package-intel-mcp

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

With no annotations, the description must disclose all behavioral traits. It mentions 'direct dependencies' and 'via deps.dev', but omits important details like read-only nature, authentication, rate limits, output format, or recursion depth.

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, front-loaded with the action, and includes essential details (ecosystems, source). No unnecessary 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?

For a tool with no output schema and minimal annotations, the description is too sparse. It lacks information about the return format, pagination, error handling, or any constraints beyond listing dependencies.

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 low (33%), with only version having a description. The description lists ecosystems (repeating the enum) and notes default version, but does not clarify the 'name' parameter or add semantic value beyond the schema for most 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 the tool lists direct dependencies of a specific package version, using deps.dev. It provides a specific verb and resource, and distinguishes itself from siblings like package_info or package_versions by focusing on 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 implies when to use it ('before adding a package'), but does not explicitly contrast with siblings or state when not to use it. Guidance is present but not comprehensive.

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 only states what the tool does, not behavioral traits like data freshness, error handling, rate limits, or whether results are cached. The description is too vague for a tool with no output schema.

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 concise at two sentences, front-loading the purpose then adding context. Every sentence serves a purpose, though it could be even more efficient by omitting the general usefulness phrase.

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, the description should describe return values (e.g., what fields are returned, what 'recent' means). It only mentions 'recent download counts,' leaving the agent guessing about the response structure.

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%, yet the description adds minimal value: it mentions ecosystems but does not explain the 'name' parameter format or provide examples. Baseline for low coverage should compensate, but it does not.

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 popularity/download statistics for a package, specifically recent download counts. It lists supported ecosystems (npm, pypi, cargo) and distinguishes from sibling tools like package_dependencies or package_health.

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 a clear use case: 'judging how widely used and battle-tested a dependency is.' This implies when to use it, but lacks explicit exclusions or comparisons to sibling tools, so scores 4 rather than 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 present, so the description must fully disclose behavior. It states the tool performs a health check and returns a summary, but lacks details on response structure, potential side effects (likely none), rate limits, or authentication needs. The mention of deps.dev as a data source adds slight value.

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, front-loaded with the core purpose, and each sentence adds value. The list of factors is compactly integrated. No redundant information.

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 moderate complexity (multiple factors) and no output schema, the description provides a high-level overview but does not specify the return format or fields. It mentions 'summary an agent can act on' but lacks concrete output details that would help an agent use the result.

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

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

The input schema has no parameter descriptions (0% coverage). The description explains the two parameters (name and ecosystem) and lists the allowed ecosystems (npm, pypi, cargo), adding meaning beyond the schema. However, it does not specify the format of the name 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 it is a composite trust/health check for a package, listing specific factors (release recency, deprecation, license, maintainers, dependencies, security advisories). It distinguishes itself from sibling tools like package_info (general info) and package_downloads (download stats) by focusing on health assessment.

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 when an agent needs to evaluate package health, but it does not explicitly state when to use this tool versus alternatives (e.g., package_dependencies for dependency details). No exclusion criteria or usage context 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 provided. Description discloses the returned metadata fields and deprecation status, but lacks info on side effects, rate limits, or error handling. Adequate for a read tool.

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?

Three short sentences, no wasted words. Front-loaded with the tool's action, followed by usage guidance and ecosystem list.

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 two simple parameters, no output schema, and siblings listed, the description is complete: it explains return fields, use case, and supported ecosystems.

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 covers name (description given) and ecosystem (enum), with 50% description coverage. Description adds context that the tool returns core metadata but does not elaborate on individual 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 'Get core metadata for a package' and lists specific fields (latest version, description, license, etc.), distinguishing it from siblings like package_dependencies or package_downloads.

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 says 'Use before an agent installs or recommends a dependency,' providing clear context. Does not mention when not to use or alternatives, but the sibling list helps.

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, description partially informs behavior: it is a search returning top matches with descriptions. Missing details on pagination, ordering, or side effects, but no contradictions.

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 efficient sentences cover action and ecosystem constraints with no redundant information.

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?

Lacks output schema and does not describe return format details like data fields or pagination, but is adequate for a simple search tool. Could be more complete given sibling 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?

0% schema description coverage; description does not explain parameter roles (e.g., 'query' as keyword, 'limit' default) beyond basic schema definition, failing to compensate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Search for packages by keyword' with specific result 'top matches with descriptions' and lists supported ecosystems, distinguishing it from sibling tools that focus on dependencies, downloads, etc.

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 states supported ecosystems and that PyPI is not supported due to missing API, giving clear use context but no explicit comparison with sibling tools.

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 burden. It discloses ordering (most recent first) and ecosystems, but does not mention whether the operation is read-only, limits beyond the schema, or handling of empty results.

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, front-loaded with the action, and every word serves a purpose. No unnecessary information.

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 list tool with 3 parameters and no output schema, the description covers the essential purpose, ecosystems, and use cases. It could be improved by briefly mentioning that the output includes version strings and dates, but overall it is largely 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?

Schema description coverage is 0%, so the description must compensate. It lists the three ecosystems, which adds meaning to the 'ecosystem' parameter, but the 'name' and 'limit' parameters receive no additional clarifying details beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists recent released versions with release dates, sorted most recent first, and mentions its purpose. It is specific but does not explicitly differentiate from sibling tools like package_search or 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?

The description provides context for when to use the tool (e.g., picking a version or checking maintenance), but it does not explicitly state when not to use it or mention alternatives among sibling tools.

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