Plain-AyI

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety behavior is well covered. The description adds value by enumerating the fields returned, which partly stands in for the missing output schema, but it says nothing about failure behavior for an unknown slug or about response size/shape. With annotations carrying the safety profile, a 3 fits.

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?

A single front-loaded sentence with zero filler; the field enumeration is dense but every element tells the agent something about the payload.

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 one-parameter read tool with rich annotations and no output schema, the description covers what comes back well enough to call it correctly. It is only slightly short on error/not-found behavior.

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%; the schema already documents the slug, its pattern, its max length, and where to obtain it. The description adds no additional meaning about the parameter, so the baseline 3 applies.

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 names a specific verb-and-resource combination ('Full details of one project') and enumerates the returned fields (what it does, audience, coding requirement, rating, numbers, license, source links). It implicitly contrasts with search_projects/list_categories by emphasizing 'one project', but never names those siblings explicitly, so differentiation requires inference.

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 only implied: an agent can infer this tool is for retrieving a single known project, and the slug parameter's schema note points to search_projects or list_categories as the discovery path. However, the description itself gives no explicit when-to-use or when-not-to-use guidance.

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 declare readOnlyHint, idempotentHint, destructiveHint=false and closed-world scope, so the safety profile is covered. The description adds value by enumerating what comes back — short description, per-category project counts, and project slugs — which the agent cannot learn from annotations or the empty input schema.

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?

A single sentence that front-loads the scope and then lists the returned fields. Every clause carries information; nothing is 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?

With no output schema, the description has to convey the shape of the response, and it does so by naming the three returned elements. Minor gaps remain — ordering of categories, and whether the 'short description' can be null — but the essential contract is present for a simple no-arg read tool.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a no-param tool applies. The description correctly implies a full, unfiltered listing without inventing options.

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 ('All categories') and pins the scope to both shelves, 'ai' and 'ai-integrated', so an agent knows exactly what set is returned. It does not explicitly contrast with siblings, but get_project and search_projects operate on a different resource, so confusion is unlikely.

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 phrase 'All categories on both shelves' implies there is no filtering or querying option, which is useful context. However, there is no explicit when-to-use or when-not guidance, and no pointer to the sibling tools for project-level lookups.

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 establish read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower; the description still adds real context by defining search semantics (describe what the project does), the both-shelves default, and the ordering of results. It omits anything on pagination beyond the limit parameter or on what happens with zero matches.

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?

Four tight sentences, front-loaded with purpose and then defaults and narrowing options; nothing is padding. Minor overlap with the schema's enum text is the only waste.

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 no output schema, the description correctly takes on the return contract — "short summaries, best matches first" — and covers defaults, narrowing, and query style. An agent has everything needed to form a correct call.

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% and each parameter already carries a description, so the baseline is 3. The description adds the most value for `query` — signaling that natural-language descriptions of function work better than project names — but its treatment of `shelf` and `useWithoutCoding` largely restates the enum descriptions already in 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 — "Find open-source AI projects by what they do" — and immediately clarifies that the query is semantic/functional rather than name-based, with two concrete examples. This clearly separates it from get_project (fetch one) and list_categories (enumerate slugs) without needing the schemas.

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?

Explicitly explains the default behavior ("Searches both shelves unless 'shelf' is given") and the two optional narrowing axes (category, useWithoutCoding). It does not say when to prefer this over a sibling such as get_project for retrieving full details, so it stops short of full alternative routing.

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