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Glama

Activecampaign

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

A4.8/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint. Description adds crucial behavioral details: partial failure graceful degradation, bundlephobia first measurement timing (5-30s), and sources_failed reporting. 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?

Description is longer but every sentence adds value. Front-loads key purpose and constraints. Could be slightly more concise, but structure is clear and logical.

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?

No output schema provided, but description compensates by listing expected return fields (summary block, per-advisory detail, links, alternative versions) and error handling. Complete enough for agent to understand outcomes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters described. Description adds value by noting version defaults to latest and confirming scoped packages are accepted. While baseline is 3 due to high coverage, the added context earns a 4.

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?

Description explicitly states the tool performs a composite check for adding npm packages, aggregating data from deps.dev and bundlephobia. It clearly distinguishes from sibling tools by specifying NPM ecosystem only and directing other ecosystems to deps.dev:version.

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".' Also provides exclusion criteria: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.'

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

All Activecampaign toolsInspect
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TDQS

B3.3/5.0
Disambiguation2/5

Several tools occupy nearly the same role: ask_pipeworx and ask_pipeworx_beta are described as behaviorally identical, ask_pipeworx_grounded and deep_research both route questions across the same large catalog, and the Polymarket tools overlap heavily in intent. The ActiveCampaign list/get tools are distinct, but an agent facing 37 tools would frequently struggle to choose the right research or betting tool.

Naming Consistency2/5

Some clusters are internally consistent (list_*, ask_pipeworx_*, polymarket_*), but the server overall mixes bare verbs like remember and forget, noun phrases like entity_profile and deep_research, and brand-prefixed names like pipeworx_trending and polymarket_arbitrage. There is no unified naming convention across the tool set.

Tool Count2/5

37 tools is excessive for an ActiveCampaign integration, and only 6 of them actually relate to ActiveCampaign. The rest are a broad Pipeworx data, research, and prediction-market utility suite, so the count is not well-scoped to the server's stated purpose.

Completeness1/5

As an ActiveCampaign server, the surface is severely incomplete: it only provides read-only list/get operations and no create, update, delete, send, tag, or workflow-management tools. The unrelated Pipeworx tools may be feature-rich, but they do not fill the gaps in the named ActiveCampaign domain.

Resources