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hackernews

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.

TDQS

A4.6/5.0
Behavior5/5

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

Beyond readOnly/idempotent/non-destructive annotations, the description discloses fan-out behavior, partial failure degradation ('sources_failed will list it if it times out, the rest still returns'), and timing caveat ('first measurement ... can take 5-30s'). This adds meaningful behavioral context.

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?

Although lengthy, every sentence conveys necessary information: purpose, usage, return fields, ecosystem limits, and failure behavior. The structure is front-loaded with the core purpose and uses separators for clarity.

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 no output schema, the description lists all summary fields and explains failure modes, timeout behavior, and ecosystem scope. It is sufficiently complete for an agent to understand what to expect and when to use 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 coverage is 100% and describes both parameters. Description adds no new semantic details beyond what the schema already states, but it does reinforce the default version behavior. Baseline 3 is appropriate.

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 opens with a specific composite purpose: 'should I add this npm package to my project' check, and details the fan-out across deps.dev and bundlephobia. It clearly indicates the resource (npm package) and the action (scan), distinguishing it from siblings by focusing on dependency metrics.

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?

Explicit when-to-use guidance is given: 'Use whenever an agent asks ...' plus an explicit exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly,' steering users to an alternative for non-npm packages.

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

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, and deep_research also overlaps with them. The Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have fuzzy boundaries. An agent could easily pick the wrong meta-tool or duplicate functionality.

Naming Consistency3/5

All names are snake_case, but the pattern is mixed: some are verb_noun (get_item, list_subscriptions, resolve_entity), some are noun_noun (entity_profile, pipeworx_feedback, polymarket_edges), some are adjective_noun (deep_research, recent_alerts), and a few are single verbs (forget, recall, remember, subscribe). This is readable but not a consistent convention.

Tool Count2/5

With 36 tools, this is far too many for a server named 'hackernews'. The bulk of the tools concern Pipeworx data research, prediction markets, memory, and subscriptions — unrelated to the server's apparent purpose. Many of these could be split into separate servers, and the HN-specific functionality would be better served by a focused set of ~5-8 tools.

Completeness2/5

For the Hacker News domain implied by the server name, the surface is incomplete: there are read-only tools (search, top stories, item/comments) but no write functionality (submit, comment, vote) and no user profile access. The broader data-research capabilities are fairly comprehensive, but that does not rescue the server's coherence given its stated name.

Resources