Jupyter MCP Server

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

Annotations indicate readOnlyHint=true, which the description aligns with by describing a read operation. The description adds valuable context beyond annotations: it explains the two response formats (brief vs. detailed), their use cases, and a recommended pagination strategy. However, it doesn't mention rate limits or authentication needs, which could be relevant but aren't critical given the read-only nature.

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 and front-loaded with the core purpose. It uses three sentences efficiently: the first states what it does, the second explains format use cases, and the third gives a workflow recommendation. There's minimal redundancy, though the second sentence could be slightly more concise.

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), and the presence of an output schema, the description is complete. It covers purpose, usage guidelines, and behavioral context without needing to repeat schema details or explain return values. The guidance on formats and pagination addresses key user needs effectively.

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%, so the schema fully documents all parameters. The description adds some semantic context by explaining the practical implications of 'response_format' (e.g., 'brief' for overview, 'detailed' for debugging) and hints at how 'start_index' and 'limit' interact with formats. This provides marginal value beyond the schema, meeting the baseline for high coverage.

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: 'Read a notebook and return index, source content, type, execution count of each cell.' It specifies the verb ('read'), resource ('notebook'), and output details, distinguishing it from siblings like 'list_notebooks' (which lists notebooks) or 'read_cell' (which reads a single cell).

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 provides explicit guidance on when to use different formats: 'brief format to get a quick overview' for structure and locating cells, and 'detailed format for debugging and analysis.' It also recommends a workflow: 'use brief format with larger limit to get an overview, then use detailed format with exact index and limit for specific cells,' offering clear alternatives and context.

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