Installs a Python notebook environment with ipykernel, pandas, matplotlib, and singlestoredb using uv, preparing Jupyter kernels for SingleStore data analysis.
Create the notebook Python environment (ipykernel, pandas, matplotlib, singlestoredb) with uv.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.8.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false and destructiveHint=false, so the agent knows this is a non-destructive mutation. The description usefully adds the package list and the installer (uv), but says nothing about idempotency, overwrite behavior on an existing environment, or expected duration for a dependency install.
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?
A single front-loaded sentence with the action first and the package set in parentheses. No filler, nothing that could be trimmed without losing information.
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?
No output schema exists, so the description could reasonably explain the result of the install (success signal, whether packages are verifiable afterward). For a mutation tool with zero parameters, the description is adequate but leaves the post-install state and failure modes unspecified.
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 takes zero parameters, so there is no schema to compensate for; the baseline for 0 params is 4. The description's package list is informational rather than parameter guidance.
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?
States a concrete verb (Create) and resource (notebook Python environment) and enumerates the exact packages installed, so an agent knows what it produces. It does not explicitly distinguish itself from the nearby sibling notebook_packages_install, which is the main remaining ambiguity.
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?
Usage is implied: 'Create the environment' suggests running this when the notebook environment does not yet exist, versus notebook_packages_install for adding packages to an existing one. However, no alternative is named and no precondition (e.g., environment must not already exist) is stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.