Install Python packages in a Google Colab or Jupyter runtime using pip. Specify package name and optional session ID to resolve missing dependencies for your code execution.
Install a Python package (e.g. 'transformers', 'torch==2.3.0') inside the runtime via pip.
| Name | Required | Description | Default |
|---|---|---|---|
| package | Yes | ||
| session_id | No |
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 behavioral disclosure burden. It adds meaningful context by specifying the mechanism ('via pip') and the target environment ('inside the runtime'), but it does not disclose side effects such as dependency installation, network use, or whether an active session is required.
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 one concise, front-loaded sentence with an illustrative example. Every word earns its place, and there is no redundant filler.
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?
For a simple install action, the definition is mostly adequate: it names the required parameter format and the installation mechanism. However, with no annotations, no output schema, and no explanation of 'session_id', the description leaves modest gaps for an agent trying to invoke the tool correctly in all contexts.
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 0%, so the description must compensate. It does add value for the 'package' parameter with concrete examples like 'transformers' and 'torch==2.3.0', but it omits any semantics for 'session_id', leaving that parameter 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?
The description uses a specific verb ('Install') with a clear resource ('a Python package') and scope ('inside the runtime via pip'). It cleanly differentiates the tool from siblings like colab_uninstall_package and colab_list_packages.
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 when the tool should be used—when a package needs to be installed into the runtime—but does not explicitly address alternatives or exclusions. There is no mention of when to prefer another sibling tool, such as colab_uninstall_package or colab_get_package_version.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.