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Glama

Ashby

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

A4.7/5.0
Behavior4/5

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

Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive. The description adds valuable behavioral details such as fan-out to two services, potential 5-30s timeout for new versions, and return of a summary block with specific fields. This exceeds what annotations alone provide.

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?

The description is well-structured with the core purpose upfront ('Composite check in ONE call'), followed by details on return value, ecosystem limitations, and error behavior. Every sentence adds value, and the length is appropriate for the tool's complexity.

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?

Since no output schema is provided, the description fully enumerates the return fields (summary block fields, advisory details, links, alternative versions) and explains partial failure behavior. No critical information is missing for an agent to use the tool correctly.

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%, so baseline is 3. The description adds implicit context by explaining how parameters are used (e.g., 'Defaults to the latest published version when omitted') and constrains the 'package' parameter to the npm ecosystem. This provides meaningful extra information 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?

The description clearly states the tool's purpose: a composite check for npm packages that gathers license, advisories, version history, and bundle size info. It differentiates itself from sibling tools like 'deep_research' and 'scan_competitor_ai_presence' by focusing on a specific npm package evaluation.

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?

The description explicitly specifies when to use this tool (e.g., 'is X safe / popular / small') and provides alternatives for other ecosystems (PyPI, Maven, etc.). It also mentions graceful degradation for partial failures, giving clear usage context.

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

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants; polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread all target prediction-market opportunities. Research tools like entity_profile, compare_entities, recent_changes, and validate_claim also blur together, making it hard to pick the right tool.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use verb_noun (get_candidate, list_applications, remember), others use noun_verb (bet_research, entity_profile) or prefix-only patterns (ask_pipeworx, pipeworx_feedback, polymarket_edges). The Ashby tools use ashby_ prefix, but the rest mix pipeworx_, polymarket_, and bare names, with no uniform style.

Tool Count2/5

36 tools is excessive for a coherent server and spans unrelated domains: ATS (Ashby), data queries (Pipeworx), prediction markets (Polymarket), memory, subscriptions, and web utilities. This feels like a kitchen sink rather than a focused toolset, and the count alone makes selection overwhelming.

Completeness2/5

The Ashby ATS subset is incomplete: it provides get/list operations but no create, update, or delete for candidates or jobs, and no interview management. The broader server's scope is so mixed that each domain has obvious gaps, leaving agents unable to complete common workflows end-to-end.

Resources