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Glama

Data Nola

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?

Description adds value beyond annotations: explains partial failures, slow bundlephobia first measurement (5-30s), and graceful degradation with sources_failed. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructiveness.

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?

Single paragraph, dense but well-organized. Front-loaded purpose and data sources. Could be slightly more structured, but no wasted words.

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, but description fully enumerates return fields (summary block, per-advisory detail, links, alternative versions) and explains failure modes. Holistically covers what the agent needs to know.

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 practical context: scoped packages accepted for 'package', version defaults to latest. Slightly above baseline.

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?

Description clearly identifies the verb ('composite check'), resource (npm package), and data sources (deps.dev and bundlephobia). Distinguishes from siblings by noting NPM-only scope and directing other ecosystems to deps.dev:version.

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 states when to use ('is X safe / popular / small' or 'what does adding lodash cost me') and when not (non-NPM ecosystems). Provides alternative ('deps.dev:version directly'), meeting the highest standard.

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

A3.9/5.0
Disambiguation3/5

Several tools overlap in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and composite tools like entity_profile, recent_changes, compare_entities, and validate_claim draw on similar company-data sources. However, the descriptions are extremely detailed with explicit usage guidance, so an agent can usually pick correctly despite the overlap.

Naming Consistency4/5

Most tool names follow a verb_noun snake_case pattern (ask_pipeworx, compare_entities, list_subscriptions, validate_claim), and domain prefixes like nola_, pipeworx_, and polymarket_ help group tools. Minor deviations such as entity_profile, recent_changes, and polymarket_edges are still readable and do not undermine the overall pattern.

Tool Count2/5

At 34 tools, the set spans far beyond the 'Data Nola' name: New Orleans open data, general Pipeworx data research, Polymarket arbitrage, npm dependency scanning, AI visibility, memory, and subscriptions. This breadth makes the surface heavy and forces agents to triage a large and heterogeneous toolset.

Completeness4/5

The broader research platform is well covered: question answering, entity resolution, profiles, comparisons, claim validation, subscriptions, memory, and NOLA dataset querying all have solid lifecycle support. Minor gaps exist—no NOLA dataset metadata or write tools, no Polymarket order execution—but these are acceptable for a read-only data server.

Resources