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Dailymed

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.

TDQS

A4.8/5.0
Behavior5/5

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

Discloses important behavioral traits beyond annotations: bundlephobia's first measurement can take 5-30s, partial failures degrade gracefully with sources_failed listing, and the composite nature of the call. All consistent with readOnly and idempotent 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?

Description is informative and front-loaded with core purpose, but somewhat lengthy. Could be tightened while retaining essential details, but structure is clear.

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?

Despite no output schema, the description fully explains return format (summary block, advisories, links, alternatives) and edge cases (timeouts, failures). Complete for effective tool usage.

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% with parameter descriptions. The description adds value by explaining default behavior for version and acceptance of scoped packages, enhancing semantic understanding.

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 using deps.dev and bundlephobia. It uses specific verbs ('scan', 'check') and resources, and distinguishes itself from sibling tools by its unique functionality.

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?

Explicitly states when to use ('is X safe / popular / small' questions) and when not to use (other ecosystems), with alternative guidance for PyPI/Maven/etc. Also notes partial failure behavior.

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

All Dailymed toolsInspect
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TDQS

B3.4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; deep_research; bet_research and multiple polymarket tools). This makes it difficult for an agent to distinguish which tool to use.

Naming Consistency4/5

Tool names consistently use snake_case and follow a verb_noun or noun_verb pattern. However, some names are very long and descriptive (e.g., polymarket_kalshi_spread, scan_competitor_ai_presence), which is acceptable but slightly inconsistent in length.

Tool Count3/5

With 38 tools, the server is on the high side of reasonable. Many are meta-tools or query routers, which inflates the count. The scope is very broad, covering diverse domains, making the number somewhat justifiable but still feeling heavy.

Completeness3/5

For the Dailymed domain, tools cover label search, retrieval, history, and comparison. However, many other domains (e.g., finance, prediction markets) rely on a handful of routing tools (ask_pipeworx) rather than dedicated tools, leaving the coverage uneven and not fully self-contained.

Resources