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Glama

Arcgis Puyallup

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, etc.), the description discloses composite fan-out behavior, partial failure degradation, a specific 5-30s timeout for bundlephobia, and the 'sources_failed' field. This adds substantial behavioral context without contradicting annotations.

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-structured, starting with the core value proposition, then usage triggers, return format, and limitations. Each sentence serves a purpose, though it is longer than average; still, it earns its place for a composite tool.

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?

Even without an output schema, the description enumerates all return fields, notes partial failure behavior, ecosystem restrictions, and performance caveats. This makes the tool's behavior fully predictable for an agent.

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% for both parameters (package and version), so the schema already provides full descriptions. The tool description does not add parameter-specific meaning beyond what's already in the schema, landing at the baseline 3.

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 composite purpose: a 'should I add this npm package' check aggregating deps.dev and bundlephobia. It names specific data sources and return fields, distinguishing it from any sibling tools. The verb 'scan' and resource 'dependency' are specific.

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 trigger phrases are given ('is X safe / popular / small', 'what does adding lodash cost me'). It also states the NPM-only scope and directs other ecosystems to 'deps.dev:version directly', providing clear when-not/alternative guidance.

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

All Arcgis Puyallup toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve research questions through the same router, and the five polymarket_* tools plus bet_research form a dense prediction-market cluster. Even with detailed descriptions, an agent choosing between these near-synonyms would frequently need extra reasoning or make the wrong pick.

Naming Consistency2/5

Names mix product-prefixed verbs (ask_pipeworx, polymarket_arbitrage), generic verbs (remember, forget, recall, subscribe), and noun phrases (entity_profile, layer_info, recent_alerts). There is no consistent verb_noun or prefix convention across the set, making the tool surface feel patchwork rather than systematically named.

Tool Count2/5

With 34 tools, the count is already on the heavy side, but it is especially mismatched with the server name 'Arcgis Puyallup': only search_datasets, query_layer, and layer_info actually belong to that GIS domain. The rest are a broad Pipeworx research and prediction-market platform, so the set feels bloated and off-scope for the apparent purpose.

Completeness3/5

The ArcGIS read-only surface is minimally reasonable: search, schema inspection, and querying cover basic open-data consumption. The Pipeworx side is quite rich, with memory, subscriptions, lookups, grounded verification, and discovery, but the named GIS domain is thinly served and lacks obvious capabilities like listing all datasets or browsing layers without a keyword. Overall, coverage is uneven and hard to evaluate cleanly because the server mixes two unrelated purposes.

Resources