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Glama

Slack_connect

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?

Annotations indicate readOnly, idempotent, non-destructive. Description adds: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. No contradictions.

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 thorough but front-loaded with key purpose. Every sentence adds value, though slightly long. No redundancy.

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, but description comprehensively details the return structure (summary block, per-advisory detail, links, alternative versions) and error handling (partial failures). Fully adequate for a 2-parameter tool.

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. Description adds examples for scoped packages and version format, providing extra context beyond the schema.

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 clearly states it is a composite check for npm packages, combining deps.dev and bundlephobia. It explicitly scopes to NPM and distinguishes from sibling tools that cover 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 says 'Use whenever an agent asks is X safe / popular / small' and provides exclusion: for PyPI/Maven/Cargo/Go, use 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 Slack_connect toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

The five Slack tools are distinct, but the rest of the set is a sprawling bundle of Pipeworx, prediction-market, memory, and subscription tools with several overlapping pairs: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, while discover_tools and suggest_questions both act as discovery entry points and scan_competitor_ai_presence wraps ai_visibility_check. An agent would frequently have to read long caveats to choose the right tool.

Naming Consistency3/5

Most names are descriptive snake_case and the Slack tools share a clean slack_ prefix, but the broader set mixes verb-led names (ask_pipeworx, generate_llms_txt, validate_claim) with noun-style names (entity_profile, pipeworx_trending, ai_visibility_check) and a few bare verbs (remember, recall, forget, subscribe). It is readable but does not follow a single predictable pattern.

Tool Count2/5

36 tools is above the 25+ threshold and far more than a Slack connector needs: only five tools actually interact with Slack, while the other 31 are unrelated Pipeworx research, prediction-market, memory, and subscription features. The set reads as a kitchen-sink bundle rather than a focused integration.

Completeness2/5

For a Slack_connect server, the surface is only partially complete: it can list channels/users, join, read history, and send messages, but common Slack operations like threads, reactions, message update/delete, channel creation/archiving, and direct messages are missing. The unrelated data tools do not fill these gaps, so the actual Slack domain would still cause agent failures.

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