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Microsoft Fabric MCP Server

Microsoft Fabric MCP Server

by Jasuni69
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

sql_explain

Estimate a SQL query's execution plan to analyze performance and identify optimization opportunities before running it.

Instructions

Retrieve an estimated execution plan for a query.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "sql_explain"
  }
}

Input Schema

NameRequiredDescriptionDefault
typeNo
queryYes
lakehouseNo
warehouseNo
workspaceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It communicates that the operation is read-only in nature ('Retrieve') and that the result is an estimated plan, suggesting the query is not actually executed. However, it does not state whether execution occurs, what the plan output looks like, or any permission requirements.

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 focused sentence with no filler. It front-loads the core action and result, making it easy for an agent to parse quickly.

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?

This is a 5-parameter tool with no output schema and no annotations, yet the description only explains the query input. The optional context parameters and the structure of the returned execution plan are unexplained, so the description is not complete enough for reliable invocation.

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

Parameters2/5

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

Schema description coverage is 0%, so the description needed to compensate by explaining the optional parameters. It only reinforces that 'query' is the main input; the purpose and effect of type, lakehouse, warehouse, and workspace remain entirely undocumented.

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 names a specific verb ('Retrieve'), a specific resource ('estimated execution plan'), and a clear object ('a query'). It distinguishes sql_explain from sibling tools like sql_query and sql_export by indicating this tool returns a plan rather than executing a query or exporting results.

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?

There is no guidance on when to use this tool versus alternatives such as sql_query or analyze_dax_query. No conditions, exclusions, or preferred scenarios are provided, leaving the agent to infer when an estimated execution plan is wanted.

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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