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

No annotations are provided, and the description only says 'Compare two MCP servers side-by-side' without disclosing behavioral traits such as whether the operation is read-only, what happens with invalid slugs, how results are presented, or any side effects. This is a significant gap for a tool with no annotation support.

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 with no filler words. It efficiently conveys the core purpose without extraneous detail, earning full marks for conciseness.

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 has a simple interface (two required parameters) and an output schema exists, which reduces the need to describe return values. However, the description lacks any detail about what the comparison entails or behavioral caveats, and with no annotations, completeness is only adequate, not rich.

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% for both parameters (slugA and slugB), so the schema already documents the parameter semantics. The description adds no additional meaning beyond noting that two servers are compared, which does not go beyond the schema's information.

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 clearly states the tool compares two MCP servers, using a specific verb ('compare') and a specific resource ('MCP servers'). It distinguishes itself from sibling tools like get_server (single server) and search_servers (find servers) by explicitly targeting a side-by-side comparison of two named servers.

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 context implies usage when the agent wants to compare two specific servers identified by slugs, which is clear. However, there is no explicit guidance on when not to use this tool or which alternatives (e.g., get_server) to choose for a single-server lookup, so it falls 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?

No annotations are provided, so the description must carry the transparency burden. It states the tool returns 'metadata + README,' which gives some insight into the return content. However, it does not disclose potential errors (e.g., invalid slug), side effects (none expected), or any special behavior (e.g., caching, rate limits). The description is minimal but not misleading.

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, concise sentence that packs the essential information: what it fetches, the scope (single server), and the identifier type (slug). No filler or redundant wording.

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 fetch tool, the description covers the key inputs and outputs. It specifies the return includes metadata and README, and an output schema exists to provide formal structure. There are no complex side effects or prerequisites to document. The description is complete for this tool's complexity.

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 has 100% coverage for the single 'slug' parameter, which is described as 'server slug (required)'. The description also mentions 'by its slug,' reinforcing this. Since the schema already fully documents the parameter, the description adds marginal value, aligning with the baseline of 3.

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 ('full details (metadata + README) of a single MCP server') with a clear targeting mechanism ('by its slug'). It clearly distinguishes from sibling tools like list_categories, search_servers, and compare_servers, which serve different purposes.

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 when to use this tool: when you need full details for a single server and know its slug. However, it does not explicitly mention alternatives or exclusions, such as 'use search_servers if you don't have a slug' or 'use compare_servers for multiple servers.' This is adequate but lacks explicit 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic function without mentioning read-only status, pagination, ordering, or other behavioral traits. This is minimal disclosure beyond what the name already implies.

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 conveys the action and scope with no wasted words. Every word 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-parameter list tool with an output schema, the description adequately specifies the scope. It does not clarify whether counts are live or how categories are ordered, but these are minor gaps and the output schema likely covers return value structure. Overall, it is complete enough for the tool's simplicity.

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, so the description does not need to explain parameter semantics. The baseline of 4 applies, as there is no param-related burden to compensate for.

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 ('List') and resource ('MCP server categories') and adds output detail ('with server counts'). It clearly distinguishes from sibling list tools like list_languages and list_tags, which cover different facets.

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 explicit guidance on when to use this tool versus alternatives like list_languages or list_tags. The scope is implied by the description and name, but no exclusions or alternative references are provided. For a simple list operation, this is acceptable but not exceptional.

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. The phrase 'Featured/hot MCP servers' hints at curated content but does not explain what 'featured' or 'hot' means, nor does it describe the response format or any side effects. This is too sparse for a tool with no annotation support.

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 extremely concise, but it sacrifices useful context. While brevity is positive, the phrase lacks a verb and leaves the tool's exact behavior ambiguous, making it more under-specified than appropriately concise.

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 (one optional parameter, output schema present), so the description does not need to explain return values. However, the ambiguity with 'list_trending' and the vague 'featured/hot' phrase leave the tool's purpose incompletely defined, preventing a higher score.

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 fully covers the only parameter 'limit' with a clear description (max results, 1-50, default 20), achieving 100% schema_description_coverage. Since the schema does the heavy lifting, the description adds no additional parameter semantics, earning the baseline score of 3.

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 clearly indicates this tool returns featured/hot MCP servers, aligning with the 'list' prefix in the name. However, it does not differentiate from the sibling tool 'list_trending', which may overlap in meaning, so it misses the distinction that would earn 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?

No guidance is provided on when to use this tool versus alternatives like 'list_trending' or 'search_servers'. The presence of similar sibling tools makes this omission a notable gap.

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. It clarifies the scope ('of MCP servers') and indicates the output includes counts, but does not disclose any potential limitations or side effects. For a simple list operation this is adequate, though minimal.

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 concise sentence that is front-loaded with the key verb and resource. Every word adds value, with no redundancy 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?

The tool is simple, has no parameters, and an output schema exists. The description fully conveys the purpose and scope. No additional context is necessary.

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 accepts zero parameters, and the schema coverage is 100% (empty object). Per the rubric, zero parameters warrants a baseline of 4. The description adds no parameter info but none 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 uses a specific verb ('List') and resource ('programming languages of MCP servers') with a clear output detail ('with counts'). It distinguishes itself from sibling tools like list_categories and list_tags by focusing on languages.

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 no guidance on when to use this tool versus alternatives. It does not mention exclusions or alternative tools, leaving the agent to infer usage from the name alone.

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. It discloses the primary behavior (listing tags with counts) and implies a read-only operation, but it does not provide additional context such as ordering, pagination, or any limitations. The description is adequate but not rich in behavioral detail beyond the obvious.

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 sentence that is front-loaded and contains no extraneous information. Every word adds substantive meaning, making it highly efficient and well-structured.

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 tool's simplicity (0 parameters) and the presence of an output schema, the description is complete. It fully communicates what the tool does without needing to explain return values, and there is no missing context that would prevent an agent from selecting or invoking it 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?

The tool has zero parameters, so per the rubric the baseline is 4. The description does not need to explain parameters because there are none, and the schema further confirms no inputs.

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 'List all MCP server tags with counts' uses a specific verb ('List') and clearly identifies the resource ('MCP server tags') and the returned detail ('with counts'). It is distinguishable from sibling tools like list_categories and list_languages, which target different resource types.

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 usage when one needs tags with counts, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. There is no sibling differentiation or guidance on when not to use it, though the purpose is straightforward enough for a simple listing tool.

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 of behavioral disclosure. It only states the ranking criterion (24h star growth) but omits details like sort order, pagination behavior, authentication needs, rate limits, or whether the list includes all servers or only a subset. This is insufficient for full informed use.

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 one-sentence description is succinct and starts with the key idea, containing no redundant words. It earns its place by immediately telling the user what the tool does, though its brevity comes at the cost of additional 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?

The tool is simple with one optional parameter and an output schema, but the description lacks guidance on when to use it relative to sibling tools like list_hot. It also doesn't mention any prerequisites, limitations, or return behavior, leaving a new user uncertain about the tool's full context.

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 fully documents the optional 'limit' parameter with a description and default value, so the description doesn't need to add more. However, the description also doesn't reinforce or clarify the parameter's meaning beyond what the schema already provides.

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 'Top MCP servers by 24h star growth' clearly conveys the tool's purpose: it returns a ranked list of servers based on their recent star growth. It specifies the resource (MCP servers) and the metric (24h star growth), which helps distinguish it from siblings like list_hot, although it doesn't explicitly contrast 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?

No guidance is given on when to use this tool versus alternatives such as list_hot or list_tags. The description implies the use case (finding trending servers) but doesn't provide explicit conditions, exclusions, or mentions of when another tool would be more 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?

With no annotations, the description carries full transparency burden. It discloses the catalog source and mentions full-text query and filters, but does not describe return behavior, defaults for sort/limit, or any external API dependencies. The output schema covers return format, so a 3 is appropriate for the limited extra context.

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: the first states the core action and target, the second lists supported filters. No filler, front-loaded, and 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?

The tool has 7 optional parameters and an output schema. The description covers the main purpose and filter categories, while sort/limit are documented in the schema. It is sufficiently complete for invocation, though it could have explicitly noted that it is a query-based alternative to list tools.

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 parameters are fully documented in the input schema. The description adds only a sentence listing filter types but does not enrich meaning for q, sort, or limit 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 uses a specific verb-resource combination ('Search MCP servers in the awesome-mcp.tools catalog') and lists filters, clearly distinguishing it from siblings like get_server and compare_servers.

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 usage via the verb 'Search' and filter support, but does not explicitly contrast with sibling list tools (e.g., list_hot, list_trending) or provide when/when-not guidance. Usage is implied rather than explicit.

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