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Arcgis Tucson

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

A4.7/5.0
Behavior5/5

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

Beyond annotations, description adds critical behavior: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, sources_failed will list timeouts. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single dense paragraph, front-loaded with purpose, every sentence adds value. No wasted words.

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 tool complexity (composite, multiple outputs), description covers return fields, failure modes, timing, and ecosystem scope. Complete for agent decision-making.

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 has 100% coverage; description adds meaning by explaining 'package' accepts scoped names and 'version' defaults to latest. Provides usage context beyond schema.

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?

Description clearly states it's a composite check for adding npm packages, fans out to multiple services, and distinguishes from siblings (NPM only in v1, other ecosystems use deps.dev:version).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit when-to-use ('is X safe/popular/small', 'what does adding lodash cost me'). Implicitly suggests alternatives for non-NPM ecosystems. Lacks explicit when-not-to-use but overall clear.

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

A3.7/5.0
Disambiguation3/5

While individual tool descriptions are detailed and specific, the set includes overlapping tools like ask_pipeworx and ask_pipeworx_grounded, and several prediction market tools with similar purposes (bet_research, polymarket_edges, polymarket_arbitrage). The broad range of unrelated domains means many tools are distinct, but some pairs are ambiguous.

Naming Consistency2/5

Tool names use snake_case but follow no consistent pattern. Some are verb_noun (ask_pipeworx, query_layer), some noun_verb (ai_visibility_check, entity_profile), and some have inconsistent structure (discover_tools, recent_alerts). The mix of conventions reduces predictability.

Tool Count1/5

33 tools is excessive for a server named 'Arcgis Tucson', as only 3 tools relate to ArcGIS (search_datasets, layer_info, query_layer). The rest span completely unrelated domains (Pipeworx data, Polymarket, memory, npm scanning, etc.), creating a severe mismatch between server name and tool functionality.

Completeness3/5

Considering the actual tool surface (a diverse data retrieval and prediction market analysis set), it is reasonably complete for common lookups (SEC, FDA, economics, news, bets). However, it lacks web search and the ArcGIS tools are minimal. The absence of a cohesive domain makes completeness hard to judge, but for the implied data-retrieval purpose, it's passable.

Resources