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Arcgis Delawareco

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?

Beyond the readOnly/openWorld/idempotent annotations, the description discloses key behavioral traits: it fans out across two sources, returns a structured summary block, handles partial failures gracefully, lists sources_failed on timeout, and warns that the first bundlephobia measurement may take 5-30s. This is rich context.

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 longer than typical but each sentence adds value: purpose, usage, return fields, limitations, and failure behavior. It is well-structured and front-loaded with the main purpose, though slightly dense.

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 there is no output schema, the description fully covers return values (summary block fields, advisories, links, alternative versions), ecosystem scope, and degradation behavior. This is complete for a composite tool of this complexity.

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 coverage is 100%, so the baseline is 3. The description does not add parameter-specific meaning beyond the schema; it mentions npm-only scope and default version behavior, both already stated in the schema. No extra examples or constraints are provided.

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 explicitly states the tool's purpose: a composite 'should I add this npm package' check that aggregates data from deps.dev and bundlephobia. It clearly identifies the resource (npm package) and the specific data sources, distinguishing it from any sibling tool.

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?

It provides explicit usage triggers ('Use whenever an agent asks...') and names an alternative for non-npm ecosystems ('PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). This fully covers when to use and when not to use.

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

All Arcgis Delawareco toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Many tools have overlapping or redundant purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical in routing, and several polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) all surface trading opportunities with similar outputs. The three ArcGIS tools are distinct but buried among dozens of unrelated data/meta tools, making selection confusing.

Naming Consistency3/5

All names use snake_case, which is consistent, but the verb/noun pattern is inconsistent. Some are verb_noun (query_layer, resolve_entity), some are noun phrases (entity_profile, polymarket_edges, recent_alerts), and some are bare verbs (recall, remember, forget, subscribe). The naming style is readable but not predictably patterned.

Tool Count1/5

34 tools is already high, but the severe issue is that only 3 of them (search_datasets, query_layer, layer_info) relate to the server's stated ArcGIS Delaware County purpose. The other 31 are Pipeworx data, memory, subscription, and prediction-market tools, which is a blatant scope mismatch. The tool count is not appropriate for the advertised server domain.

Completeness1/5

For an ArcGIS Delaware County GIS server, the surface is extremely thin: only search, query, and layer metadata exist. There are no tools for editing features, uploading data, managing layers, or exporting maps. Conversely, the Pipeworx tools form a broad but fragmented domain with many monitoring and meta-tools but no clear end-to-end workflow. The set is severely incomplete for its apparent dual purpose.

Resources