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Glama

Dbnomics

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

A4.9/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: explains partial failures, the 5-30s first measurement for bundlephobia, and the sources_failed list. It also confirms the idempotent, read-only nature, consistent 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: purpose first, then details, usage guidance, limitations, and edge cases. Every sentence adds value. No redundant 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?

Even without an output schema, the description lists all return fields (summary block, advisories, links, alternatives). It covers inputs, behavior, partial failures, and timestamp expectations, making it complete for the tool's complexity.

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%, so baseline is 3. The description adds value by providing an example of scoped packages (`@types/node`) and clarifying that omitting version defaults to latest. This adds meaning beyond the schema.

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 purpose: a composite check for adding an npm package, covering license, advisories, version history, and bundle size. It distinguishes itself from sibling tools by being specific to npm dependencies in v1.

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 when to use this tool: when an agent asks about safety, popularity, size, or cost of adding a package. It also notes the NPM-only scope and mentions fallback tools for other ecosystems.

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

All Dbnomics toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, while bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities. The detailed descriptions mitigate some confusion, but an agent must read carefully to avoid selecting the wrong member of these overlapping groups.

Naming Consistency3/5

The set is consistently snake_case and many tools follow verb_noun conventions like list_datasets, get_series, find_series, and validate_claim. However, a large minority are noun-led names such as entity_profile, deep_research, bet_research, pipeworx_feedback, and polymarket_arbitrage, so there is no single predictable naming pattern across the whole server.

Tool Count2/5

At 36 tools, this is well above the 25+ threshold for an over-heavy surface, and the set spans DBnomics data, prediction markets, memory, subscriptions, npm auditing, llms.txt generation, and AI-visibility checks. Several near-duplicate meta-tools could be consolidated, and unrelated domains would be better split into separate servers.

Completeness4/5

Core workflows are well covered: DBnomics browse/fetch/search, company resolve/profile/compare/change, prediction-market discovery and fill-risk, and memory/subscription lifecycles are all represented. Minor gaps exist, such as no subscription-update operation and no direct single-dataset detail fetch without listing, but agents can work around them.

Resources