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

Annotations already declare readOnly/idempotent/non-destructive/openWorld=false, so safety needs no restating. The description adds real value by describing the return payload (id, names, one-line summary, course link) and by implying fuzzy multi-field matching. It omits result-set size limits or pagination behavior.

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?

Two sentences: the first defines scope and filter modes, the second states the return shape, with the primary use case last. No filler, and the examples earn their space by pinning down match semantics.

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?

With a single fully documented parameter and no output schema, the description carries the return-value burden itself and does so explicitly. Nothing needed to call this correctly is missing.

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 coverage is 100%, so the baseline is 3. The description still adds meaning beyond 'Optional name or topic': it clarifies that matching covers brand names, code names and topics, and gives concrete example queries ('Ozempic', 'GLP-1', 'sleep'), which is exactly the disambiguation an agent needs.

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?

Specific verb 'List' plus the exact resource ('the peptides Peptides Academy covers'), and it explicitly enumerates the filter dimensions (name, brand name, code name, topic). An agent can distinguish it from compare_peptides and get_peptide_research without opening any schema.

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?

'Use it to resolve a peptide id before get_peptide_research' gives a concrete calling context and downstream hand-off. It does not state when not to use it or mention compare_peptides, so routing against the sibling list is left partly implicit.

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