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Glama

Tavily

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 the read-only and idempotent annotations, the description discloses critical behavioral details: it fans out to two external services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30 seconds, and the sources_failed field reports timeouts. This is comprehensive transparency that aids the agent in setting expectations.

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 a single, dense paragraph that front-loads the core purpose and provides all necessary information in a structured flow: purpose, usage, return value, failure behavior, and ecosystem limitations. Every sentence adds value, with no redundant 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?

Given the complexity of the tool (multi-source aggregation) and the absence of an output schema, the description fully enumerates the return fields, failure behavior, and timing expectations. This is more than sufficient for an agent to understand what the tool does and what to expect from its results.

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 already provides 100% coverage for both parameters, including descriptions for 'package' and 'version'. The description adds minimal additional parameter context (e.g., NPM ecosystem only, scoped packages accepted) which mostly mirrors the schema, so it does not significantly elevate the baseline score.

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 as a composite 'should I add this npm package' check, detailing the specific data sources and the types of questions it answers. This distinguishes it from sibling tools like deep_research or validate_claim by focusing on npm dependency evaluation.

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 the tool ('Use whenever an agent asks...') and provides clear exclusions for non-NPM ecosystems, directing to deps.dev:version directly. This gives strong guidance on when to use this tool versus alternatives.

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
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same 5,529 tools, and deep_research overlaps for broad questions. The six prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have heavily overlapping purposes, making misselection likely.

Naming Consistency4/5

All names use snake_case, which is consistent, and most are verb-first (search, extract, remember, resolve_entity, validate_claim). However, several are noun-phrases (entity_profile, polymarket_arbitrage, recent_alerts, pipeworx_feedback), breaking the verb_noun pattern. The deviations are minor but present.

Tool Count2/5

At 33 tools, the set is well above the 25-tool threshold for 'too many'. The server also spans several unrelated domains—web search, Pipeworx structured data, prediction markets, memory, subscriptions, AI visibility—making the count feel excessive for a coherent purpose.

Completeness4/5

Each sub-domain is well covered: search has search/extract/search_within, prediction markets have research/arbitrage/edges/fill-risk/tracking, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. Minor gaps exist (e.g., no way to edit a subscription's parameters, no direct SEC filing content viewer), but agents can work around them via ask_pipeworx.

Resources