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

With no annotations provided, the description carries full burden but only states what the tool does without disclosing behavioral traits. It doesn't mention rate limits, error conditions, whether it queries an external service (Bundlephobia), or what the output format looks like (e.g., JSON with size metrics). This leaves significant gaps for agent understanding.

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, efficient sentence that front-loads the core purpose with zero wasted words. It directly communicates the tool's function without unnecessary elaboration, making it easy for an agent to parse quickly.

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 no annotations and no output schema, the description is incomplete for a tool that queries external data. It doesn't explain what information is returned (e.g., size in bytes/KB, comparison data, version specifics) or behavioral aspects like network dependencies. For a tool with 1 parameter but external complexity, this is inadequate.

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 parameter 'name' well-documented in the schema. The description adds no additional parameter semantics beyond implying it's for npm packages, which is already covered. Baseline 3 is appropriate since the schema does the heavy lifting.

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 specific action ('Get bundle size') and resource ('npm package via Bundlephobia'), including the precise measurement format ('minified + gzip'). It distinguishes from siblings like 'downloads' or 'vulnerabilities' by focusing on bundle analysis rather than download metrics or security.

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 for npm package bundle analysis, but provides no explicit guidance on when to use this tool versus alternatives like 'package_info' for general metadata or 'compare_downloads' for download comparisons. The context is clear but lacks sibling differentiation or 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool compares download counts but doesn't mention how the comparison is presented (e.g., table, chart), data sources, rate limits, or error handling. This is a significant gap for a tool with potential complexity.

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, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.

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 no annotations, no output schema, and a tool that likely returns structured comparison data, the description is incomplete. It doesn't explain what the output looks like (e.g., aggregated counts, trends), leaving the agent uncertain about the tool's behavior and 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 schema fully documents both parameters ('packages' and 'period'). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints, meeting the baseline for high coverage.

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 action ('compare') and resource ('download counts across multiple packages'), providing specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'downloads' or 'download_trends', which might offer related functionality.

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 like 'downloads' or 'download_trends'. It mentions comparing 'multiple packages' but doesn't specify use cases, prerequisites, or exclusions, leaving the agent to infer context.

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 states the operation is a 'Get' (implying read-only) and specifies 'direct deps only,' but doesn't cover other important traits like rate limits, authentication needs, error conditions, or what the output format looks like. This leaves significant gaps for a tool that likely queries external data.

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, efficient sentence with zero wasted words. It front-loads the core purpose and includes a clarifying limitation ('direct deps only') that adds value without verbosity.

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 lack of annotations and output schema, the description is incomplete for a tool that likely returns structured dependency data. It doesn't explain what the tree output includes (e.g., versions, licenses), how errors are handled, or any dependencies on external services like the npm registry, leaving the agent with insufficient context for reliable use.

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 description coverage is 100%, with the single parameter 'name' documented as 'Package name.' The description adds no additional semantic context beyond what the schema provides (e.g., format examples, constraints like npm registry compatibility). Baseline 3 is appropriate since the schema adequately covers the parameter.

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 verb ('Get') and resource ('dependency tree for an npm package') with a specific scope limitation ('direct deps only'). It distinguishes this from potential siblings that might provide full dependency trees or other package analyses, though it doesn't explicitly name 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 provided about when to use this tool versus the sibling tools (like 'package_info' or 'vulnerabilities'). The description implies it's for dependency analysis but doesn't specify scenarios, prerequisites, or exclusions that would help an agent choose between available 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 provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves statistics, implying a read-only operation, but doesn't mention potential limitations like rate limits, authentication needs, or what the output format looks like (e.g., raw numbers, charts, or structured data). This leaves gaps in understanding how the tool behaves in practice.

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, direct sentence that efficiently conveys the core purpose without unnecessary words. It is front-loaded with the essential information, making it easy to parse quickly, which is ideal for conciseness in tool descriptions.

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 moderate complexity (2 parameters, no output schema, and no annotations), the description is incomplete. It doesn't explain what the download statistics include (e.g., counts, trends, or breakdowns), how results are returned, or any dependencies on sibling tools, leaving the agent with insufficient context for effective use.

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% description coverage, with clear documentation for both parameters ('name' as package name and 'period' with default and options). The description adds no additional semantic context beyond what the schema provides, such as examples or edge cases, so it meets the baseline for adequate but not enhanced parameter understanding.

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 action ('Get download statistics') and resource ('for an npm package'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'download_trends' or 'compare_downloads', which likely provide related download data but with different scopes or comparisons.

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 like 'download_trends' or 'compare_downloads'. It lacks any context about prerequisites, such as needing a valid npm package name, or exclusions, leaving the agent to infer usage from the tool name and parameters 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?

No annotations are provided, so the description carries full burden. It mentions output format ('daily breakdown + sparkline') but omits critical behavioral details: rate limits, authentication needs, data freshness, error handling, or pagination. For a data-fetching tool with no annotation coverage, this is a significant gap in transparency.

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, efficient sentence that front-loads the core purpose and key output details. Every word earns its place with no redundancy or fluff, making it highly concise 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 no annotations, no output schema, and 2 parameters with full schema coverage, the description is minimally adequate. It covers purpose and output format but lacks behavioral context (e.g., performance, errors) and doesn't explain return values, leaving gaps for a tool that fetches data.

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 fully documents both parameters (name and period with default and options). The description adds no additional parameter semantics beyond implying the output relates to these inputs. Baseline 3 is appropriate as the schema does the heavy lifting.

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 verb 'Get' and the resource 'download trends for an npm package', specifying the output format 'daily breakdown + sparkline'. It distinguishes from siblings like 'downloads' (likely aggregate) and 'compare_downloads' (comparative), but doesn't explicitly name alternatives. This makes it clear but not fully differentiated.

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 for trend analysis with daily granularity and visual sparkline, suggesting it's for detailed historical data vs. siblings like 'downloads' (possibly total counts) or 'package_info' (general metadata). However, it lacks explicit when-to-use guidance or exclusions, leaving some ambiguity about tool selection.

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 states it 'gets' info, implying a read-only operation, but doesn't clarify aspects like authentication requirements, rate limits, error handling, or data freshness. For a tool with no annotations, this leaves significant behavioral gaps unaddressed.

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, efficient sentence that front-loads the core purpose ('Get detailed info about an npm package') and lists key data points. There is no wasted verbiage, and every word contributes to understanding the tool's function.

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 low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and data returned, but lacks details on behavioral traits, usage context, or output format. Without annotations or output schema, more completeness would be beneficial for a read operation.

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 parameter 'name' clearly documented in the schema as 'Package name (e.g., 'express')'. The description adds no additional parameter semantics beyond what the schema provides, such as format constraints or examples. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ('Get') and resource ('npm package'), specifying what information is retrieved (description, license, repo, dependencies). It distinguishes from siblings by focusing on comprehensive package metadata rather than specific aspects like downloads, vulnerabilities, or comparisons. However, it doesn't explicitly differentiate from all siblings (e.g., 'search' might overlap in purpose).

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. With multiple sibling tools available (e.g., 'vulnerabilities' for security info, 'dependency_tree' for dependencies, 'search' for finding packages), there is no indication of when this comprehensive metadata tool is preferred over more specialized ones or what prerequisites might exist.

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 full burden for behavioral disclosure. While it mentions what information is returned, it doesn't address important behavioral aspects like rate limits, authentication requirements, pagination behavior, error conditions, or whether this is a read-only operation. The description is minimal and lacks operational 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?

The description is extremely concise - a single sentence that efficiently communicates the core functionality. It's front-loaded with the main purpose and includes the key return information. There's zero wasted language or 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?

For a search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain the search algorithm, result ordering, error handling, or what happens when no results are found. Given the complexity of search operations and the lack of structured behavioral information, more context would be helpful for an AI agent to use this tool effectively.

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?

With 100% schema description coverage, both parameters are well-documented in the input schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema - it mentions 'search npm packages by query' which aligns with the schema's query parameter description but provides no additional context about parameter usage or interactions.

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's purpose with a specific verb ('search') and resource ('npm packages'), and specifies what information is returned. However, it doesn't explicitly differentiate this search tool from potential sibling tools like 'package_info' or 'vulnerabilities' that might also involve npm package lookup.

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 the seven sibling tools listed. There's no mention of alternatives, prerequisites, or specific contexts where this search is preferred over other npm-related tools available on the server.

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 mentions that this tool provides 'vulnerability info' and notes a limitation compared to 'npm audit,' but doesn't describe what specific information is returned (e.g., severity levels, CVEs), whether it requires authentication, rate limits, or how up-to-date the data is. This leaves significant gaps in understanding the tool's behavior.

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 with just two sentences that are front-loaded: the first states the core purpose, and the second adds a crucial limitation note. Every word earns its place, with no redundancy or unnecessary elaboration, making it efficient and easy to parse.

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 moderate complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and a key limitation, but lacks details on return values, error handling, or deeper behavioral traits. Without an output schema, the description should ideally explain what 'vulnerability info' includes, but it doesn't, leaving the agent with incomplete 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 has 100% description coverage, with the single parameter 'name' clearly documented as 'Package name.' The description doesn't add any additional semantic context beyond this, such as format examples or constraints. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even without parameter details in the description.

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's purpose with a specific verb ('Get') and resource ('vulnerability info for an npm package'). It distinguishes from siblings like 'package_info' or 'dependency_tree' by focusing specifically on security vulnerabilities. However, it doesn't explicitly contrast with sibling tools, which prevents a perfect score.

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 implied usage guidance through the note about 'Full audit requires npm audit in project context,' suggesting this tool provides limited vulnerability information compared to a full audit. However, it doesn't explicitly state when to use this tool versus alternatives like 'package_info' or when not to use it, leaving some ambiguity about its scope.

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