get_course_fces

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

No annotations are provided, so the description bears full responsibility. It discloses key behaviors: returns a compact summary with aggregates and 5 recent entries, details the effect of include_all (truncated flag, full history), and mentions auth requirements (env var SCOTTYLABS_AUTH_TOKEN). It does not mention rate limits or destructive effects, but those are not critical for a read tool.

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 well-structured and front-loaded with the main purpose. It includes all necessary sections (usage, auth, parameters) without excessive verbosity. However, it could be slightly shorter by condensing the auth note or combining some sentences. Overall, it is efficient and clear.

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?

Given the tool has 2 parameters and an output schema (FCESummary), the description covers behavior (aggregates vs. entries), parameters, auth, and return structure adequately. It explains truncation and full history. It does not explicitly list all output fields, but they are partially described in the 'Returns' section and the output schema exists. The description is sufficiently complete for an agent.

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%, but the description provides thorough semantics: course_id is explained with an example ('15-122'), include_all is detailed with its default, behavior (populates entries vs. full dataset), and the 'truncated' flag. This adds significant meaning beyond the schema's bare type and title.

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 explicitly states 'Fetch Faculty Course Evaluations (FCE) ratings for a course,' specifying the verb (fetch), resource (course evaluations), and scope (for a course). It distinguishes from sibling tools like get_instructor_fces, making the purpose clear and unambiguous.

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 provides direct usage guidance: 'Use this when the user asks about course difficulty, hours per week, or instructor ratings.' It also specifies when to set include_all=True ('only when the user asks for the full history'). While it does not explicitly list alternatives or when-not-to-use, the context of sibling tools implies differentiation, and the guidance is clear.

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