Check active IPython sessions and their health: see state, kernel PID, Python version, working directory, uptime, and execution count. Identify dead sessions that remain until cleared.
List every session with its state (idle, busy, or dead), kernel pid, Python version, working directory, uptime, and execution count. A dead session keeps its name until ipython_stop clears it.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the core behavior: it lists session details and includes the notable trait that dead sessions retain their name until ipython_stop clears them. This adds behavioral context beyond a generic 'list' and helps the agent understand the session lifecycle. It does not mention side effects, but for a read-only status tool, this is reasonably transparent.
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 two sentences with no filler. The primary action and output fields are front-loaded in the first sentence, and the second sentence adds a crucial nuance about dead sessions. Every word earns its place, and the structure is clean and scannable.
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 no output schema, the description adequately specifies the return content by listing all fields (state, kernel pid, Python version, working directory, uptime, execution count) and the edge case for dead sessions. There are no parameters to document, and the behavior is fully described. An agent has enough to call the tool and interpret results correctly.
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?
The tool has zero parameters, and the schema coverage is 100% (empty schema). The description does not need to explain parameter semantics. The baseline for 0 params is 4, and there is no missing information here.
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 uses a specific verb 'List' with a clear resource 'every session' and enumerates the exact fields (state, kernel pid, Python version, working directory, uptime, execution count). It also clarifies the behavior for dead sessions, distinguishing it from siblings like ipython_start or ipython_run. This leaves no ambiguity about what the tool does.
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 (to inspect sessions) but does not explicitly state when to use this tool versus alternatives, such as 'use ipython_start to create a session' or 'use ipython_stop to clear dead sessions.' There is no exclusionary guidance, so it relies on the agent to infer context. This meets the 'implied usage' level but lacks explicit routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.