aicoolies

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

With no annotations, the description must carry behavioral disclosure. It adds the 'Equivalent to GET /api/agents/context' detail and mentions the compact slug-only mode, which are genuinely behavior-revealing. However, it does not mention return format, default behavior, pagination, or error cases, so coverage is only partial.

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 with no filler, and the core purpose is front-loaded. The endpoint and optional modes are stated compactly. Slightly more structure or clarity around the include parameter would be an improvement, but the definition is efficient.

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 GET-style catalog tool with zero required parameters, the definition is usable. However, there is no output schema and no description of the returned catalog structure, what 'context' means here, or how include interacts with the returned index, so an agent is left to infer important 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%, so the schema already documents all three parameters. The description lightly reinforces category, include, and compact modes but adds no meaning beyond what the schema provides, so the baseline 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 ('Return') and resource ('filterable aicoolies catalog index'), and reinforces it with the REST endpoint. It is clear enough to distinguish from sibling tools, though it does not explicitly name or contrast those alternatives.

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?

No guidance is given about when to use this tool versus search_tools, get_tool, or get_catalog_meta. The optional parameters suggest it is a listing/filtering endpoint, but exclusions and alternative-selection conditions are left entirely implicit.

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 burden. It discloses that the tool is a read-only GET returning counts and timestamps, and implies it is lightweight since it precedes the full context pull. It does not mention auth, rate limits, or exact response structure, but for a zero-parameter meta endpoint this is acceptable.

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 with no wasted words. The core return value is front-loaded, the endpoint equivalence adds unambiguous reference, and the usage guidance closes the description efficiently.

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 endpoint, the description is nearly complete: it covers what is returned, the equivalent endpoint, and when to call it. It could add response format details, but the absence of parameters and the simple nature of the tool make the description adequate.

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 schema has zero parameters, so there is no parameter detail to document. The description's mention of what the endpoint returns covers the only meaningful input question: no configuration is needed.

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 ('Return') and a specific resource ('aicoolies collection counts and freshness timestamps'), so an agent knows exactly what this tool produces. Mentioning the equivalent REST endpoint reinforces the purpose and distinguishes it from context 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?

'Use this before pulling the full context payload' gives clear, actionable usage context and implies the intended sequence relative to a heavier sibling like get_catalog_context. It does not explicitly state when not to use it or list alternatives, so it stops one step short of a full 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?

With no annotations, the description carries the behavioral disclosure burden. It does convey that this is a public read-style fetch from a catalog, which is useful, but it does not disclose behavior for unknown slugs, error conditions, or the shape of the returned 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?

One sentence with no filler, and the essential identifying constraint ('by slug') is front-loaded. Every word contributes to tool selection and invocation.

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 single-parameter public fetch, the description is nearly sufficient: it identifies the tool, source, and lookup key. The only missing piece is what happens when the slug does not exist, but that is a minor gap for an otherwise straightforward retrieval 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 input schema already fully documents the single 'slug' parameter with an example. The description's 'by slug' wording merely repeats the schema's meaning without adding format, constraints, or lookup semantics beyond what is already provided.

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 a precise lookup key ('by slug'). This clearly differentiates the operation from the sibling tools that list, search, or fetch catalog context/meta.

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 for retrieving a single known tool by slug, which contrasts with listing or searching tools. However, it does not explicitly state when to prefer this over siblings, such as 'use search_tools when you don't know the slug'.

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 full burden. It clearly implies a read-only operation by using 'List', but it does not disclose any additional behavioral traits such as response format, potential pagination, or effects on resources. For a simple listing tool, this minimal disclosure is acceptable but not exceptional, hence a middle score.

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 two sentences with zero filler. The core purpose and the included resource types are front-loaded, and the parameter note is concise. Every word earns its place, making it highly efficient.

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 simplicity of the tool (no parameters, no output schema), the description provides a solid list of what will be returned (resource categories) but does not specify the format of the output (e.g., whether it's a list of URLs, brief descriptions, or a structured object). An agent might benefit from a hint about the structure or the nature of the items, so completeness is adequate but not fully robust.

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 description explicitly notes 'No arguments.' Per the rubric, a zero-parameter tool gets a baseline of 4 because there is nothing to explain beyond the absence of inputs. The description does not need to add parameter semantics.

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 ('List'), a clear resource ('official aicoolies developer resources'), and enumerates the exact types (OpenAPI, MCP, llms.txt, datasets, developer portal). It clearly distinguishes itself from sibling tools like get_tool or search_tools, which focus on individual catalog entries rather than a bundled resource overview.

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 implicitly indicates when to use this tool (when you want a listing of developer resources) but does not explicitly state when not to use it or mention alternatives. There is no guidance contrasting it with get_catalog_meta or search_tools, leaving the agent to infer usage context from the tool name and purpose.

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 of behavioral disclosure. It only says the tool returns matching public records; it does not describe match semantics, ordering, pagination, result shape, or any side effects. The single behavioral detail it adds is the 'public records' scoping, but that is not enough to be transparent.

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 tight sentence with no filler: it identifies the action, the resource, the search fields, and the result scope. It is front-loaded and efficiently structured, though slightly under-specified in behavior, so it does not earn a 5.

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 low-complexity tool with only two documented parameters, the description is fairly complete. However, there is no output schema and no annotation coverage, and the description does not clarify match behavior or result details, leaving moderate gaps for an agent deciding how to interpret the response.

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 input schema already describes both parameters completely, including the case-insensitive fragment behavior for 'q' and defaults/maximums for 'limit'. With 100% schema description coverage, the tool description does not need to repeat parameter details. It adds no new semantic value but also does not need to.

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 ('Search'), a clear resource ('the aicoolies tool catalog'), and the matching criterion ('by name or slug'). It also clarifies that only public records are returned, which distinguishes it from tools that might return internal or administrative data. It does not explicitly contrast itself with siblings like get_tool, but the purpose is still clear.

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 a search/exploration use case: an agent should call this when looking up tools by name or slug fragment. However, it does not explicitly state when to prefer this over get_tool or other sibling tools, and it offers no exclusions or alternative routing. This is implied usage, not clearly guided.

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