Skip to main content
Glama

Datecalc

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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: fans out to two services, returns specific fields, handles partial failures gracefully, and notes that bundlephobia's first measurement may take 5-30 seconds. 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 relatively long but every sentence adds value. It front-loads the core purpose and then details scope, return data, and edge cases. A bit dense, but well-structured and efficient 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?

Given no output schema, the description fully explains the return format (summary block with specific fields, per-advisory detail, links, alternative versions). It also covers partial failures, ecosystem scope, and performance notes. Comprehensive for the tool's purpose.

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 both parameters fully described (package name with scoped package acceptance, version defaults to latest). The description adds no additional parameter semantics beyond the schema. Baseline of 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 clearly states the composite purpose: checking an npm package for safety, popularity, and size by combining deps.dev and bundlephobia. It uses specific verbs ('scan', 'check') and resources ('npm package'), and distinguishes from siblings by its unique composite nature.

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: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also specifies ecosystem limitations ('NPM ecosystem only in v1') and suggests alternatives for other ecosystems ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'), providing clear when-to-use and when-not-to-use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Datecalc toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose, such as the multiple Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) and the several query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research). Memory tools (remember, recall, forget) also add to the confusion.

Naming Consistency3/5

Tool names are mostly snake_case but vary in pattern: some are verb_noun (add_duration, compare_entities), others are noun_noun (entity_profile, date_diff) or single verbs (forget). This mix reduces predictability but is not chaotic.

Tool Count2/5

The server name 'Datecalc' implies a narrow focus, yet 33 tools exist covering far more than date calculations. The count is too high for the implied purpose, and many tools are unrelated to the server's apparent domain.

Completeness2/5

For the implied date calculation domain, only three tools exist (add_duration, date_diff, date_info), leaving obvious gaps. For the actual broad data access domain, coverage is better but still lacks a cohesive structure.

Resources