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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

A4.6/5.0
Behavior5/5

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

While annotations already declare readOnlyHint and idempotentHint, the description adds substantial behavioral context: it fans out across two external services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeout cases. This goes beyond basic safety hints to describe latency and failure behavior, which is valuable for an agent.

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 longer than ideal but every sentence carries information: purpose, fan-out architecture, usage trigger, output summary fields, ecosystem limitation, and performance caveat. It is front-loaded with the core purpose and then logically organized. A slight deduction for density, but no wasted words.

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?

Given the tool's complexity (multi-source, composite check) and the absence of an output schema, the description is remarkably complete. It lists the exact return fields (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), covers failure modes, and explains ecosystem constraints. An agent would know exactly what to expect and when to use this tool.

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 baseline is 3. The description does not add extra meaning to the parameters beyond what the schema already provides (package name and optional version). However, it does contextualize the parameters by mentioning the NPM ecosystem and the default to latest version implicitly, but not in a way that adds new semantic detail.

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, action-oriented statement: a composite 'should I add this npm package to my project' check. It clearly identifies the tool's scope (scanning dependencies) and differentiates it from siblings by naming the specific data sources (deps.dev and bundlephobia) and the question it answers (safe/popular/small).

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 says when to use: whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. It also gives an exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', steering users toward alternatives. This is clear when/when-not guidance.

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

A3.7/5.0
Disambiguation2/5

Several tool families have genuine boundary ambiguity: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, and the five Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) all live in the 'find/pursue an edge' space, requiring an agent to parse very long descriptions to choose correctly. The IP, memory, and subscription tools are clearly distinct, but the overlap-prone families cause real misselection risk.

Naming Consistency3/5

All names are snake_case and each family is internally consistent (polymarket_*, ask_pipeworx_*, subscribe/unsubscribe, remember/recall/forget). However, conventions mix across the set: verb_noun (geolocate_ip, resolve_entity, validate_claim) coexists with noun-first names (entity_profile, pipeworx_feedback, recent_alerts) and bare verbs (remember, forget), so there is no predictable global pattern.

Tool Count2/5

At 33 tools the set exceeds the heavy threshold, and the server name 'iplookup' covers only 2 of them; the remaining 31 form a sprawling data-research, prediction-market, memory, and subscription platform with tangential utilities like generate_llms_txt and scan_dependency. The broad scope means few tools are individually useless, but the server reads as a kitchen sink rather than a focused toolkit.

Completeness3/5

As an IP-lookup service the surface is thin: only geolocation and ISP data, with no WHOIS, reverse DNS, proxy/VPN detection, or reputation records. As a data-research platform the surface is strong (universal query routing, grounded verification, entity profiles, comparisons, subscription lifecycle, memory). This lopsidedness makes the actual domain ambiguous and leaves the namesake use case under-covered.