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Glama

Maptiler

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

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description details fan-out to two services, bundlephobia timing (5-30s), graceful degradation, and sources_failed reporting. No contradiction with 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?

Well-structured and informative, but somewhat verbose. Could be tightened without losing clarity. Front-loaded with purpose and usage.

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 no output schema, description fully explains return content (summary fields, advisories, links, versions) and edge cases (timeout, partial failure). Covers all necessary context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds value: for 'package' it notes scoped packages are accepted; for 'version' it states default to latest. Slightly more detail would be ideal, but sufficient.

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 'Composite "should I add this npm package to my project" check in ONE call' and specifies it scans across deps.dev and bundlephobia, distinguishing it from sibling tools like scan_competitor_ai_presence or geocode.

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 guides when to use: 'Use whenever an agent asks "is X safe / popular / small"' and when not: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' Also mentions partial failures and timing.

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

All Maptiler toolsInspect
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TDQS

B3.4/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap, e.g., ask_pipeworx vs ask_pipeworx_grounded vs deep_research all for data lookup, and multiple Polymarket tools. Descriptions are detailed enough to distinguish, but the set is confusing.

Naming Consistency2/5

Naming is highly inconsistent: snake_case (ai_visibility_check), verb_noun (resolve_entity), underscores (ask_pipeworx), and domain-specific prefixes (polymarket_). No consistent pattern across tools.

Tool Count2/5

With 38 tools spanning geospatial, data lookup, prediction markets, memory, feedback, etc., the count is too high for a coherent server. Many tools are one-off and unrelated to the server's name (Maptiler).

Completeness2/5

The tool set covers many areas but lacks obvious gaps for each domain (e.g., MapTiler only has 4 tools). The overall surface is a collection of unrelated features, not a cohesive domain, so completeness is poor for any single purpose.

Resources