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Glama

Altos

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds key behaviors: fans out across two services, bundlephobia first measurement can take 5-30s, partial failures degrade gracefully, sources_failed lists timeouts. No contradiction.

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?

Single paragraph but well-structured: front-loads purpose, then details services, return data, and edge cases. Every sentence adds value with 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 fully explains the return value (summary block, per-advisory detail, links, alternative versions). Covers partial failures and timing constraints. Very complete for a composite 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% for both parameters. Description adds value by clarifying scoped packages are accepted and version defaults to latest published. These details go beyond the schema definitions.

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 npm packages covering license, advisories, bundle size, etc. It uses specific verbs ('scan', 'check') and resource ('npm package'), and distinguishes from siblings by focusing on npm dependency 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 tells when to use ('when an agent asks is X safe/popular/small') and notes ecosystem limitations ('NPM ecosystem only in v1; PyPI/Maven/Cargo/Go fall under deps.dev:version directly'). Provides clear guidance on partial failures.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes; however, 'ask_pipeworx' and 'ask_pipeworx_grounded' are very similar, and 'polymarket_edges' vs 'polymarket_edge_tracker' could cause confusion. The real estate tools (altos_active_listings, altos_new_listings, altos_pending_sales) are clearly differentiated by status.

Naming Consistency4/5

Tool names use snake_case and are descriptive, but prefixes vary (altos_, polymarket_, ask_pipeworx, etc.) and patterns like 'entity_profile' or 'generate_llms_txt' don't follow strict verb_noun. Overall, naming is fairly consistent and readable.

Tool Count3/5

36 tools is high but justifiable given the broad scope (real estate, prediction markets, SEC/FDA data, etc.). Some utility tools (list_subscriptions, pipeworx_feedback) could be integrated, but the count is borderline heavy for a single server.

Completeness4/5

The server covers multiple domains thoroughly with tools for real estate, company profiles, prediction markets, and general data queries. Minor gaps exist (e.g., no dedicated tool for FDA drug details beyond ask_pipeworx), but meta-tools like deep_research fill many needs.

Resources