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Glama

Calendly

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 (readOnly, openWorld, idempotent, non-destructive), the description discloses partial failures, timeout behavior (bundlephobia's first measurement 5-30s), and the sources_failed fallback. 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 moderately long but front-loaded with key purpose and usage. Every sentence adds value, though could be slightly more concise.

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?

With no output schema, the description comprehensively lists return fields (summary block fields, advisory detail, links, alternative versions) and handles edge cases like partial failures. Complete and well-rounded.

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 parameter descriptions. The tool description provides context tying params to the composite check but does not add new constraints or format details beyond the schema. 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 clearly states it is a composite check for npm packages combining deps.dev and bundlephobia data. It uses specific verbs ('should I add this npm package') and distinguishes from sibling tools by specifying the npm ecosystem focus.

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 ('is X safe / popular / small' questions) and provides exclusion guidance ('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.

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TDQS

A3.6/5.0
Disambiguation2/5

Several research tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying catalog. Similarly, bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread form a dense prediction-market cluster with fuzzy boundaries. The few Calendly tools are distinct, but they are drowned out by duplicative families.

Naming Consistency3/5

Most names use snake_case and a noun-based pattern (e.g. polymarket_edges, entity_profile), and several follow verb_noun (list_event_types, list_scheduled_events, resolve_entity). But there are standalone verbs (forget, recall, subscribe) and inconsistent verb styles (ask_ vs list_ vs scan_), so the pattern is readable but not predictable.

Tool Count2/5

36 tools is far too many for a server ostensibly named Calendly: only 5 tools relate to Calendly events and invitees, while 31 tools belong to an unrelated Pipeworx data-research platform. The count is bloated by a second domain bolted onto the server, making the surface hard to navigate for its apparent purpose.

Completeness2/5

For the Calendly domain implied by the server name, the surface is missing core operations: there is no create/update/cancel/delete event, no cancel or reschedule flow, no invitee management beyond listing, and no event-type creation or editing. The Pipeworx side is broader, but even it has gaps like no bulk data export or direct raw-tool invocation.

Resources