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Glama

Ulid

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?

Annotations already indicate read-only, idempotent, non-destructive. The description adds significant behavioral details: composite multi-source fan-out, partial failure degradation (sources_failed list), latency note for bundlephobia first measurement (5-30s), and the structure of the returned data. 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?

The description is informative but somewhat verbose, listing return fields in detail. However, every sentence adds value, and the key information (purpose, usage, ecosystem, behavior) is front-loaded. Nearly concise, but could be trimmed slightly without losing essential context.

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?

Given the absence of an output schema, the description thoroughly explains all returned components (summary block, per-advisory detail, links, alternative versions). It also covers ecosystem scope, partial failure, and latency. No gaps in understanding the tool's capability.

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 descriptions for both parameters. The description adds no additional detail beyond what the schema provides for parameters (package name, version optional). 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 explicitly states it is a composite check for deciding whether to add an npm package, using deps.dev and bundlephobia. It specifies the verb (scan/determine), resource (npm package dependency), and scope (license, advisories, size, tree-shaking). It clearly distinguishes from direct deps.dev calls for 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?

The description directly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also notes the NPM-only limitation and fallback for other ecosystems, providing clear context for when to use this tool.

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

A3.8/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as query entry points (with ask_pipeworx_beta explicitly identical to ask_pipeworx right now); polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research form a dense prediction-market suite; ai_visibility_check and scan_competitor_ai_presence are single vs. multi variants. An agent would frequently struggle to pick the right tool without reading long descriptions.

Naming Consistency4/5

Most tools follow a clean snake_case convention and are mostly verb_noun (generate_ulid, parse_ulid, list_subscriptions, resolve_entity, validate_claim, compare_entities), making the set predictable. Minor deviations exist (entity_profile, deep_research, bet_research, ask_pipeworx_beta) but they are still readable and don't break the overall pattern.

Tool Count2/5

33 tools is well above the 25-tool threshold for a heavy surface, and the server name 'Ulid' suggests a tiny scope that wildly mismatches the actual content. While the real domain (Pipeworx data + prediction markets) is broad, the set bundles many subdomains into one server, making navigation and selection costly.

Completeness4/5

For the actual apparent domain—structured data research, entity lookups, verification, prediction-market analysis, memory, and subscriptions—coverage is strong: query, grounded query, deep research, entity profiles, comparisons, resolution, validation, discovery, alerts, and memory tools are all present. ULID functionality is minimal but sufficient (generate + parse). Minor gaps exist (e.g., no direct single-source browser beyond discover_tools) but agents can work around them.

Resources