package-intel-mcp

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

No annotations are provided, so the description must fully disclose behavior. It mentions the data source ('via deps.dev') but does not discuss rate limits, data freshness, error handling, or output structure. The description is too brief to cover behavioral traits beyond the basic operation.

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 sentences long, front-loaded with the main action, and contains no extraneous information. Every sentence serves a purpose: stating the action and providing 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?

Given the tool's simplicity (3 parameters, no output schema, no annotations), the description covers the basic use case, ecosystems, and data source. It could mention that it only returns direct dependencies (already implied by 'direct') and perhaps response format, but overall it is adequate.

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 33% (only 'version' has a description). The description adds 'Ecosystems: npm, pypi, cargo' which repeats the enum, and 'defaults to latest' aligns with the version schema. However, no additional semantics are provided for 'name' or beyond what schema presents.

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 lists direct dependencies of a package version, using a specific verb ('List') and resource ('direct dependencies'). It distinguishes from siblings by focusing on dependencies, while sibling tools cover downloads, health, info, search, and versions.

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 context on when to use the tool ('Lets an agent understand what a package pulls in before adding it'), but does not explicitly state when not to use it or mention alternatives. The guidance is clear though not exhaustive.

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 disclose behavioral traits. It mentions returning 'recent download counts,' which implies a read-only operation and provides basic context. However, it lacks details on output format, rate limits, or authentication needs.

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: first states the core function, second adds the use case and lists ecosystems. Every word earns its place; no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 parameters, no output schema), the description is adequately complete. It states the purpose, the returned data (recent counts), and supported ecosystems. Missing details on exact output structure, but acceptable for a straightforward 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 lists the three ecosystems (npm, pypi, cargo), adding meaning beyond the enum's raw values, but does not describe the 'name' parameter beyond being a package identifier. Partially compensates.

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 ('Get popularity/download statistics'), the resource (a package), and the specific data ('recent download counts'). It also lists supported ecosystems, distinguishing it from sibling tools like package_info or package_dependencies.

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 the tool is 'useful for judging how widely used and battle-tested a dependency is,' providing clear guidance on when to use it. It does not directly contrast with siblings, but the context is sufficient.

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 describes what the tool checks (read-only data) and mentions deps.dev as a data source, but does not explicitly state that it is a read-only operation or disclose any side effects, rate limits, or data freshness.

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, front-loaded with purpose and components. No wasted words. Efficiently conveys the tool's function and scope.

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 simple parameters and no output schema, the description covers inputs, factors checked, data source, and return type ('summary'). The summary description could be more specific (e.g., format), but the listed factors provide sufficient context for an agent.

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 0%, so the description must compensate. It adds context that 'ecosystem' is one of npm/pypi/cargo (already in enum) and 'name' is a package name. No additional format or constraint details beyond what the schema provides, making it marginal but adequate for the simple parameters.

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 it's a composite trust/health check for a package, listing specific factors (recency, deprecation, license, maintainer count, dependency count, security advisories). It distinguishes from sibling tools like package_dependencies or package_info which are more specific.

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 an agent needs a quick health overview ('for a package an agent is considering'), but does not explicitly state when to use vs. alternatives or provide exclusions. The context is clear, but not exhaustive.

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?

The description implies read-only behavior ('Get core metadata') and lists returned fields, but with no annotations, it could further clarify safety 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 sentences, 36 words, front-loads purpose and usage, no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 2-param tool with no output schema, the description fully conveys what metadata is returned and when to use it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% (only 'name' described). The description adds ecosystem context but doesn't detail parameter format or constraints beyond 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 clearly states the tool retrieves core metadata (latest version, description, license, etc.) and distinguishes it from sibling tools like package_dependencies and package_versions.

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?

Explicitly advises using before installing or recommending a dependency, but doesn't mention when not to use it or alternatives.

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 only mentions returning 'top matches with descriptions' but fails to disclose pagination, ordering, rate limits, or behavior for no matches.

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 two sentences are front-loaded and concise, with no unnecessary detail. Every sentence contributes 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?

Given the lack of schema descriptions, output schema, and annotations, the description is incomplete. It fails to provide sufficient context for proper parameter usage and expected behavior.

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 0% schema description coverage, the description adds minimal meaning beyond the schema. It explains ecosystem values but does not elaborate on query format or limit behavior.

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 searches for packages by keyword and returns top matches, specifying supported ecosystems (npm, cargo) and explicitly excluding PyPI. This distinguishes it from sibling tools like package_info and package_dependencies.

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 explains when to use the tool (keyword search for packages) and which ecosystems are supported, but does not explicitly contrast it with alternatives or provide conditions for when not to use it.

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. The description states it lists 'released versions' with 'release dates' and 'most recent first', which adds basic behavioral context. However, it does not disclose whether pre-release versions are included or any pagination/rate limits, leaving some 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?

The description is very concise: two sentences that cover purpose, usage guidance, and supported ecosystems with no wasted words.

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 explains what is returned (recent versions with release dates, ordered) but does not mention that the number of results is controllable via the 'limit' parameter. The absence of an output schema makes the description the sole source of return information, and it is slightly incomplete.

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 description repeats the ecosystem enum values but does not explain the 'name' or 'limit' parameters. The 'limit' parameter has a default of 15 but is not mentioned at all. With 0% schema coverage, the description fails to compensate by adding 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 verb 'list' and resource 'released versions of a package', and distinguishes this tool from siblings like package_dependencies and package_downloads by focusing 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 second sentence provides explicit context for when to use the tool ('pick a version or check maintenance activity'), but does not mention when not to use it or name alternative tools.

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