Skip to main content
Glama
Jasuni69

Microsoft Fabric MCP Server

by Jasuni69

get_notebook_content

Retrieve the JSON content of a specific notebook from a Microsoft Fabric workspace by providing the workspace name or ID and notebook ID.

Instructions

Get the content of a specific notebook in a Fabric workspace.

Args:
    workspace: Name or ID of the workspace
    notebook_id: ID or name of the notebook
    ctx: Context object containing client information
Returns:
    A string containing the notebook content in JSON format or an error message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceYes
notebook_idYes

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It communicates a read operation via 'Get' and discloses the return format as 'a string containing the notebook content in JSON format or an error message.' It does not mention permissions, side effects beyond reading, or rate limits, but this is a simple read tool.

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 is a compact docstring with an Args/Returns structure. Each line earns its place, and the key information is front-loaded in the first sentence. The only minor issue is including ctx, which is not part of the input schema.

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?

For a simple 2-parameter read tool with no output schema, the description is largely complete: it explains what the tool returns (JSON string or error message) and clarifies that both parameters accept names or IDs. It would benefit from a note that this is a read-only operation, but the verb 'Get' conveys that reasonably.

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 input schema only provides field names with no descriptions (0% coverage), so the description must compensate. It does: 'workspace: Name or ID of the workspace' and 'notebook_id: ID or name of the notebook' add meaningful detail about accepted value forms. The mention of ctx in Args is confusing because ctx is not in the input schema, but the actual callable params are well clarified.

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 states a specific verb and resource: 'Get the content of a specific notebook in a Fabric workspace.' It clearly distinguishes this from siblings like list_notebooks (listing only) and run_notebook_job (executing), even without saying so explicitly.

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 gives no guidance on when to use this tool versus alternatives such as list_notebooks, get_report, or run_notebook_job. There are no conditions, exclusions, or alternative tool names mentioned.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Jasuni69/ms-core-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server