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Glama

Carbon Interface

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral details: partial failure degradation, bundlephobia's first measurement can take 5-30s, and 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.

Conciseness5/5

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

Though long, every sentence adds distinct information: composite nature, usage cues, return fields, ecosystem limitations, and failure timing. It is front-loaded with the core purpose and well structured.

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?

Without an output schema, the description thoroughly enumerates the return structure (summary block fields, per-advisory detail, links, recent versions) and failure semantics. This is sufficient for an agent to understand expected outputs and edge cases, even for a multi-source 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 covers both parameters with descriptions (100% coverage), so baseline is 3. The description reinforces 'version defaults to latest' and scoped package acceptance, but adds little new parameter syntax beyond what the schema already provides.

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's verb ('check') and resource ('npm package'), framing it as a composite check across deps.dev and bundlephobia. It differentiates from sibling tools like scan_competitor_ai_presence by focusing on package health/cost analysis.

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 instructs 'Use whenever an agent asks is X safe / popular / small' with concrete example. Also gives an exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', which guides the agent to alternatives for non-npm packages.

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

All Carbon Interface toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools are near-duplicates or heavily overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, several polymarket_* tools cover the same edge-detection domain, and scan_competitor_ai_presence is just a wrapper around ai_visibility_check. Agents will frequently struggle to pick the right tool.

Naming Consistency3/5

Most tools use snake_case, but the pattern is inconsistent: verb_noun (estimate_electricity, resolve_entity), noun_phrase (entity_profile, recent_changes), brand-specific (ask_pipeworx, pipeworx_trending), and family-prefixed (polymarket_*). The naming is readable but lacks a single cohesive convention.

Tool Count2/5

At 34 tools, the set is far larger than the 'Carbon Interface' name implies. It piles together carbon estimation, a massive data-router, prediction-market analysis, memory, subscriptions, and meta-tools, making the surface feel bloated and unfocused.

Completeness3/5

The carbon-estimation purpose is thin (only three estimators with no lifecycle), but the broader Pipeworx/data and prediction-market subdomains are fairly well covered. Gaps exist in each subdomain (e.g., no order placement for betting, no carbon scope beyond the three estimates), and the lack of a clear primary domain makes coverage hard to assess.

Resources