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Glama

Openreview

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=true, idempotentHint=true, and destructiveHint=false. The description adds rich behavioral context: it fans out across two external services, mentions partial failure (sources_failed list), and notes that bundlephobia's first measurement can take 5-30 seconds. 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?

The description is well-structured, starting with a concise summary, then usage guidance, return format, ecosystem note, and timing details. It is informative without being verbose, though slightly longer than strictly necessary.

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 no output schema, the description fully covers return values (summary block, per-advisory details, links, alternative versions) and edge cases (partial failures, timing for new versions). It is complete for a composite tool of this complexity.

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 description coverage is 100% with clear descriptions of both parameters. The description indirectly adds context about default version behavior but does not provide additional meaning beyond the schema. Baseline of 3 is appropriate when schema is already comprehensive.

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 performs a composite check for evaluating npm packages by querying deps.dev and bundlephobia. It explicitly distinguishes itself from siblings by focusing on package quality assessment, which none of the listed sibling tools address.

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?

The description explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and also specifies the ecosystem limitation: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This provides clear when-to-use and 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

A3.5/5.0
Disambiguation2/5

The tool set includes both OpenReview-specific tools (e.g., get_paper, list_submissions) and a large number of unrelated tools for Pipeworx, Polymarket, and SEC filings. While individual descriptions are clear, the mix of domains creates confusion about which tools to use for a given task, leading to potential misselection.

Naming Consistency2/5

Tool names follow inconsistent patterns: some use underscore_case (ai_visibility_check, generate_llms_txt), some are verb_noun (get_paper, list_venues), and others use descriptive phrases (ask_pipeworx_grounded, polymarket_arbitrage). The lack of a unified naming scheme makes the tool set feel disjointed.

Tool Count2/5

With 37 tools, the count is too high for a server supposedly focused on OpenReview. Only about 6 tools are directly related to OpenReview; the rest are for unrelated domains like prediction markets, data retrieval, and memory management. The scope is unclear and overloaded.

Completeness2/5

For the OpenReview domain, the tool set covers basic retrieval (get_paper, search_notes, list_submissions) but lacks operations like creating or updating notes, which are common in a review platform. The inclusion of many non-OpenReview tools does not compensate for these gaps, leaving the surface incomplete for its stated purpose.

Resources