ms-fabric-mcp-server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the read-only nature ('Retrieves') and output format ('dictionary matching the Jupyter notebook format'), but lacks details on error handling, permissions required, rate limits, or whether the operation is idempotent. The example adds some context but doesn't fully compensate for missing behavioral traits.

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

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

The description is well-structured with clear sections (purpose, parameters, returns, example) and uses bullet points effectively. It is appropriately sized but includes some redundancy (e.g., repeating 'notebook definition' concepts). Every sentence adds value, though minor trimming could improve conciseness.

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

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

Given the tool's moderate complexity, no annotations, and the presence of an output schema, the description provides sufficient context. It covers purpose, parameters, return structure, and includes a practical example. The output schema handles return value details, so the description doesn't need to explain those extensively, making it reasonably complete for agent use.

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

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 explicitly lists both parameters ('workspace_name', 'notebook_display_name') and provides an example with concrete values, adding meaningful context beyond the bare schema. However, it doesn't explain parameter constraints (e.g., format of 'notebook_display_name') or dependencies, leaving minor gaps.

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

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

The description clearly states the tool's purpose with specific verbs ('Get', 'Retrieves') and resources ('notebook content and definition', 'full notebook definition including all cells, metadata, and configuration'). It distinguishes from sibling tools like 'get_notebook_execution_details' or 'get_notebook_driver_logs' by focusing on content retrieval rather than execution status or logs.

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

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

The description implies usage context ('from a Fabric workspace') but does not explicitly state when to use this tool versus alternatives. No guidance is provided on prerequisites, exclusions, or comparisons with sibling tools like 'get_semantic_model_definition' or 'list_items', leaving the agent to infer appropriate usage scenarios.

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