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Microsoft Fabric MCP Server

Microsoft Fabric MCP Server

by Jasuni69
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

sql_query

Run a SQL query against a Microsoft Fabric lakehouse or warehouse to fetch or manipulate data.

Instructions

Run a SQL query against a lakehouse or warehouse endpoint.

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_query"
  }
}

Input Schema

NameRequiredDescriptionDefault
typeNo
queryYes
max_rowsNo
lakehouseNo
warehouseNo
workspaceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not state whether the SQL query is read-only, whether it can mutate data, what permissions are needed, what side effects may occur, or what the result format looks like.

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, front-loaded sentence with no wasted words. It efficiently communicates the core action and target resource.

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?

With six parameters, no annotations, no output schema, and minimal description, the tool definition leaves important gaps: parameter meaning, endpoint selection, side effects, limits, and expected result are all undocumented. This is inadequate for an agent to call the tool correctly in many realistic scenarios.

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 must compensate. It gives some meaning to the lakehouse and warehouse parameters by naming the endpoint types, but it leaves the type, max_rows, workspace, and endpoint-selection semantics unexplained.

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 and resource: 'Run a SQL query against a lakehouse or warehouse endpoint.' It clearly identifies what the tool does. However, it does not explicitly differentiate from similar siblings like sql_explain or table_preview, so it falls short of a 5.

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?

The description provides no guidance on when to use this tool versus alternatives such as sql_explain, sql_export, or table_preview. It does not mention exclusions, prerequisites, or when a different tool would be more appropriate.

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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