Jupyter MCP Server

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds valuable behavioral context beyond this by specifying what information is included (IDs, names, states, connection info, kernel specs) and the purpose (monitoring resources, identifying kernels for connection), enhancing transparency without contradicting 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 front-loaded with the core purpose in the first sentence, followed by additional details in two concise sentences. Every sentence adds value by elaborating on scope and usage, with no wasted words, making it highly efficient and well-structured.

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 low complexity (0 parameters, read-only), rich annotations (readOnlyHint), and the presence of an output schema, the description is complete. It covers purpose, scope, and usage context adequately without needing to explain return values or behavioral risks.

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?

With 0 parameters and 100% schema description coverage, the baseline is 4 as per the rules. The description does not need to add parameter details, and it appropriately focuses on the tool's function and output context without redundancy.

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 ('List all available kernels') and resource ('in the Jupyter server'), distinguishing it from sibling tools like list_files or list_notebooks. It explicitly mentions what is included in the listing (IDs, names, states, etc.), providing a comprehensive scope.

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 clear context for when to use this tool ('Useful for monitoring kernel resources and identifying specific kernels for connection'), but it does not explicitly state when not to use it or name alternatives. This gives good guidance but lacks explicit exclusions or comparisons to siblings.

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