AgentoolRank

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 does disclose the return contents (a list of alternatives each annotated with GitHub stats), which is genuinely useful, but it says nothing about read-only nature, auth needs, result limits, or how many alternatives are returned.

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 tight sentence that leads with the action and resource, then adds the payload and purpose. No filler, though it is arguably too short to carry usage context.

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 read tool with no output schema, the description covers purpose and return contents adequately. It omits routing guidance versus siblings and any parameter detail, which are the remaining gaps for an agent choosing and invoking it.

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?

One parameter ('slug') with 0% schema description coverage, so the description must compensate. 'Alternatives to a tool' implies the slug identifies the target tool, but the description never names the parameter or clarifies its expected format, so compensation is only partial.

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 (lists open-source alternatives to a given tool) and previews the payload (GitHub stats per item) and the intent (compare options). It is distinguishable from siblings like get_tool and search_tools, though it never explicitly contrasts itself with them.

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 trailing phrase 'to compare options' hints at intent but gives no when-to-use condition, no prerequisites, and no exclusions relative to get_tool or search_tools. The agent must guess whether to call this or search_tools to explore options.

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 full burden, and it does disclose the possible status values and the kind of data returned (queue position, listing URL), which is genuinely useful behavioral context. However it says nothing about whether the call requires authorization, how the status_token is validated, or how stale the status may be.

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 the parenthetical used efficiently to enumerate return values. No filler, no 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?

No output schema means the description has to describe returns (it does, partially), but it is silent on the two required parameters, one of which is an unexplained token. For a two-parameter tool with zero schema coverage this leaves meaningful gaps.

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 0% and both parameters are required. The description never explains what submission_id is or that status_token is an opaque credential-like value, leaving the agent to guess at formats and sourcing for a required secret.

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 — 'check review status of a submission' — and even previews the result set (pending/approved/rejected, queue position, listing URL). It is clearly distinct from siblings like get_tool and search_tools, though it never explicitly differentiates itself.

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?

There is no statement of when to use this tool versus alternatives, and no prerequisites (e.g., that it is the natural follow-up to submit_tool). Usage is only inferable from the tool name and the word 'submission'.

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 behavioral burden. It usefully discloses what the response contains (stats, capabilities, best-for, limitations, alternatives), but says nothing about being a read-only lookup, behavior on unknown slugs, or rate limits.

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: verb, resource, identifier source, then the returned fields. No filler or restatement of the name.

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 and no annotations, the description compensates by enumerating the returned fields, which is adequate for a simple one-parameter getter. Error behavior and read-only status remain unstated but are minor for this shape.

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 0%, so the description must define 'slug'. It does: the identifier comes from search_tools, which tells an agent where to get a valid value — meaning beyond the bare 'string' type 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 (get details for one tool by slug) and enumerates the payload: GitHub stats, capabilities, best-for, limitations, alternatives. It does not explicitly separate itself from the sibling get_alternatives, which the mention of an 'alternatives' field could blur, so it falls short of a 5.

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 parenthetical '(from search_tools)' establishes the pipeline: discover a slug with search_tools, then fetch its details here. That is clear context for when to reach for this tool, but no explicit when-not case or direct comparison to get_alternatives is offered.

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 full behavioral burden. It does add real value by disclosing the ranking mechanism (GitHub activity) and the return payload (stars, 30-day star growth, capabilities, alternatives), but is silent on result limits/pagination behavior, rate limits, and any auth or data-freshness 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?

Two tight sentences: scope and ranking come first, return payload second. Every clause earns its place with no filler or repetition.

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?

There is no output schema, so the description's description of return fields is necessary and present, and the ranking basis is stated. It is nearly complete for a 2-param search tool; only edge behavior like query semantics or result ordering tie-breaks remain unspecified.

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% — both 'query' and 'limit' are documented in-schema, including format examples and a range/default. The description adds nothing beyond that, so the baseline of 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?

States a specific verb and resource ('Search open-source AI agent tools') and enumerates the categories it covers plus the ranking basis ('ranked by GitHub activity'). It is clearly the discovery tool versus the retrieval siblings (get_tool, get_alternatives), but it never names or contrasts those siblings 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 by the enumeration of categories and the example queries in the schema, so an agent can infer 'use this to find tools'. However, there is no explicit when-to-use guidance, no statement of when to prefer get_tool or get_alternatives over searching, and no exclusions.

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 full burden and does well: it discloses that free listings are queued, that the response enumerates all paid options with price/days/checkout_url, that it recommends the cheapest option fitting the budget/deadline, and that it returns submission_id + status_token. It omits auth/permission requirements and rate limits, but the behavior profile is largely covered.

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 dense sentence, front-loaded with the core purpose followed by flow and return details; every clause (queued free listing, paid options, recommendation, submission_id/status_token) adds value. It is slightly long but not padded.

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?

There is no output schema, so the description must explain return values, and it does (paid option list with checkout_url, submission_id, status_token). Combined with the 88% parameter coverage and named follow-up tool, an agent has enough to invoke correctly; only auth/rate-limit context 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 already high (88%), so the baseline is 3, and the description adds genuine meaning beyond the schema by explaining how max_budget_usd and deadline_days drive the recommendation of the cheapest fitting paid option. That interaction is not evident from the schema descriptions alone.

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 ('List an AI agent tool ... on AgentoolRank') and clearly scopes the target domain (framework, coding agent, MCP server, etc.). It is trivially distinguishable from the read-only siblings get_tool/search_tools/get_alternatives, since this is the only submission 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?

Explains the submission flow (free listing is queued) and explicitly routes the agent to the follow-up tool by name ('for get_submission_status'). It does not state when NOT to use it or any preconditions (e.g., duplicate listings), so it stops short of a full when/when-not guide.

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