View Education

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

The annotations indicate a read-only, non-destructive operation. The description adds value by revealing that entries include IDs for later mutation and outlines common data quality issues (missing dates, field of study) without contradicting annotations.

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 somewhat verbose but each sentence contributes meaningful guidance. The primary purpose is stated upfront, and the additional instructions are well-organized.

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?

Despite the absence of an output schema, the description comprehensively explains what the tool returns, how to use the data, and what to look for. It fully equips the agent to handle the education history appropriately.

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?

With zero parameters, the input schema is fully covered. The description provides context about the returned data structure and actionable use of IDs, which adds meaning beyond the schema.

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 clearly states the tool retrieves the user's education history and specifies that each entry includes its ID for subsequent update/delete operations. It distinguishes itself from sibling tools like create_education and update_education.

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 advises when education history is most relevant (limited work history) and provides explicit instructions for flagging missing or incomplete entries. It suggests using create_education to fix issues, though it does not explicitly state when not to use this tool.

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