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tecton-ai

Tecton MCP Server

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by tecton-ai

query_documentation_index_tool

Find Tecton documentation snippets for your query, including source URLs, section headers, and relevant text to resolve your question.

Instructions

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."

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool retrieves and formats documentation snippets, enumerates the exact output fields, and instructs the agent to tell the user which documentation URL to open. It does not cover edge cases such as no matches found, but the core behavior is clear and accurate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description front-loads the core behavior and output format before moving to user-facing instructions and examples. The list of examples is long but earns its place by serving as parameter guidance; there is no filler or redundant wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema and annotations, the description covers what the tool returns, how the agent should present the result, and how to phrase queries. The main gap is the lack of explicit guidance for choosing between this tool and its siblings, which is already reflected in the usage guidelines score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides no description for the 'query' parameter, so the description must compensate. It does so through a detailed 'Input query examples' section that illustrates the expected natural-language phrasing. For a single parameter, this gives an agent sufficient understanding of what to pass, even though explicit constraints like length or format are not stated.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs ('Retrieves and formats') and clearly names the resource ('Tecton documentation snippets'). It also describes exactly what each snippet contains (Source URL, section header, relevant text chunk), which makes the tool's function unambiguous and distinguishable from sibling code-example and SDK-reference tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides ten concrete input query examples that establish when the tool is appropriate, such as 'How do I unit test a Feature View?' and 'Explain Batch Feature Views.' It gives clear context on the kind of natural-language documentation questions to use, but it does not explicitly name alternatives or state when not to use this tool versus its siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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