Install Python packages into a Colab runtime using uv, with automatic pip fallback. Provide package names or version specifiers to set up dependencies.
Install Python packages on the runtime (uses uv, falls back to pip). Names/specifiers only, e.g. 'qiskit', 'pandas>=2.0'.
| Name | Required | Description | Default |
|---|---|---|---|
| session | No | ||
| packages | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It usefully discloses the install mechanism ('uses uv, falls back to pip'), which is real behavioral context, but says nothing about side effects (e.g. kernel restart requirement), persistence, or failure behavior.
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?
Two tight sentences, front-loaded with the action and the tooling detail, then the input format constraint. Nothing is redundant or padded.
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?
An output schema exists, so return values need not be described. However, for a 2-param mutation-style tool with no annotations, the unexplained 'session' parameter and unstated side effects leave clear gaps an agent would need to know.
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 coverage is 0%, so the description must compensate. It does clarify the 'packages' argument format ('Names/specifiers only, e.g. qiskit, pandas>=2.0'), which is valuable, but the 'session' parameter is left entirely unexplained.
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 and resource ('Install Python packages on the runtime'), which is unambiguous. It is the only install-type tool among the siblings, so no explicit differentiation is required, but it never names a sibling or scope boundary to signal that.
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 only implied (install before running code); there is no explicit when-to-use, precondition, or alternative. It does not mention, for example, that a session must exist first or when to prefer this over running pip inside colab_exec.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.