AgentRank

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

No annotations are provided, so the description must carry the behavioral burden. It explains that the tool searches for similar tools and compares quality scores, but it does not describe output format, ordering, data sources, or side effects.

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 concise sentences with no redundancy. The primary action is front-loaded, and the usage guidance is included 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?

The tool is simple with clear parameters and usage guidance. It mentions comparing quality scores, hinting at the output, but the absence of an output schema means return value details are not fully 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%, so the parameters are already well-documented. The description reinforces that tool_name can be a name or description but adds little 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?

Description clearly states the tool finds alternatives to a specific tool and compares quality scores. It is understandable but does not explicitly differentiate from sibling 'recommend', which could also suggest better options.

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 concrete use cases: when a tool scores poorly or when a user asks for similar tools. It does not mention when not to use it versus alternatives, so it lacks 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 provided, the description carries the behavioral disclosure burden. It discloses the nature of the operation ('Deep quality evaluation') and the return content ('score, signal breakdown, and specific red flags'). It does not mention network access, failure modes, or explicitly confirm read-only behavior, but for a safe-looking evaluation tool this is reasonably 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 two focused sentences with no filler. The core purpose and outputs are front-loaded, and the usage instruction ('Use this BEFORE...') earns its place. Every sentence adds value.

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 single-parameter tool with no output schema, the description is complete: it explains what the tool does, what it returns at a high level, and when to use it. The schema covers parameter format, and the description covers intent and expected result.

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 only parameter 'tool' fully documented as a GitHub URL, owner/repo string, or tool slug. The description adds no additional parameter meaning beyond the schema, so the 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 names a specific verb ('evaluate'), a clear resource ('a tool or MCP server'), and distinctive outputs ('score, signal breakdown, and specific red flags'). It also lists concrete red flags like abandoned, no license, and single contributor, which separates it from sibling tools search, lookup, and recommend.

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 explicitly states when to use the tool: 'BEFORE recommending or installing any tool to verify it is safe and well-maintained.' It gives clear context but does not explicitly name sibling alternatives or state when not to use it, so it falls just short of a 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 burden of explaining behavior. It conveys that this is a read-style verification lookup and connects it to quality checking, but it does not disclose the return format, failure behavior for missing repositories, or any operational caveats.

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, front-loaded sentence that states the core operation first and then adds practical usage guidance. Every phrase earns its place with no repetition or 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 one-parameter lookup tool, the description is nearly complete: it states the purpose, the exact input, and the typical trigger context. It could mention what the tool returns or how 'quality' is conveyed, but the basic usage is fully understandable.

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 is one parameter with 100% schema description coverage, so the schema already fully documents github_url. The description adds context by referencing the URL in the tool's purpose, but it does not add additional meaning 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 gives a specific action ('Check if a specific GitHub repository is in the AgentRank index') and a clear resource (a GitHub URL). It implies distinction from broader search tools by emphasizing a single, specific repository, though it does not explicitly name siblings.

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 provides explicit when-to-use guidance: whenever a user mentions a specific tool or a GitHub URL is encountered, and before recommending a tool. It does not mention when not to use it or compare against alternatives like search or evaluate, but the context is clear.

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 and does reveal the key behavior: returning a ranked list sorted by AgentRank score. However, it does not disclose data source, freshness, scoring basis details, or any side-effect/read-only guarantee beyond the implied read-only return.

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 focused sentences: the first states the action and output, the second gives usage context with illustrative examples. Every sentence earns its place and the key behavior is front-loaded.

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 recommendation query with no output schema, the description sufficiently conveys purpose, return content, and ranking order. Minor gap: it does not describe the exact return structure or any caveats, but nothing critical 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 description coverage is 100%, so both parameters are already documented. The description reiterates use_case with examples but adds little beyond the schema; no new parameter-level semantics are 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 uses a specific verb ('Get') and resource ('recommendations for a use case'), and clarifies the output ('top tools/skills sorted by AgentRank score'). This clearly separates it from siblings like search and lookup by emphasizing quality ranking rather than retrieval or evaluation.

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 explicitly states when to use the tool: 'when a user needs a tool for a specific purpose,' with concrete example queries. It does not explicitly list exclusions or alternatives, but the intended trigger is clear enough.

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 of behavioral disclosure. It does disclose ranking behavior based on maintenance, community health, and adoption, and implies a read-only search operation. However, it does not mention rate limits, pagination, default result counts, or output format, leaving some behavioral gaps.

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 first sentence states the action and scope; the second provides usage guidance. The key instruction to search before installing/recommending is front-loaded and the ranking guidance is actionable.

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 3-parameter search tool with no output schema, the description is mostly complete: it identifies the corpus, the purpose, and the ranking priority. It does not explicitly describe the return value shape (e.g., list of objects with scores), which would be helpful given the absence of an output schema, but the described ranking behavior implies the response contains scored results.

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 input schema already fully documents the query, type, and limit parameters. The description adds context about what to search for and the ranking emphasis, but does not meaningfully add parameter-level semantics beyond the schema. Baseline 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 ('Search') and a clear resource ('AgentRank's index of MCP servers, agent tools, and AI skills'). It also conveys that results are ranked by quality signals. However, it does not explicitly differentiate this tool from its siblings (alternatives, evaluate, lookup, recommend), though the main search role is fairly obvious.

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 explicit usage context: 'Use this BEFORE installing or recommending any tool' and advises preferring higher-scored results. It lacks explicit exclusion criteria or comparison with sibling tools, so it doesn't fully meet the bar for explicit 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.