Package Intel MCP

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

With no annotations, the description carries the full burden and adds useful behavioral facts: it narrows the result to direct (not transitive) dependencies, notes the default latest version, and identifies the external deps.dev source. It does not, however, disclose edge cases, return shape, or registry-specific resolution 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?

Two sentences with no padding: the core action and default behavior are front-loaded, and the use case plus ecosystem list round it out. Every phrase 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?

For a read-only listing tool, it covers what is returned, the default version, the source, and supported ecosystems. An explicit output-format statement would be nice, but 'List...dependencies' plus the use case makes the behavior sufficiently predictable to call the 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?

All three parameters are already fully described in the input schema, including the ecosystem enum and the version default, so schema coverage is 100%. The description reinforces the version default and dependency scope but adds little parameter-level detail 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 names an exact verb and resource: 'List the direct dependencies of a specific package version' and adds the data source ('via deps.dev'). This clearly separates it from sibling tools like package_downloads or package_health, which cover different aspects of package data.

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?

It gives a clear invocation context: 'understand what a package pulls in before adding it,' so an agent knows when the tool is appropriate. It does not name alternatives or state when not to use it, but the situational guidance is unambiguous.

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. It does disclose that this is a read-only statistics lookup returning recent download counts, which is the core behavior. However, it does not specify the exact time window, aggregation level, or whether any limits or caveats apply.

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 and front-loads the core function, followed by a useful real-world use case and supported ecosystems. Every sentence adds value, with no filler or repetition beyond the brief 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?

The tool is simple and the schema fully documents required parameters, but there is no output schema and no annotations. The description gives a general sense of the return value ('recent download counts') but lacks detail on time range or exact output format, which leaves some ambiguity for interpreting 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 coverage is 100%, so the schema already documents both parameters with examples and the enum for ecosystem. The description repeats the ecosystem list but adds no meaning beyond what the schema provides, so the baseline score of 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 begins with a specific verb and resource: 'Get popularity/download statistics for a package,' and clarifies the data as 'recent download counts.' This clearly distinguishes the tool from siblings like package_dependencies, package_health, 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?

The description gives a clear use case: judging how widely used and battle-tested a dependency is. It does not explicitly name alternatives or state when not to use this tool, but the context is strong enough for an agent to infer appropriate 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?

With no annotations, the description carries the full burden and does well: it lists the checks performed, mentions the external data source (deps.dev) for advisories, and states that the output is an actionable summary. It does not disclose potential rate limits, external call side effects, or error behavior, but the core behavior is transparent.

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 compact sentences: the first front-loads the purpose and the metrics checked, the second states the ecosystem coverage and output type. Every sentence earns its place 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?

For a moderately complex tool with no output schema, the description covers the return format ('summary an agent can act on'), the supported ecosystems, and the specific components of the health check. Minor omissions like not-found behavior and third-party dependency caveats are not critical for basic invocation.

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 fully documents both parameters, including the ecosystem enum and exact name expectations with examples. The description adds context by linking parameters to the health-check purpose, but it does not add new syntactic or formatting details beyond the schema, which is the baseline for 100% schema 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 uses a specific verb 'health check' and resource 'package', and enumerates the exact dimensions checked (release recency, deprecation, license, maintainer count, dependency count, security advisories). This clearly distinguishes it from sibling tools that each focus on a single aspect like dependencies or 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?

'For a package an agent is considering' gives clear context for when to use this tool, and 'Returns a summary an agent can act on' signals its decision-support role. However, it does not explicitly state when not to use it or name alternatives among the sibling tools, so it stops short of full exclusion 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?

Without annotations, the description carries behavior disclosure; 'Get' and the field list make it clear this is a read-only metadata lookup. However, it does not mention error behavior (e.g., package not found), live data, or any limits, and no annotations back up the safety profile.

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, with the action and key output front-loaded, followed by a usage cue and ecosystem list. No redundant or filler text.

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?

Returned fields are enumerated in lieu of an output schema, so an agent knows what it will get. It lacks only minor behavioral details like not-found handling, but the combination of param schema, field list, and usage context is sufficient for this simple 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?

Input schema already documents both parameters fully (name and ecosystem with enum), and the description only repeats 'Ecosystems: npm, pypi, cargo.' The field list relates to output, not parameter semantics, so description adds no extra parameter value.

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?

States a clear verb and resource ('Get core metadata for a package') and itemizes the exact fields (latest version, description, license, etc.). This distinguishes it from sibling tools focused on dependencies, downloads, health, 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?

'Use before an agent installs or recommends a dependency' gives explicit context for when to call. It does not name sibling alternatives or state when not to use it, but the guidance is unambiguous enough.

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 carry the transparency burden. It clearly discloses the core search behavior and the ecosystem limitation, but it does not explain result ranking, sorting, pagination, or the exact return shape beyond 'descriptions', leaving some behavioral aspects implicit.

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 front-load the purpose and then provide a precise scope limitation. Every sentence adds information, and the parenthetical about PyPI prevents invalid calls without extra 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 tool is simple, but with no annotations and no output schema, the description should specify more about result structure and ordering. It covers scope and supported ecosystems, yet omits return fields, ranking behavior, and behavior on no matches, and the schema's contradictory ecosystem description introduces some confusion that the description only partially mitigates.

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 100% parameter coverage, so baseline is 3. The description adds value by resolving a contradiction in the ecosystem parameter: the schema says 'One of: npm, pypi, cargo' while the enum only allows npm/cargo; the description explicitly clarifies that PyPI is unsupported and explains why. It also frames query as keyword search, reinforcing the schema's free-text 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 names a specific action ('Search'), a resource ('packages'), and a concrete output ('top matches with descriptions'). It also explicitly states the supported ecosystems (npm/cargo) and why PyPI is excluded, clearly separating it from sibling tools that retrieve dependencies, downloads, health, info, or 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 implies use when searching npm/cargo packages and gives an explicit when-not for PyPI ('PyPI has no public search API'). However, it does not contrast this tool with sibling tools or state when to prefer e.g. package_info for details, so routing to alternatives is left to inference.

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 and does state the key behavior: output is a list of versions with dates, sorted most recent first. However, it doesn't disclose error behavior, what happens for unknown packages, or the exact output shape, which are relevant for an unannotated 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: core action, purpose, and supported ecosystems. It is front-loaded with the main behavior in the first sentence and contains no 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 has no output schema and no annotations, so the description must cover enough for correct invocation. It covers purpose, sorting, and ecosystems, and the schema handles parameters, but it omits error cases and any return-format details beyond 'release dates,' leaving minor gaps.

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 description doesn't need to add parameter detail. It does reiterate the ecosystem enum and package-name semantics implicitly, but that's already in the schema; no extra semantic value is added.

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 the specific verb 'List' with a clear resource ('recent released versions of a package with release dates') and adds ordering ('most recent first'), which immediately distinguishes the tool from siblings like package_dependencies or package_downloads. It also states the supported ecosystems, so there's no ambiguity about 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?

It frames when the tool is useful: 'Helps an agent pick a version or check how actively it is maintained,' which gives a clear context for invocation. It doesn't explicitly mention exclusions or alternative tools, but the use-case framing is enough to route an agent to this tool for version-related queries.

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