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eventhouse_execute_kql

Run KQL queries against a Microsoft Fabric eventhouse KQL database to retrieve and analyze data. Specify workspace, eventhouse, database, and query to get results.

Instructions

Execute a KQL query against an eventhouse's KQL database

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesKQL query string to execute
maxRowsNoMaximum number of rows to return (default 1000)
databaseYesKQL database name to query
workspaceIdYesThe workspace ID
eventhouseIdYesThe eventhouse ID

Schema Changelog

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

  1. First observedv2.8.0

TDQS

C2.9/5.0
Behavior2/5

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

The annotations only indicate readOnlyHint=false and destructiveHint=false, which are not informative. The description does not disclose that KQL queries can potentially include management commands, that results may be large, or that this operation may not be purely read-only. It adds no behavioral context beyond 'execute a query'.

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

Conciseness5/5

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

The description is a single clear sentence with no filler or repetition. It is appropriately compact for a tool whose parameter details are already captured by the schema.

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

Completeness2/5

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

There is no output schema, so the description should indicate what the query returns or at least note that results are returned and capped by maxRows. It also doesn't clarify whether arbitrary KQL including mutating commands is allowed. For an execution tool, this leaves significant context missing.

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

Parameters3/5

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

Schema description coverage is 100%, and each parameter has a functional description. The tool description itself adds no extra parameter meaning, so the baseline of 3 applies.

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

Purpose4/5

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

The description states a specific verb ('Execute'), a specific resource type (KQL query against an eventhouse's KQL database), and is clearly distinct from siblings like sql_endpoint_execute_query and semantic_model_execute_dax. It does not mention what the query returns, so it falls short of a 5, but the core purpose is unambiguous.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives such as sql_endpoint_execute_query or kql_database_get. The description implies 'use this for KQL queries against eventhouse databases' but provides no explicit conditions, exclusions, or alternative routing.

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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