Assess which features might be unavailable during prediction to detect leakage risks and ensure valid evaluation before training.
Optionally ask Gemini which features may be unavailable at the prediction moment.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| target | Yes | ||
| prediction_time | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure, but it only says the tool asks Gemini about availability. It does not state whether the operation has side effects, requires external connectivity, returns a report, or behaves differently depending on inputs, leaving the agent to guess.
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 sentence with no redundant clauses and the core action appears first. 'Optionally' is a minor filler, but overall the structure is appropriately compact.
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 tool with three required parameters, no output schema, and no annotations, this description is far too thin: it leaves parameter meanings, expected input formats, return behavior, and relationship to siblings unspecified. An agent cannot invoke it correctly from this text alone.
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% and the description does not explain path, target, or prediction_time. With three required parameters and no semantics beyond their names, an agent cannot confidently construct valid arguments.
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 names a specific action (asking Gemini) and subject (features that may be unavailable at prediction time), so an agent can roughly understand the tool's purpose. However, it does not define what 'features' refers to or differentiate it from the sibling dataset/audit tools, so it stops short of a 5.
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?
It offers no guidance on when to invoke this tool, what conditions warrant it, or why an agent might choose it over profile_dataset, audit_dataset, or audit_training_code. The word 'Optionally' hints it is not mandatory but does not provide a decision rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.