Jupyter MCP Server

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

Annotations provide readOnlyHint=true, indicating it's a safe read operation. The description adds valuable context beyond this: it specifies that outputs are included only for code cells (not for markdown or other types) and that metadata like index, type, and execution count are returned. This clarifies behavioral traits not covered by annotations.

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 a single, well-structured sentence that efficiently conveys the tool's purpose, scope, and return values. It is front-loaded with the core action and resource, with no wasted words, making it highly concise and effective.

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, rich annotations (readOnlyHint), 100% schema coverage, and the presence of an output schema, the description is complete enough. It covers the key aspects: what it does, what it returns, and context-specific details (e.g., code cell outputs), without needing to explain parameters or safety, which are handled by structured data.

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 100%, with clear descriptions for both parameters. The description adds minimal semantic value beyond the schema: it implies 'cell_index' targets a specific cell and 'include_outputs' applies to code cells, but these are already covered in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 specific action ('Read a specific cell'), the resource ('from the currently activated notebook'), and the return value ('metadata, source and outputs'). It distinguishes from siblings like 'read_notebook' (which reads entire notebooks) and 'list_notebooks' (which lists files).

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 by specifying 'currently activated notebook' and 'for code cells', suggesting it requires a notebook to be in use and is cell-type aware. However, it does not explicitly state when to use this tool versus alternatives like 'read_notebook' or 'overwrite_cell_source', nor does it mention prerequisites or exclusions.

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