npmlens-mcp

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

With no annotations, the description implies a read-only comparison but does not disclose output format, error handling, or the maximum of 10 packages (covered only in schema). It does list some metrics, adding moderate 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?

A single, front-loaded sentence that conveys the action, resource, and example metrics without any unnecessary words. It is 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?

For a one-parameter tool with no output schema, the description explains the core action but omits return structure, edge cases, and limits. It is adequate but lacks depth for full self-sufficiency.

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 already provides 100% coverage for the packages parameter. The description reinforces the purpose but adds no new parameter-specific details, 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?

The description clearly states the tool compares multiple npm packages side-by-side, with specific metrics like downloads, stars, and license. This distinctly separates it from sibling tools such as get_package_info (single package) and search_npm.

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 is clear: use this when you need to compare multiple packages. However, it does not explicitly name alternatives or state when not to use it, so it stops 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?

No annotations are provided, so the description must carry the burden of disclosing behavioral traits. It does not specify what is returned (e.g., a number or object), how invalid package names are handled, or any rate limits. The description is too thin to offer meaningful transparency beyond the basic action.

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. It efficiently states the resource and the period scope, earning its place without 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?

The tool is simple with only two parameters, but there is no output schema, so the description should at least hint at the return format or required input semantics. It covers the core purpose but omits details like what the response contains or any edge-case behaviors, leaving it adequate but not fully complete.

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 0% description coverage, and the description only restates the period values ('day/week/month') that are already in the enum. It does not clarify the 'name' parameter beyond what is inferable from the tool name, so it adds little semantic value over 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 uses a specific verb ('Get'), identifies the resource ('npm downloads'), and scopes by period ('last day/week/month'). This clearly distinguishes it from siblings like get_readme and get_package_info, making the tool's purpose unambiguous.

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 explicit guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or mention sibling tools like search_npm for different use cases, leaving usage context 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 full burden of behavioral disclosure. It only states that a dependency tree is fetched, but does not disclose whether this is a read-only operation, how depth/version/defaults behave, whether dev dependencies are included by default, or what the returned tree structure looks like. The schema covers some defaults but the description itself adds little behavioral 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 a single, short, front-loaded sentence: 'Get the dependency tree for a package.' There is no filler, repetition, or extraneous content. It is concise and immediately states the tool's purpose.

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 four parameters, no output schema, and no annotations, yet the description is only one sentence. It does not explain what the returned dependency tree contains, how depth affects the result, or when to prefer this over sibling tools. This is inadequate for an agent to fully understand the tool's behavior without additional inference.

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 describes all four parameters with 100% coverage, so the baseline is 3. The description does not add any parameter-specific meaning beyond the schema, but this is acceptable because the schema already provides clear definitions for name, depth, version, and includeDevDependencies.

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 ('dependency tree for a package'), clearly identifying the tool's core function. It does not explicitly contrast with sibling tools like get_package_info, but 'dependency tree' is sufficiently distinct to differentiate it from listing package metadata or readmes.

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 such as get_package_info or get_usage_snippet. There is no mention of scenarios where a dependency tree is useful, nor any exclusions or prerequisites, leaving the agent to infer usage solely from the name.

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. It discloses 'when available' for GitHub details and specifies 'last-week downloads,' but it omits behavior around the includeReadme parameter, version resolution, or failure modes when sources are unavailable.

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, focused sentence that front-loads the primary action and lists key outputs. No wasted words or redundant details.

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 composite tool with no output schema and no annotations, the description provides a serviceable overview but misses critical details like the effect of includeReadme, version fallback behavior, and what 'enriched' includes beyond the listed items. The incompleteness is noticeable for an aggregator 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?

Schema description coverage is 0%, so the description must compensate. It does not explain the version or includeReadme parameters; only 'name' is implied by 'package info.' This leaves agents guessing about parameter purposes.

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 function with specific components: registry metadata, last-week downloads, and GitHub repo details. This distinguishes it from sibling tools like get_downloads and get_readme, which are more specialized.

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 use for obtaining a comprehensive package overview, but it does not explicitly contrast with alternatives like calling get_downloads or get_readme separately. No when-not-to-use guidance is provided.

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 full burden. It states the action but does not disclose behavioral nuances such as how the 'limit' parameter affects the claim of listing 'all' versions, whether pre-releases are included, or the result ordering. The description's 'all available versions' claim conflicts with the existence of the limit parameter.

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, focused sentence that begins with the verb 'List' and immediately conveys the core purpose. It includes no redundant words or filler, 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?

The description mentions the key output fields (publish dates, dist tags) but does not explain the optional parameters or their effect on the result. The 'all' claim is misleading given the limit parameter. Overall, the description is adequate but leaves gaps regarding behavior and edge cases.

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 provides descriptions for all parameters (100% coverage), so the description adds no additional parameter semantics. The baseline of 3 is appropriate since the schema already documents each parameter clearly.

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 specifies the resource ('available versions of a package') with distinguishing details ('publish dates and dist tags'). It clearly differentiates from sibling tools like get_package_info and get_downloads by focusing specifically on version history.

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 its use case (when you need package versions) but does not explicitly state when to use it over alternatives or provide exclusions. There is no mention of sibling tools or scenarios, but the purpose is clear enough to infer usage.

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. It states the tool fetches text and can target a version, but it does not disclose error handling, rate limits, or return format details. For a simple read operation, this is minimal but 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?

A single, front-loaded sentence with no wasted words. It directly states the action and resource, and the parenthetical about version adds useful precision without clutter.

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 only a brief description, the tool's essential purpose is clear, but return format, error behavior, and truncation (truncateAt) are not mentioned. Given the sibling context, this is adequate but lacks depth.

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 all three parameters with 100% coverage. The description adds no new parameter semantics beyond restating that version is optional, which the schema already conveys. 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 uses a specific verb 'Fetch' and a clear resource 'README text' for an npm package, which effectively distinguishes it from sibling tools like get_package_info or get_usage_snippet. It also specifies optional version targeting, clarifying exact scope.

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 clearly implies the tool is for retrieving README content, giving the user a direct context of when to use it. However, it does not explicitly name alternatives or provide exclusions, so it falls short of a 5 but is still clearly contextualized.

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 mentions 'likely' suggesting heuristic behavior, but does not disclose limitations (e.g., what happens if the README has no usage snippet), return format, or whether network requests or certain permissions are involved. Minimal information beyond the basic action.

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 unnecessary words. It immediately states the action and target. All essential information is presented 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 tool with no output schema and no annotations, the description is incomplete. It does not explain what a 'usage snippet' looks like, how version affects the result, or any edge cases. Given the existence of many sibling tools, more context is needed for an agent to select this tool 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?

Input schema has 0% description coverage, and the description does not elaborate on the parameters. While 'name' is self-explanatory, 'version' is not explained—whether it is optional, how it affects the extraction, or its format. The description fails to compensate for the lack of schema-level parameter details.

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 extracts a usage snippet from a package's README. This distinguishes it from sibling tools like get_readme (which likely fetches the full README) and search_npm (which searches packages). The verb 'extract' and the resource 'usage snippet' are specific and unambiguous.

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 such as get_readme or get_package_info. It does not mention prerequisites, exclusions, or any contextual triggers. The usage is only implied by the tool's name and description, but no explicit guidance is given.

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. However, it only restates the core function without explaining how keyword matching works, default operator behavior, result ordering, pagination, or rate limits. The schema provides some parameter details, but the description adds no additional behavioral 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 a single, concise sentence that is front-loaded with the action ('Search npm packages'). It contains no unnecessary words or filler, making it efficient and easy to scan.

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?

Despite having only a few parameters, the tool lacks an output schema and has no annotations. The description omits crucial context such as result format, sorting behavior, pagination limits, and how it differs from 'search_npm'. This makes the tool incomplete for an agent that needs to invoke it correctly without prior knowledge.

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 67% (2 of 3 params described). The description merely repeats the keyword concept and adds no extra meaning for 'size' or 'operator'. It does not clarify how keywords interact with the operator parameter or what the size controls. The description fails to compensate for the missing 'size' description 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?

The description uses a specific verb and resource ('Search npm packages') with a clear scope ('by specific keywords/tags'). This distinguishes it from the sibling 'search_npm' tool, which likely performs a broader full-text search. The purpose is immediately understandable.

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 over alternatives. The description does not mention prerequisites, exclusions, or the relationship to the sibling 'search_npm' tool. Users are left to infer usage context from the name and description 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 must carry the full burden of disclosure. It only says 'Search' and offers no details about result format, pagination behavior, default sorting, or any side effects. This is not a contradiction, but it is minimal and leaves much to inference.

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. Every word contributes to communicating the core purpose, 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 tool's complexity (4 parameters, a nested weights object, no output schema), the description is far too sparse. It omits pagination semantics, weight interpretation, and response shape, so an agent cannot confidently invoke this tool without additional information.

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 only 25%, with descriptions provided solely for 'query'. The description does not add any meaning for 'from', 'size', or the nested 'weights' object. It fails to compensate for the low schema coverage, leaving those parameters semantically opaque.

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 function: 'Search the npm registry for packages.' The verb 'Search', resource 'npm registry', and object 'packages' make it specific and easy to distinguish from siblings like get_readme or get_package_info.

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 alternatives. In particular, there is a near-identical sibling tool 'search_by_keywords' that likely overlaps in functionality, and the description does not clarify which search tool to select for a given task.

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