ConductScience catalog

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

Annotations already declare the safe read-only, idempotent profile, yet the description still adds real behavioral facts: unknown/unpublished IDs return a null product, price is only present when published, and the study-count sentence appears only for named equipment. These conditional outcomes are exactly the kind of context annotations cannot express.

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?

Front-loaded with the return scope, then conditions, then the null case in one dense paragraph with no filler. It is lengthy but nearly every clause carries information; only the price_type cross-reference to search_products is arguably redundant.

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?

Since an output schema exists, the field enumeration is partly redundant, but the description contributes the conditional logic (price only when published, null for unknown IDs) that the schema alone would not convey. A brief note on what a null means for the caller would make it complete.

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% for the single parameter, and the schema already says "Numeric product ID from search_products", which the description essentially repeats. Baseline 3 applies since no additional format or constraint detail is added 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?

States a specific verb and resource ("Returns the full public details of one product in the ConductScience catalog") and immediately ties the lookup to "the numeric ID from search_products". The scope (single product, by ID) is unambiguous versus the sibling search tool.

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 tells the agent exactly where the required ID comes from ("from search_products"), which is the key routing information for a detail-lookup tool. It does not say when *not* to use it (e.g., batch lookups) or what to do when a null is returned, which keeps it below 5.

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

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

Annotations already cover read-only, idempotent and open-world behavior, so the bar is lower; the description still adds real context by documenting the shape of results, the price_type values including request_quote and not_applicable, the appended study-count sentence, and the empty-list outcome. It does not cover rate limits or pagination ceiling behavior, which remain undocumented in prose.

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?

Content is front-loaded with the purpose, then the return payload, then edge cases, in a logical order with no filler. It is dense but each sentence carries information; the result-shape sentence is long and overlaps with the existing output schema, which is the only redundancy.

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 search tool with a full output schema and complete annotations, the description supplies what structured fields cannot: price_type semantics, the conditional study-count addition, and the empty-result case. Pagination maximums are left to the schema, which is acceptable given full description coverage there.

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 100%, so the schema already documents all three parameters, including the page/limit bounds and defaults. The description reinforces what query accepts and adds one concrete example, but adds no syntax or format detail 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 opens with a specific verb and resource ("Searches the public ConductScience catalog of laboratory equipment and research software") and enumerates the query modes (name, SKU, keyword, short description). It is clearly distinct from get_product in substance, but it never names the sibling or states the search-vs-fetch distinction explicitly.

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?

Usage is implied rather than stated: an agent can infer this is the entry point for discovery, and the example query "rotarod for mice" illustrates acceptable input. There is no explicit when-to-use versus get_product guidance and no statement of when to prefer direct lookup over searching.

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