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Glama

Opendosm My

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?

Annotations already declare readOnlyHint and idempotentHint. The description adds significant behavioral context: fan-out across two services, return format (summary block, per-advisory detail, links, alternative versions), and a practical warning about bundlephobia's first measurement taking 5-30 seconds with sources_failed reporting timeouts. No contradiction with annotations.

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 moderately long (4-5 sentences) but every sentence provides unique value: composite action, return content, usage trigger, ecosystem scope, failure mode. It is front-loaded with the main purpose. A slight trim could improve conciseness, but it is well-structured and not wasteful.

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 tool's complexity (two services, multiple data points) and lack of output schema, the description comprehensively covers return structure (summary block fields, per-advisory detail, links, alternative versions), usage scenario, limitations (NPM only, bundlephobia latency), and error handling (sources_failed). No gaps remain.

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% (both parameters documented). The description adds value by noting scoped packages (e.g., '@types/node') are accepted and that version defaults to latest when omitted. This goes beyond the schema descriptions. Slightly more could be added about the effect of omitting version, but the description is adequate.

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 composite 'should I add this npm package' check with specific resources (deps.dev, bundlephobia), lists what it returns (license, advisories, bundle size, etc.), and distinguishes from siblings by noting NPM ecosystem only and directing other ecosystems to another tool.

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?

Explicit usage guidance is provided: 'Use whenever an agent asks is X safe / popular / small or what does adding lodash cost me'. It also clarifies ecosystem scope (NPM only) and directs to alternatives for other package managers. The description mentions graceful degradation for partial failures, helping agents set expectations.

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

A4.1/5.0
Disambiguation3/5

The set includes three nearly-identical ask_pipeworx variants (base, beta, grounded) that differ only subtly, and deep_research overlaps with ask_pipeworx for multi-part queries. Several company/prediction tools also share adjacent purposes (entity_profile vs recent_changes vs compare_entities; polymarket_arbitrage vs polymarket_edges), though detailed descriptions help. Overall, an agent could mis-select between these overlapping tools.

Naming Consistency4/5

Most tools follow a verb_noun or consistent prefix pattern (opendosm_*, polymarket_*), and the ask_pipeworx family is internally consistent. However, a few are noun phrases (entity_profile, ai_visibility_check, recent_alerts) and some verbs aren't uniform (list_datasets vs dataset_meta vs get_dataset). The mixture is readable but not fully consistent.

Tool Count2/5

34 tools is well above the typical focused server range and includes a clearly redundant experimental variant (ask_pipeworx_beta) plus many loosely-related utility functions (memory, subscriptions, dependency scanning, llms.txt generation). While the broad scope justifies some size, the count feels excessive for the 'Opendosm My' name, which suggests a narrower Malaysian-statistics focus.

Completeness4/5

For the apparent overarching goal of a multi-domain research/query platform, the surface is quite complete: data lookup, deep research, entity comparison, claim verification, prediction-market analysis, memory, and subscriptions are all covered. The Malaysian OpenDOSM component itself has list/meta/get lifecycle. Minor gaps (e.g., no direct dataset search beyond curated lists, no way to execute arbitrary Pipeworx tools directly) are workable.

Resources