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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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context beyond annotations, including partial failure degradation, the 5-30s potential delay for bundlephobia's first measurement, and the sources_failed field. 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.

Conciseness5/5

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

The description is dense but every sentence adds value: the core purpose, when to use, return fields, ecosystem limitation, and failure behavior are all covered. It is front-loaded with the primary function and structured logically, with no fluff.

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?

With no output schema, the description fully explains the return format, listing all summary fields, advisory details, links, and alternative versions. It also covers edge cases like partial failures and timeouts, making it complete for the tool's complexity.

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 coverage is 100%, so the schema already describes both parameters (package name with scoped packages accepted, version with default behavior). The description does not add new parameter-specific semantics beyond what the schema provides, but it does reinforce the NPM-only ecosystem constraint, which is a minor addition. 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 is a composite check for evaluating whether to add an npm package, with specific verb ('scan') and resource ('dependency'). It distinguishes itself by detailing the two data sources (deps.dev and bundlephobia) and the exact question it answers, which differentiates it from sibling tools.

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 triggers are given: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides exclusions, noting that non-NPM ecosystems should use deps.dev:version directly, giving clear guidance on when not to use this tool.

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

C2.9/5.0
Disambiguation3/5

The 40 tools span Ethereum RPC, Pipeworx data lookup, prediction markets, memory, and subscriptions, creating several overlapping clusters (ask_pipeworx variants, polymarket_edges vs polymarket_arbitrage vs bet_research). Detailed descriptions help, but an agent could still misselect among the deeply related prediction-market tools or the ask_pipeworx family.

Naming Consistency3/5

Snake_case is consistent, but the convention mixes verb-first names (ask_pipeworx, validate_claim, generate_llms_txt) with noun-first names (token_balances, nft_owners, recent_alerts) and RPC-derived names (eth_call, asset_transfers). It is readable but lacks a uniform verb_noun pattern.

Tool Count2/5

40 tools is well over the 25+ threshold for a coherent surface, and the server is named 'Alchemy Eth' while the majority of tools belong to Pipeworx and Polymarket. The count is far too heavy for the apparent Ethereum-focused scope, and would benefit from being split into separate servers.

Completeness3/5

The Ethereum subset is read-heavy (transfers, tokens, NFTs) but the generic eth_call passthrough covers arbitrary RPC methods, partially filling gaps. The Pipeworx side is fairly complete with query, research, grounding, subscriptions, and memory. Overall, the mixed domain makes the full surface feel incomplete with no unified lifecycle.