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Glama

exchange

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses important behavioral traits: it is a composite fan-out call, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts while the rest still returns. It also states the ecosystem limitation and output shape.

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 highly structured, leading with purpose, then use cases, then return fields, then ecosystem constraints, and finally failure behavior. Every sentence provides operational value, and no fluff is present.

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?

For a composite tool with no output schema, the description is remarkably complete: it details the return block fields, per-advisory details, links, alternative versions, ecosystem scope, failure modes, and latency expectations. This is sufficient for an agent to correctly invoke and interpret the tool.

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%, so the baseline is 3. The description adds little parameter-specific meaning beyond the schema; it confirms the purpose but does not explain parameter semantics in more depth than the already-detailed 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 starts with a specific, composite purpose: 'should I add this npm package to my project' check in ONE call, and clearly enumerates the fan-out sources (deps.dev and bundlephobia) and the data categories. It is unambiguous and distinguishes this tool from siblings by its scope and aggregation.

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?

It explicitly states when to use: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides an exclusion and alternative: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'.

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

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions. Potential confusion exists between similar tools like ask_pipeworx and ask_pipeworx_grounded, but the descriptions differentiate them well. The high number of tools across multiple domains could still cause some ambiguity, but overall an agent can distinguish them.

Naming Consistency3/5

Naming patterns are mixed. Some subgroups follow consistent patterns (polymarket_*, pipeworx_*, get_*), but overall the set includes verb_noun (list_subscriptions, get_rate, remember) and noun_verb (entity_profile, deep_research) without a unified convention. The mixture of imperative and descriptive names reduces predictability.

Tool Count2/5

34 tools is excessive for a single server, bundling unrelated domains (currency, memory, data query, prediction markets, dependency scanning). This scope could be split into multiple servers for better coherence. The high count may overwhelm agents and increases the risk of misselection.

Completeness3/5

Each sub-domain (e.g., currency, memory, Polymarket) has reasonable coverage with common operations present. However, the server name 'exchange' is misleading—it suggests a narrower focus. Some gaps exist (e.g., no batch currency conversion, no direct Polymarket market detail tool). Overall, the set covers many tasks but lacks a unified purpose.

Resources