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Arcgis Marioncountyor

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

A4.5/5.0
Behavior5/5

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

Adds important behavioral context beyond the readOnly/idempotent annotations: bundlephobia first-measurement latency (5-30s), partial failure degradation with sources_failed listing, and the NPM-only limitation. This helps the agent set expectations and handle edge cases.

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 but information-dense, covering purpose, usage, return summary, and caveats in one paragraph. Every sentence earns its place, though it could be tightened slightly for faster scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by listing the summary block fields, per-advisory details, links, alternative versions, and failure behavior. It is complete for a high-level composite tool, though a few specifics (e.g., what advisory_count means) are left implicit.

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?

The schema already documents both parameters (package, version) with 100% coverage, including scoped package acceptance and version defaults. The description adds only the ecosystem restriction, which is contextual but does not deepen understanding of the parameters themselves.

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 as a composite 'should I add this npm package' check that fans out across deps.dev and bundlephobia. It specifies what it returns and explicitly distinguishes itself from siblings by naming its data sources and ecosystem scope.

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?

Provides explicit usage context ('Use whenever an agent asks...') and states a clear exclusion ('NPM ecosystem only in v1... fall under deps.dev:version directly'). This gives the agent unambiguous guidance on when to use this tool versus alternatives.

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

All Arcgis Marioncountyor toolsInspect
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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that specify when to use each. Even overlapping functions like ask_pipeworx varieties are well-differentiated by mode (casual vs grounded vs multi-source).

Naming Consistency4/5

Most tool names follow a verb_noun snake_case pattern (e.g., query_layer, resolve_entity), but a few deviate with single-word verbs (forget, remember, recall) or noun_noun (layer_info). The pattern is mostly consistent with minor exceptions.

Tool Count3/5

With 33 tools, the count is high and borders on heavy. However, the tools span multiple domains (GIS, financial data, prediction markets, memory, subscriptions), and each serves a unique role, so the count is justifiable but could be streamlined.

Completeness4/5

The tool set covers a broad range of data access and analysis tasks relevant to the inferred domain of a multi-purpose assistant. While the ArcGIS portion is limited, the overall surface is well-populated with few obvious gaps.

Resources