aicoolies

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

With no annotations provided, the description carries the behavioral disclosure burden. It satisfies this reasonably by explicitly mapping the operation to an HTTP GET, which signals a read-only retrieval with no mutation side effects, and by noting compact slug-only and include-list output modes beyond the raw 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?

The description is one economical sentence, front-loads the core behavior and endpoint, and then lists the optional modes. Every clause earns its place with no filler.

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 simple, zero-required-parameter read operation, the description covers purpose, endpoint, and output shape modes. The absence of an output schema means a slightly more explicit return-format description could help, but the catalog-index phrasing and compact mode give an agent enough context to call the tool correctly.

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. The description adds no new semantic detail beyond a brief summary of the optional flags; it doesn't compensate beyond the baseline.

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 identifies a specific verb ('Return') and resource ('filterable aicoolies catalog index'), and anchors it to the exact API endpoint ('GET /api/agents/context'). This makes it easy to tell apart from sibling tools like get_catalog_meta or search_tools, which address different operations.

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 'filterable catalog index' and the optional parameters imply this is the tool for retrieving a filtered catalog listing, but there is no explicit guidance about when to choose it over siblings such as search_tools or get_catalog_meta, and no exclusion criteria.

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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns counts and timestamps and implies a read-only GET operation via the endpoint equivalence, but it does not explicitly state side-effect freedom or any rate-limit/auth considerations. This is acceptable for a simple metadata read but not rich.

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 tight sentences with no filler. The core purpose and the usage hint are both front-loaded, and the endpoint equivalence is useful without bloating the description.

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 parameterless metadata tool, the description gives enough to select and invoke it correctly: return contents, endpoint, and recommended usage order. It omits exact response shape, but the absence of an output schema and the simple nature of the data make this a minor gap.

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 has zero parameters and the schema is fully covered by an empty object. With no parameters, the description cannot add parameter meaning, and the baseline of 4 is appropriate for a no-parameter tool.

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 catalog collection counts and freshness timestamps, with an explicit HTTP endpoint equivalent. It also distinguishes itself from sibling get_catalog_context by positioning itself as the lightweight metadata precursor.

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 advises using this tool before pulling the full context payload, giving clear sequencing guidance. It does not name alternative tools or exclusion conditions, but the usage context is clear enough for an agent to decide when this is appropriate.

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?

No annotations are provided, so the description carries the burden. 'Fetch' implies a read-only operation and 'public catalog' suggests no authentication, but the description does not mention behavior for unknown slugs, output format, or error handling. This is adequate but not rich.

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 a single focused sentence with no filler. The key identifying information is front-loaded: fetch, one tool, by slug, public catalog.

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 simple one-parameter retrieval tool, the description covers the essential context. There is no output schema, so a note about return format or not-found behavior would improve completeness, but the core operation is sufficiently described.

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%, with the single required parameter 'slug' already documented in the schema. The description adds little beyond repeating 'by slug', so a baseline score of 3 is appropriate.

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 states a specific verb ('Fetch'), a specific resource ('one aicoolies tool'), and the key discriminator ('by slug'). This clearly distinguishes it from sibling tools like search_tools and list_developer_resources.

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 gives clear context: use this when you know a tool's slug and want a single tool from the public catalog. It does not explicitly name alternatives or exclusions, but the slug-based single-item retrieval is an evident trigger condition.

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?

With no annotations, the description carries the burden of behavior disclosure; the verb 'List' implies a non-mutating retrieval, and 'No arguments' signals invocation constraints. It does not disclose return shape, whether links/URLs are returned, or any auth/rate-limit considerations.

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 short sentences front-load the action and resource, then concretely enumerate the included items and the no-argument requirement. Every sentence earns its place.

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 no-argument listing tool, the description is nearly complete: it names all resource categories and sets invocation expectations. It could be slightly stronger by stating what the response contains (e.g., a list of links), but the call itself is fully specified.

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?

There are zero parameters and the schema already documents an empty object; the description reinforces this with 'No arguments.' There is no semantic gap for the agent to resolve.

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 uses a specific verb and resource ('List official aicoolies developer resources') and enumerates concrete item types (OpenAPI, MCP, llms.txt, datasets, developer portal). It is clear but does not explicitly differentiate from sibling catalog tools such as get_tool or search_tools.

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 intended use is implied by the listing purpose and the explicit 'No arguments' statement, so an agent can infer when to call it. However, it gives no guidance about when to prefer this tool over the sibling get_* or search_* tools, and no exclusions or alternatives are mentioned.

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?

With no annotations provided, the description carries the behavioral disclosure burden. It states the operation is a search and that results are limited to public records, which is useful. However, it does not describe result shape, pagination behavior beyond the schema, or any other runtime characteristics.

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 is direct and front-loaded. Every clause contributes: the resource, the search keys, and the result scope. There is no filler or redundant restatement.

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 simple two-parameter search tool with fully documented parameters, the description is nearly complete. It identifies the search target, the matching criteria, and the visibility boundary ('public records'). The main gap is the absence of return-field detail, but the lack of an output schema makes that less critical here.

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 both parameters. The description's 'by name or slug' loosely aligns with the q parameter but adds no new meaning beyond the schema. 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 action ('Search'), a concrete resource ('aicoolies tool catalog'), and the search keys ('by name or slug'). It also clarifies the result type ('matching public records'), which distinguishes it from sibling tools like get_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?

The description implies this is the right tool for finding tools by name or slug, but it does not explicitly contrast it with get_tool or other siblings. There is no when-not-to-use guidance or mention of alternatives, leaving the agent to infer the appropriate selection.

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