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Arcgis Lacounty

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?

Annotations already declare readOnly, idempotent, openWorld, non-destructive. The description adds valuable context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s with sources_failed indicating timeouts. This goes beyond annotations.

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?

The description is dense yet every sentence earns its place: purpose, sources, use case, output contents, ecosystem limitations, and failure modes. It's front-loaded with the primary intent and remains readable despite its length.

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?

With no output schema, the description fully enumerates the return block fields, per-advisory details, links, alternative versions, and failure behavior. Given the composite complexity and multiple sources, this is complete.

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 covers both parameters (package name and optional version) with 100% coverage. The description adds no extra parameter nuance; it only references the version's default behavior which is already in the schema. Baseline 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 it's a composite 'should I add this npm package' check fanning out across deps.dev and bundlephobia. The verb 'check' plus the use-case framing ('is X safe / popular / small') makes the tool's purpose unambiguous and distinct from siblings.

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 provides when to use: whenever an agent asks about safety/popularity/size or cost of adding a package. It also states what NOT to use it for: non-NPM ecosystems fall under deps.dev:version directly, steering alternative tool usage.

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

All Arcgis Lacounty toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all handle natural-language data queries, with ask_pipeworx_beta currently identical to ask_pipeworx. discover_tools and suggest_questions both exist to help agents find tools, and the five Polymarket tools have subtle, hard-to-distinguish boundaries. Agents will frequently select the wrong tool without careful reading.

Naming Consistency4/5

Tool names are mostly lowercase snake_case with verb-noun structure (query_layer, search_datasets, resolve_entity), which is consistent and readable. However, some names break the pattern (entity_profile, layer_info, recent_changes, pipeworx_feedback) and the prefixes are not uniform. Still, the convention is predictable enough to navigate.

Tool Count2/5

34 tools is a heavy count, and the vast majority are unrelated to the server's stated 'Arcgis Lacounty' purpose. Only three tools (search_datasets, query_layer, layer_info) serve the named GIS domain, while the rest form a sprawling collection of data-lookup, prediction-market, and utility tools. This is a severe scope mismatch.

Completeness1/5

The tool surface is severely incomplete for an ArcGIS LA County server: no layer listing beyond keyword search, no metadata endpoints, no editing, no spatial operations. The broader tool set lacks a coherent domain, making coverage impossible to assess beyond noting the glaring absence of core GIS functionality.

Resources