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Glama

MCPFax Dev Package Intelligence

Should I add this dependency?

package_audit

One call that answers whether a dependency is safe to add: latest version, licence, deprecation, advisories affecting the current version, how long since the last release, and a verdict with the specific concerns found. Prefer this over calling package_info and package_vulnerabilities separately — it is one payment instead of two and applies the judgement. A clean verdict means nothing was found in these sources, not that the package is guaranteed safe. Costs $0.01 USDC per call via x402 on Base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name.
ecosystemYesOne of 'npm', 'pypi', 'crates'.

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: +[
      +  "lodash"
      +]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the transparency burden and does substantial work: it discloses the pay-per-call cost ($0.01 USDC via x402 on Base), states the specific data sources checked, and includes a crucial caveat that a clean verdict 'means nothing was found in these sources, not that the package is guaranteed safe.' It stops short of describing failure modes or data freshness, but the most important behavioral traits are visible.

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?

The description is three dense sentences with no filler. The core purpose comes first, the sibling-tool guidance is second, and the limitation/cost detail is last. Every sentence contributes actionable information, and the whole thing is still short enough to parse quickly.

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

Completeness5/5

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

For a two-parameter tool with no output schema, the description fully covers what an agent needs: what the tool returns, how it should be used relative to alternatives, what a clean verdict means, and the cost. The combination of the parameter schema and this description leaves no obvious gap for successful invocation.

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%, so the schema already documents both name and ecosystem with examples and allowed values. The description adds no parameter-level detail beyond what the schema provides, so the baseline of 3 is appropriate.

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 opens with a concrete promise: 'answers whether a dependency is safe to add' and enumerates the exact output dimensions (latest version, licence, deprecation, advisories, release recency, verdict). It also clearly frames the tool as the combined alternative to package_info and package_vulnerabilities, so it is easy to distinguish from siblings without inspecting schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly directs agents: 'Prefer this over calling package_info and package_vulnerabilities separately' and gives a concrete reason (one payment instead of two plus applied judgement). This is clear when-to-use guidance relative to the named alternatives, even though it does not enumerate every edge case where the siblings would be preferable.

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.

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