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Glama

Hilma

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

A4.8/5.0
Behavior5/5

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

The description adds significant behavioral context beyond the annotations: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. Annotations already indicate read-only, idempotent, non-destructive behavior, and the description harmonizes with them.

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 lengthy but every sentence provides necessary information. It uses bold for key terms and is well-structured, though slightly dense. Could be more concise, but the complexity of the tool justifies the length.

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?

Without an output schema, the description fully explains the return structure (summary block, per-advisory detail, links, alternative versions) and handles edge cases (partial failures, timeout). It is complete for a composite API call 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%, so baseline is 3. The description adds value by clarifying that scoped packages are accepted and that version defaults to latest when omitted. It also restricts to NPM ecosystem, which is not in the schema but is crucial for correct usage.

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 combining deps.dev and bundlephobia data. It uses specific verbs ('scan', 'check') and resources ('npm package', 'deps.dev', 'bundlephobia'), and distinguishes from sibling tools by specifying ecosystem scope and alternatives for other package managers.

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 states when to use the tool: when an agent asks about safety, popularity, or cost of an npm package. It also provides exclusions: for PyPI/Maven/Cargo/Go, use deps.dev:version directly. This gives clear guidance on alternatives.

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

A3.9/5.0
Disambiguation3/5

Several natural-language query tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying Pipeworx catalog. The descriptions do clarify the differences eventually, but the boundaries are subtle enough that an agent could easily pick the wrong one, and discover_tools/suggest_questions also serve a similar onboarding role.

Naming Consistency3/5

Almost all names are snake_case and readable, but they mix verb-first names (ask_, compare_, discover_, validate_) with noun-first names (entity_profile, recent_changes, polymarket_arbitrage, pipeworx_trending). The domain prefixes like hilma_, polymarket_, and pipeworx_ help navigation, but there is no single predictable verb_noun convention across the set.

Tool Count2/5

At 34 tools, the surface is too large for the amount of genuine functional diversity. Several tools are near-duplicates (ask_pipeworx_beta vs ask_pipeworx, ai_visibility_check vs scan_competitor_ai_presence, suggest_questions vs discover_tools), and the set would be noticeably tighter around 20-25 tools without losing coverage.

Completeness4/5

The major workflow clusters are well covered: research/query modes, entity resolution and comparison, prediction-market analysis, memory, and subscription lifecycle management all have their key operations present. Minor gaps exist — such as Hilma notices returning only index metadata rather than full notice text — but there are no critical dead ends for the server's apparent multi-domain research purpose.

Resources