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Glama

Sanctions Screening

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses composite behavior, partial-failure degradation, potential 5-30s latency on first bundlephobia measurement, and the sources_failed field. This adds performance and error-handling context not provided by 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 every clause earns its place: purpose, use cases, return fields, ecosystem scope, and timeout behavior. It is front-loaded with the main purpose and avoids fluff.

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 enumerates the return summary block fields (is_latest, license, advisory_count, etc.), mentions per-advisory details and links, and explains graceful degradation with sources_failed. It also covers ecosystem limitations and version alternatives.

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?

The input schema documents both parameters with 100% coverage, including scoped-package acceptance and version default behavior. The description adds no extra meaning for the parameters, so the baseline of 3 applies.

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 a specific verb+resource: a composite 'should I add this npm package to my project' check that fans out to deps.dev and bundlephobia. It distinguishes itself from sibling tools by limiting scope to NPM and explicitly noting alternative endpoints 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?

Gives explicit usage cues: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also advises where non-NPM packages belong (deps.dev:version directly), helping choose the right path.

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

All Sanctions Screening toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are nearly identical, several prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) cover similar territory, and research/verification tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) blur together. The three sanctions tools are distinct, but the rest of the set creates frequent misselection risk.

Naming Consistency3/5

All tool names use snake_case, which is consistent, but the verb/noun ordering varies unpredictably (expectation vs entity_profile vs sanctions_screen vs scan_dependency). There is no dominant pattern like verb_noun, though names are readable.

Tool Count2/5

With 34 tools, the server is over-scoped, especially given its narrow 'Sanctions Screening' title. Many tools are duplicative (ask_pipeworx_beta duplicates ask_pipeworx; scan_competitor_ai_presence wraps ai_visibility_check), and the bulk are unrelated to the stated purpose. The count feels bloated.

Completeness3/5

The three sanctions tools (screen, entry, lists) cover the core read-only workflow well, but the server is mislabeled: most of the 34 tools are generic data/Pipeworx features unrelated to sanctions. For the broad data domain the set is fairly complete, but for the apparent 'Sanctions Screening' purpose it is both over- and under-scoped, with no bulk screening or list management.

Resources