Skip to main content
Glama

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds significant behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed list will report timeouts. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively long but each sentence adds value: primary purpose, usage guidance, ecosystem scope, fallback behavior, and return format. It could be slightly more concise but is well-structured and informative without being excessive.

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?

No output schema exists, but the description fully describes the return format: a summary block with specific fields, per-advisory detail, links, and alternative versions. It also covers error behavior and timeout scenarios. Together with annotations, the description provides complete context for an agent to use the tool effectively.

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 package and version have descriptions). The tool description does not add additional semantic meaning beyond what the schema provides. It mentions 'npm package name' and 'specific version' but these are already in schema. Baseline 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 clearly states the tool performs a composite check for npm packages covering safety, popularity, and size. It uses the specific verb 'scan' and resource 'dependency', and distinguishes from sibling tools (e.g., deep_research) by specifying the NPM ecosystem scope and the two external data sources (deps.dev, bundlephobia).

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?

Explicit usage guidance: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' Also clearly states limitations: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This provides both when-to-use and when-not-to-use guidance.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route research questions; polymarket_edges, polymarket_arbitrage, and bet_research all scan prediction markets for opportunities; ai_visibility_check and scan_competitor_ai_presence overlap. Long descriptions differentiate them, but an agent selecting quickly could easily pick the wrong one.

Naming Consistency2/5

Naming mixes brand prefixes (ask_pipeworx, pipeworx_feedback), domain prefixes (polymarket_arbitrage), bare verbs (remember, forget, recall, subscribe), and noun-ish phrases (entity_profile, resolve_entity). There is no consistent verb_noun or resource-based pattern across the set.

Tool Count2/5

34 tools is heavy, and for a server named Endoflife only 3 tools actually relate to product lifecycle dates. The rest are unrelated Pipeworx data, Polymarket, memory, and utility tools, making the count feel bloated and mismatched to the apparent purpose.

Completeness3/5

The end-of-life domain itself is reasonably complete: list_products, get_product, and get_cycle cover lookup needs. However, the broader tool surface is a patchwork of unrelated subsystems—data research, prediction markets, memory, AI visibility, subscriptions—with no single cohesive domain that completeness can be judged against.