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Glama

Pvgis

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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: bundlephobia's first measurement can take 5-30s, sources_failed lists timeouts, partial failures degrade gracefully. No contradictions 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?

The description is front-loaded with the core purpose and packs significant detail into two paragraphs. While not ultralight, every sentence adds value. There is no waste, but could be slightly tighter without losing information.

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?

No output schema is provided, so the description must explain return values. It does so thoroughly, listing all fields in the summary block, advisory details, links, and alternative versions. It also covers edge cases like partial failures and timing, making the tool's behavior fully understandable.

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% and includes descriptions for both parameters. The description adds minimal extra meaning beyond the schema, such as mentioning scoped packages and default version behavior, but it does not compensate for any gaps since there are none. Baseline 3 is appropriate.

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 tool's verb and resource: 'Composite should I add this npm package to my project check in ONE call'. It distinguishes from siblings by noting that the tool only covers the NPM ecosystem, and for other ecosystems to use deps.dev directly.

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 specifies when to use: 'Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. Also provides exclusions: 'PyPI / Maven / Cargo / Go fall under deps.dev:version directly'. This is clear and actionable.

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

B3.4/5.0
Disambiguation3/5

Multiple tools serve overlapping query purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with subtle differences that are hard to distinguish without careful reading. Similarly, prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges) have overlapping scopes.

Naming Consistency2/5

Names are highly inconsistent: verb_noun (ask_pipeworx, resolve_entity), noun_descriptive (entity_profile, polymarket_arbitrage), and simple nouns (tmy, pvgis). No clear pattern emerges, making it hard to anticipate tool names.

Tool Count3/5

34 tools is borderline high for a single server. While some utility tools (remember, forget) are justified, many tools are very narrowly scoped (tmy, generate_llms_txt) and could be merged or omitted.

Completeness3/5

The server covers a wide breadth (data querying, prediction markets, solar energy, AI visibility) but feels like a collection of unrelated domains. Core operations for data querying are present, but the solar tools (monthly_radiation, pv_performance) seem orphaned from the rest.

Resources