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Glama

iconify

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?

The annotations already declare readOnly/idempotent, but the description adds valuable behavior beyond that: it discloses composite fan-out, partial failure degradation ('sources_failed will list it'), and a critical timing caveat ('bundlephobia's first measurement can take 5-30s'). This is far beyond what annotations provide.

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 long but every sentence earns its place: purpose, use cases, output format, ecosystem limitation, and failure behavior. It's front-loaded with the core purpose and structured with dashes and clauses. 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?

Given no output schema, the description fully explains the return value: summary block fields, per-advisory detail, links, alternative versions, and the sources_failed field. It also covers latency and ecosystem scope, making it complete for a complex composite 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 coverage is 100% with both parameters well documented. The description does not add new parameter-level semantics beyond what the schema already states (e.g., scoped packages and default version are already in the schema). It adds context around the composite behavior but not parameter syntax, so 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 opens with a specific, vivid purpose: 'Composite "should I add this npm package to my project" check in ONE call'. It clearly names the resources (deps.dev, bundlephobia) and the scope (npm v1). This distinguishes it from sibling tools like validate_claim or deep_research.

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"'. It also provides an exclusion and alternative: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This is model behavior for usage 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

A3.6/5.0
Disambiguation1/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same data sources, and the Polymarket family (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) has blurred boundaries. Agents would struggle to pick the right one without reading every description carefully.

Naming Consistency2/5

Naming is mostly snake_case but follows no consistent verb_noun pattern. Verbs vary widely (ask_, get_, list_, search_, compare_, scan_, validate_, remember, recall, forget, subscribe, unsubscribe, discover, generate, resolve, suggest) and many tools are bare nouns (entity_profile, recent_alerts, polymarket_edges). The inconsistency makes the set feel ad hoc.

Tool Count2/5

34 tools is on the heavy side, and the count is inflated by many near-duplicate data-router and prediction-market tools. The server is named 'iconify' yet only 3 of 34 tools actually relate to icons, indicating poor scoping for the stated purpose.

Completeness2/5

For the icon domain, the surface is minimal (list, search, get) with no create/update/delete. For the broader data/prediction-market domain, there are significant gaps in lifecycle coverage (e.g., subscriptions have create/cancel but no pause/resume, and the memory tools lack namespacing). The mixed focus means no single domain is fully covered.

Resources