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Glama

Latam Validate

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?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant context: partial failure behavior (bundlephobia can take 5-30s, sources_failed will list it), graceful degradation, and the composite nature of the call. 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 relatively long but each sentence provides useful information. It is front-loaded with the main purpose and then details behavior. Could be slightly more concise, but no wasted content.

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 thoroughly documents the return fields (summary block with is_latest, license, etc., per-advisory detail, links, alternative versions). It also covers edge cases (scoped packages, default version, timeout behavior). Very complete for a read-only tool.

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% for both parameters. The description adds context that 'package' accepts scoped names (e.g., '@types/node') and that 'version' defaults to latest when omitted. This adds marginal value beyond the schema descriptions.

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 is a composite check for evaluating npm packages, covering license, advisories, version history, and bundle size. It distinguishes from sibling tools by mentioning NPM ecosystem only and referencing 'deps.dev:version' for other ecosystems.

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 says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes that PyPI/Maven/Cargo/Go fall under a different tool ('deps.dev:version'). This provides clear when-to-use and alternative 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

A3.6/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route the same 5,702 tools with only subtle differences, and ai_visibility_check is essentially a single-entity subset of scan_competitor_ai_presence. The memory trio (remember/recall/forget) and subscription tools also sit awkwardly alongside the data-query tools, making selection genuinely ambiguous.

Naming Consistency2/5

Naming mixes verb_noun (validate_cnpj, resolve_entity, compare_entities), noun_verb (bet_research, entity_profile, recent_changes), and bare nouns with inconsistent suffixes (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk). The ask_pipeworx family uses inconsistent qualifiers (beta vs grounded), and validate_* is used for both checksum-only tools and full lookups.

Tool Count2/5

36 tools is excessive for a server ostensibly named 'Latam Validate' — only 5 tools relate to LATAM validation while the rest form a sprawling general-purpose data and prediction-market platform. Many tools could be consolidated (the ask_pipeworx family, the polymarket_* family, the memory trio), suggesting the set is over-scoped for any single agent's typical workflow.

Completeness2/5

For the stated LATAM validation purpose, coverage is thin: only Brazil (CPF/CNPJ/CEP/banks) and Mexico (CLABE) are covered, with no validators for other LATAM jurisdictions. For the broader Pipeworx data platform, the surface is extensive but has notable gaps — grounded retrieval, claim verification, and arbitrage tools exist, yet many LatAm-specific data sources and common validation formats (RFC, RUT, DNI, CURP) are absent.