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Glama

Emoji

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds valuable behavioral details: fans out across two services, returns summary block and details, warns about potential 5-30s timeout on BundlePhobia first measurement, and how partial failures are handled (sources_failed field). No contradiction 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 informative but somewhat verbose. It front-loads the main purpose and usage, then details return values and limitations. Could be slightly more concise, but efficiently communicates all necessary information.

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 thoroughly explains the return format (summary block with specific fields, per-advisory detail, links, alternative versions) and covers failure modes (timeout handling). Provides a complete picture for the agent.

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 clear descriptions for both parameters (package and version). The description does not add significant meaning beyond what is already in the schema, so baseline score of 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 it is a composite check for npm packages, specifying the verb 'scan' and the resource 'npm package'. It distinguishes from siblings by noting the NPM-only scope and referring to alternatives for other ecosystems.

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: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also gives exclusions: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', guiding the agent when to pick another tool.

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

All Emoji toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tool groups have heavy functional overlap: ask_pipeworx, ask_pipeworx_beta (explicitly identical to stable right now), ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all route around the same data-querying core. The Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) also blurs together, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Most tools follow a clear lowercase snake_case convention with family prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*. Minor deviations exist — bare verbs like remember/recall/forget and the quirky generate_llms_txt — but the overall pattern is predictable and readable.

Tool Count2/5

33 tools is already on the heavy side, but the real problem is scope: the server is named 'Emoji' yet only 2 of 33 tools relate to emoji, with the other 31 forming a sprawling data-research/prediction-market platform. The count feels mismatched with the server's apparent identity.

Completeness2/5

The data-research side is impressively broad, but the stated domain (Emoji) is barely covered — only lookup and keyword search with no listing, metadata, or classification features. The toolset is a grab bag of unrelated domains, so the overall surface is not complete for any single coherent purpose.

Resources