mcp-fabric-api

graphql_api_execute_query

Execute GraphQL queries against Microsoft Fabric GraphQL API endpoints using workspace ID, API ID, query, and optional variables.

Instructions

Execute a GraphQL query against a GraphQL API endpoint. Uses the Power BI scope token.

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": {
      "workspaceId": "<workspaceId>",
      "graphqlApiId": "<graphqlApiId>",
      "query": "<query>"
    },
    "name": "graphql_api_execute_query"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	The GraphQL query string
`variables`	No	GraphQL query variables
`workspaceId`	Yes	The workspace ID
`graphqlApiId`	Yes	The GraphQL API ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.8.0Sep 6, 2026

TDQS

B3.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the risk profile is partially covered. The description adds the Power BI scope token detail, which is useful auth context, but it does not disclose that an arbitrary GraphQL query may cause side effects, nor does it mention error behavior or output shape.

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?

Two short sentences with no filler. The action and target are front-loaded, and the Power BI scope token detail earns its place as useful context.

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

Completeness3/5

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

For an execution tool with no output schema, the description is thin: it does not explain what the response will contain, how errors are surfaced, or how to construct a valid GraphQL query beyond the schema fields. The required identifiers are covered by the schema, but the missing output/error context leaves an agent somewhat uncertain about the call result.

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?

All four parameters are already described in the schema (100% coverage), so the description does not need to repeat them. It adds no extra parameter-level meaning, such as how query and variables interact or whether variables are optional.

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 action ('Execute') and resource ('GraphQL query against a GraphQL API endpoint'), making it clear this is the query-execution counterpart to graphql_api_* management tools and distinguishable from sql_endpoint_execute_query. It does not explicitly name sibling alternatives, 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?

There is no explicit when-to-use guidance or comparison with alternatives such as sql_endpoint_execute_query, eventhouse_execute_kql, or semantic_model_execute_dax. The phrase 'Uses the Power BI scope token' hints at a specific auth context but does not state when this tool should be selected over others.

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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