, DatabricksClusterConfig, DatabricksJsonClusterConfig, Dataset, DatetimePartitionColumn, DeltaConfig, DynamoConfig, EMRClusterConfig, EMRJsonClusterConfig, Embedding, Entity, FeatureServerGroup, FeatureService, FeatureTable, FeatureVector, FeatureView, Field, FileConfig, FilterContext, HiveConfig, IcebergConfig, KafkaConfig, KafkaOutputStream, KinesisConfig, KinesisOutputStream, LifetimeWindow, Map, MockContext, ModelConfig, OfflineStoreConfig, OnlineServingIndex, PandasBatchConfig, ParquetConfig, ProvisionedScalingConfig, PushConfig, PyArrowBatchConfig, RealtimeContext, RealtimeFeatureView, RedisConfig, RedshiftConfig, RequestSource, RiftBatchConfig, SdkDataType, Secret, SnowflakeConfig, SparkBatchConfig, SparkStreamConfig, StreamFeatureView, StreamProcessingMode, StreamSource, Struct, TectonDataFrame, TectonTimeConstant, TestRepo, TimeWindow, TimeWindowSeries, TransformServerGroup, Transformation, UnityCatalogAccessMode, UnityConfig, Workspace, approx_count_distinct, approx_percentile, batch_feature_view, const, first, first_distinct, last, last_distinct, materialization_context, pandas_batch_config, pyarrow_batch_config, realtime_feature_view, spark_batch_config, spark_stream_config, stream_feature_view, transformation

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources