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Arcgis Washingtoncomn

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

A4.6/5.0
Behavior5/5

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

The description goes beyond annotations by disclosing graceful degradation on partial failures, the 5-30s latency for first bundlephobia measurement, and the sources_failed field behavior. It also clarifies the NPM-only limitation. No contradiction with the readOnly/idempotent hints.

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 dense but well-organized, front-loading the core purpose before diving into sources, usage, return values, and limitations. Every sentence provides useful information, though the length is substantial; still concise relative to 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 the tool's complexity, no output schema, and multiple data sources, the description fully covers the return structure (summary block fields, advisories, links, alternatives), usage boundaries, and failure behavior. It gives an agent everything needed to invoke and interpret the tool.

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 description coverage is 100%, so the baseline is 3. The description does not significantly add parameter semantics beyond the schema; it reiterates that version defaults to latest, which is already in the schema. However, it does provide context on how parameters relate to the overall output.

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 dependency evaluation check for npm packages, combining deps.dev and bundlephobia data. It uses a specific verb ('scan') and resource ('dependency'), and distinguishes itself from siblings by focusing on package adoption decisions.

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 when NOT to use it (non-npm ecosystems) and directs users to deps.dev:version directly for those cases.

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

All Arcgis Washingtoncomn toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

The ArcGIS tools (query_layer, layer_info, search_datasets) are clearly distinct, but the Pipeworx family has heavy overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools can all appear as plausible entry points for a similar data lookup task. The memory and subscription tools are well separated, but the router-style tools create real ambiguity.

Naming Consistency4/5

Almost all tools use lowercase snake_case names with a verb-first pattern (ask_pipeworx, query_layer, subscribe, remember) or clear noun descriptors (entity_profile, layer_info, polymarket_edges). A few names are more cryptic (recall, forget, resolve_entity) but they still follow the same style. No mixed camelCase or inconsistent separators.

Tool Count2/5

34 tools is well beyond what the apparent ArcGIS Washington County purpose needs; only three tools actually concern GIS data. The rest are a broad Pipeworx research suite, memory, subscriptions, feedback, and AI-visibility probes. This makes the surface feel like two or three unrelated servers grafted together rather than one scoped package.

Completeness3/5

For the ArcGIS slice, you can discover, inspect, and query layers, but there is no write or create capability, no field-wise editing, no map/feature export, and no feature-level CRUD. The Pipeworx data side is more comprehensive, but the overall server confuses its purpose. The mixed-domain coverage leaves the GIS part only a thin slice of the offered features.

Resources