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Glama

Hubspot

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

A4.8/5.0
Behavior5/5

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

Adds substantial behavioral context beyond annotations: describes fan-out, return summary block, per-advisory detail, links, alternative versions, and graceful degradation with sources_failed list. No contradictions 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 moderately long but well-structured with clear sentences. Every sentence adds useful information; could be slightly more concise but is effective.

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 (multiple sources, timing, partial failures), the description covers return format, behavior, limitations, and ecosystem scope. No output schema exists, but description compensates adequately.

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 descriptions for both parameters. The description adds value by noting scoped packages are accepted for the package parameter and that version defaults to latest.

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's a composite check for adding an npm package to a project, fanning out to deps.dev and bundlephobia. It distinguishes itself by specifying NPM ecosystem only, while other ecosystems are handled by other tools.

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 'Use whenever an agent asks ...' providing concrete examples. Also mentions timing caveats for bundlephobia and partial failure handling, and notes alternatives for other ecosystems.

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

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates or overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve overlapping query/discovery purposes. The polymarket tools and HubSpot tools are more distinct, but the set as a whole has fuzzy boundaries between many members.

Naming Consistency2/5

Naming is inconsistent across the set: HubSpot tools use an hs_ prefix, Pipeworx tools mostly use bare verbs (ask_pipeworx, recall, forget), and other tools mix styles (ai_visibility_check, generate_llms_txt, polymarket_edges). The hs_* subset is consistent, but overall there is no single predictable pattern.

Tool Count2/5

38 tools is too many for the apparent scope, especially since the server is named 'Hubspot' but only 6 of the tools are HubSpot-specific. A large portion of the catalog covers unrelated Pipeworx data access, prediction markets, memory, and npm scanning, making the set feel bloated and unfocused.

Completeness2/5

The HubSpot portion of the tool set is read-only: it can get, list, and search companies/contacts/deals, but has no create, update, or delete operations, leaving obvious lifecycle gaps. If the intended domain is actually Pipeworx/data research, the HubSpot tools seem like an unrelated afterthought, so the surface is incomplete for either interpretation.

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