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save_topic_explanation

Generate and save detailed explanations for software-related queries using official documentation, powered by Vertex AI Gemini models. Input topic, query, and output path for results.

Instructions

Provides a detailed explanation for a query about a specific software topic using official documentation found via web search and saves the result to a file. Uses the configured Vertex AI model (gemini-2.5-pro-exp-03-25). Requires 'topic', 'query', and 'output_path'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
output_pathYesThe relative path where the generated explanation should be saved (e.g., 'explanations/react-router-hooks.md').
queryYesThe specific question to answer based on the documentation.
topicYesThe software/library/framework topic (e.g., 'React Router', 'Python requests').
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'uses the configured Vertex AI model (gemini-2.5-pro-exp-03-25)' which adds useful context about the implementation. However, it doesn't describe important behavioral aspects like whether this is a read-only or write operation (though 'saves' implies writing), what happens if the file already exists, error conditions, rate limits, or authentication needs. The description provides some behavioral context but leaves significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise at two sentences. The first sentence clearly states the core functionality, and the second sentence specifies requirements. There's minimal wasted text, though it could be slightly more structured (e.g., separating functionality from requirements more clearly). Every sentence earns its place by adding value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (web search + AI explanation + file saving) with no annotations and no output schema, the description provides basic functionality but lacks important context. It doesn't describe the output format, error handling, or how the web search and AI model interact. For a tool that performs multiple operations (search, generate, save), more comprehensive behavioral description would be helpful, though the core purpose is adequately stated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents all three parameters. The description mentions the three required parameters by name but doesn't add meaningful semantic context beyond what's in the schema descriptions. It doesn't explain relationships between parameters or provide additional usage examples. The baseline of 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Provides a detailed explanation for a query about a specific software topic using official documentation found via web search and saves the result to a file.' It includes specific verbs (provides explanation, saves to file) and resources (software topic, official documentation). However, it doesn't explicitly differentiate from similar siblings like 'explain_topic_with_docs' or 'save_answer_query_websearch' beyond mentioning the saving aspect.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal usage guidance. It states 'Requires topic, query, and output_path' which indicates parameter requirements but doesn't explain when to use this tool versus alternatives like 'explain_topic_with_docs' (which may not save) or 'save_answer_query_websearch' (which may not focus on official documentation). No explicit when/when-not guidance or comparison to sibling tools is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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