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Glama

Arcgis Abbotsford

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 readOnly/idempotent annotations, the description reveals important real-world behavior: it fans out to multiple services, handles partial failures gracefully via a `sources_failed` field, and warns about 5-30s latency on first measurement. This adds significant context that annotations alone don't provide, and nothing contradicts the 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 long but every sentence provides distinct value: purpose, trigger scenarios, output fields, ecosystem limitation, and failure behavior. It's front-loaded with the core purpose and structurally organized, though slightly dense for quick scanning.

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 thoroughly enumerates the returned summary block, per-advisory detail, links, and alternative versions. It also covers edge cases like timeouts, partial failures, and ecosystem scope, making it effectively complete for an agent to decide invocation.

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?

The input schema already documents both parameters with 100% coverage, including scoped packages and default version behavior. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline but does not exceed it.

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 identifies a composite dependency-scanning tool for npm packages, naming deps.dev and bundlephobia as specific data sources. The verb 'scan' plus the question it answers ('should I add this npm package') leaves no ambiguity and distinguishes it 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 states when to use the tool: 'Use whenever an agent asks "is X safe / popular / small"' and provides concrete examples. It also gives an exclusion (non-NPM ecosystems) and points to an alternative (deps.dev:version directly), making the decision boundary 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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all provide data lookup/research. The PolyMarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also creates boundary confusion despite varied signals.

Naming Consistency3/5

Most tools use snake_case, but the naming pattern is mixed: some are imperative verb phrases (query_layer, validate_claim), while others are noun phrases (entity_profile, recent_alerts, layer_info). Descriptions are readable overall, but there is no consistent verb_noun convention across the set.

Tool Count2/5

34 tools is high and the vast majority are unrelated to the server's stated ArcGIS Abbotsford purpose. The set appears to be a generic Pipeworx data/prediction-market toolkit with only a few GIS-specific tools, making the count excessive for the declared scope.

Completeness2/5

For the ArcGIS Abbotsford domain, the surface is severely incomplete: only search_datasets, query_layer, and layer_info cover GIS functionality, lacking update/delete/create operations or broader dataset management. For the actual Pipeworx domain, coverage is decent, but that does not match the server name.

Resources