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Glama

Arcgis Peterborough

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

A4.7/5.0
Behavior5/5

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

Annotations already cover readOnly, idempotent, and openWorld traits, but the description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts while the rest still returns. This goes beyond the structured 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 but well-structured: lead sentence defining the tool, usage guidance, return value list, ecosystem scope limitation, and failure behavior. Every sentence adds distinct value, and the length is justified by the composite nature of the 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?

Despite lacking an output schema, the description enumerates all summary fields (is_latest, license, published_at, advisory_count, bundle_kb_min, etc.) and mentions per-advisory detail, links, and alternative versions. It also covers limitations and graceful degradation, making it fully sufficient for an agent to select and invoke correctly.

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%, with both package and version parameters already described clearly. The description does not add new syntax or format details beyond what the schema provides, so it sits at the baseline score of 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 opens with a specific composite check definition, naming both deps.dev and bundlephobia and their specific data contributions. It clearly specializes in npm package evaluation, which distinguishes it from sibling tools like scan_competitor_ai_presence or validate_claim.

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 explicitly states when to use the tool with examples ('is X safe / popular / small' or 'what does adding lodash cost me'). It also provides an exclusion for non-npm ecosystems, directing users to deps.dev:version for PyPI/Maven/Cargo/Go, giving clear when-not-to-use guidance.

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

Multiple tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying 5,767 tools with significant functional overlap. Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) also have blurred boundaries around edge detection and fill risk. The ArcGIS tools (search_datasets, layer_info, query_layer) are distinct, but the non-ArcGIS tools dominate and create confusion.

Naming Consistency2/5

The naming conventions are inconsistent across the set. Some tools use verb_noun (ask_pipeworx, query_layer, search_datasets, list_subscriptions), some use bare verbs (forget, recall, subscribe, unsubscribe), and others use descriptive multi-word names (polymarket_fill_risk, scan_competitor_ai_presence, generate_llms_txt). The ask_pipeworx family and polymarket_* family are internally consistent, but the overall set mixes styles without a clear pattern.

Tool Count2/5

34 tools is heavy for a server that appears to be an ArcGIS data server but includes a massive Pipeworx data-research and prediction-market subsystem. The ArcGIS portion only has 3 tools (search_datasets, layer_info, query_layer), while the rest form a separate general-purpose research/betting toolkit. The count feels bloated and unfocused relative to the server's stated name.

Completeness2/5

The ArcGIS surface is incomplete: search_datasets, layer_info, and query_layer offer no update/create/delete or metadata exploration beyond one layer at a time. The Pipeworx portion is broad but lacks clear lifecycle coverage for subscriptions (create/cancel works, but no update), and the memory tools (remember/recall/forget) are peripheral. The set feels like an accidental aggregation of unrelated domains rather than a complete surface for one purpose.

Resources