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Glama

MCPFax Dev Package Intelligence

by com.mcpscores

Latest version and metadata for a package

package_info

Look up a package in npm, PyPI or crates.io: latest version, licence, whether it is deprecated or yanked, when it was last published, dependency count and repository. Use before writing a dependency line, or to check whether a pinned version is behind. Registries answer with hundreds of kilobytes; this returns only the facts that decide the question. Costs $0.005 USDC per call via x402 on Base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name, e.g. 'express', '@scope/pkg', 'requests', 'serde'.
ecosystemYesOne of 'npm', 'pypi', 'crates' (aliases: node, python, rust).

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / properties / ecosystem / examples
      Added value: +[
+  "npm"
+]
    • addedInput schema / properties / name / examples
      Added value: +[
+  "express"
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations exist, so the description carries the burden, and it delivers: it discloses the $0.005 USDC cost via x402 on Base (a critical operational trait for an agent deciding whether to invoke a paid tool) and explains that it returns only the deciding facts rather than the registries' hundreds of kilobytes. It does not cover not-found or error behavior, which keeps it below 5.

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?

Four sentences, each earning its place: purpose and output facts, usage timing, behavioral filtering note, and cost. Purpose is front-loaded and there is zero filler.

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 simple 2-parameter lookup with no output schema and no annotations, the description covers what is returned, when to call it, and its cost — the essential operational facts. The main gap is behavior for nonexistent packages or registry errors, but the tool is otherwise fully navigable.

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?

Schema description coverage is 100% — both 'name' and 'ecosystem' are already documented with examples and aliases in the schema. The description adds no parameter-level detail beyond mapping the registries to the ecosystem field, so the baseline 3 applies.

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?

States a specific verb and resource ('Look up a package in npm, PyPI or crates.io') and enumerates the exact output facts (latest version, licence, deprecated/yanked status, last published, dependency count, repository). This fact list clearly differentiates it from the sibling package_audit and package_vulnerabilities tools by content, even without naming them.

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?

Provides two concrete use cases: 'Use before writing a dependency line, or to check whether a pinned version is behind.' This gives clear contextual guidance but stops short of naming sibling alternatives or stating when-not-to-use, so it misses the top bar.

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

package_audit overlaps with package_info and package_vulnerabilities because it bundles their data, but the descriptions clearly frame audit as the combined decision-oriented call and the others as individual fact or advisory lookups. demand_report and request_data are also distinct: one is aggregate market demand, the other is a single request submission.

Naming Consistency4/5

Three tools share the package_ prefix with descriptive suffixes (audit, info, vulnerabilities), while demand_report and request_data cover the demand-side features without the prefix. All names are snake_case and readable, so the deviation is minor rather than chaotic.

Tool Count5/5

Five tools is a well-scoped size for a package intelligence server. Each tool maps to a meaningful workflow: package facts, vulnerabilities, a composite audit, individual data requests, and an aggregate demand report.

Completeness5/5

The package workflow is covered end-to-end: check package info, check advisories, and get a combined audit verdict before adding or pinning a dependency. The demand side also feels complete with both individual request_data and the aggregated demand_report.

Resources