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Package Intel MCP

package_dependencies

List the direct dependencies of a specific package version (defaults to latest), via deps.dev. Lets an agent understand what a package pulls in before adding it. Ecosystems: npm, pypi, cargo.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesExact package name as published in that registry, e.g. express for npm, requests for pypi, serde for cargo.
versionNoOptional; defaults to the latest/default version
ecosystemYesPackage registry to look in. One of: npm, pypi, cargo.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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TDQS

A4.1/5.0
Disambiguation4/5

Each tool maps to a clearly identifiable package-intelligence task—search, metadata, versions, dependencies, downloads, or health. The only real overlap is between package_health and package_info, since health includes deprecation status, license, and maintainer data that also appear in info, but the composite vs. core-metadata framing keeps them distinguishable.

Naming Consistency5/5

Every tool follows the same package_<noun> pattern, making the API surface predictable and easy to navigate. The nouns are all simple, descriptive, and consistent in style.

Tool Count5/5

Six tools is well-scoped for a package intelligence server; each tool addresses a distinct facet of evaluating a dependency. There is no bloat or redundancy that would make the set feel heavy.

Completeness4/5

The set covers the core package evaluation workflow: discover via search, inspect metadata and versions, understand dependencies, gauge popularity, and get a health/advisory summary. Minor gaps exist, such as no transitive dependency traversal or ecosystem-wide comparison, but agents can still make solid dependency decisions with the provided tools.