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Glama

countries

by io.github.pipeworx-io

Scan Dependency

scan_dependency
Read-onlyIdempotent

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYesnpm package name. Scoped packages (e.g. "@types/node") are accepted.
versionNoSpecific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Adds substantial behavioral context beyond annotations: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. This complements the readOnly/idempotent annotations without contradiction.

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 dense but front-loaded: purpose first, then usage, return fields, limitations, and failure behavior. Every sentence earns its place, making the length justified for a composite tool.

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?

Since there is no output schema, the description enumerates the summary block fields, per-advisory details, links, and alternative versions. It also covers limitations (NPM only) and partial failure behavior, making it self-sufficient for an agent author.

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?

The input schema already documents both parameters ('package' and 'version') at 100% coverage, so the description's examples ('lodash') add minimal new semantic value. Baseline 3 is appropriate when schema handles parameter documentation.

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 specific composite check ('should I add this npm package to my project') and names both data sources (deps.dev and bundlephobia), making it highly specific and self-explanatory. It clearly distinguishes from sibling tools like scan_competitor_ai_presence.

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?

Explicitly states when to use ('Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"') and provides exclusions/alternatives ('NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly').

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

B3.4/5.0
Disambiguation2/5

The tool set is a mix of country lookups and many unrelated Pipeworx tools (e.g., prediction markets, memory, subscriptions). While the country-specific tools are distinct, the overall set is a hodgepodge, making it difficult for an agent to determine which tools are relevant to a given task.

Naming Consistency2/5

Naming conventions are mixed: some use verb_noun (search_countries), some noun_verb (countries_by_currency), some single verbs (forget, recall), and some phrases (ai_visibility_check, ask_pipeworx). No consistent pattern is followed.

Tool Count1/5

35 tools is excessive for a server named 'countries'. Only about 6-7 tools are actually related to countries; the rest are unrelated (Pipeworx services, prediction markets, etc.), creating an extreme mismatch between the server name and its content.

Completeness2/5

For the country domain, the tool set includes search and lookups by code/currency/language/region but lacks basic CRUD, comparisons, maps, or sorting. Additionally, the presence of many unrelated tools dilutes completeness for the stated purpose.

Resources