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Glama

Design Feeds

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

A4.8/5.0
Behavior5/5

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

Discloses that it fans out across two external services, that bundlephobia's first measurement can take 5-30s, and that partial failures degrade gracefully with sources_failed listed. This adds context beyond annotations like readOnlyHint and idempotentHint.

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?

Description is relatively long but packed with essential information. It front-loads the core purpose and then details behavior, timing, and limitations. Could be slightly trimmed but remains efficient.

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 tool with no output schema, the description provides a comprehensive list of return fields and explains partial failure behavior. It covers all necessary context for an agent to understand inputs, behavior, and outputs.

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?

Both parameters are described in schema (100% coverage) and the description adds that version defaults to latest published, which is not in schema. This adds meaning beyond the schema.

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 it performs a composite check for adding npm packages, combining deps.dev and bundlephobia data. It distinguishes from siblings like 'validate_claim' or 'scan_competitor_ai_presence' 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: when an agent asks 'is X safe/popular/small' or 'what does adding lodash cost me'. Also specifies NPM-only and directs other ecosystems to deps.dev:version, providing clear exclusions.

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

All Design Feeds toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly the same routing purpose, with the beta variant currently identical to the stable version. Similarly, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all target prediction-market analysis with fuzzy boundaries, and ai_visibility_check is effectively wrapped by scan_competitor_ai_presence. The detailed descriptions help, but an agent would frequently need to read large descriptions to pick correctly.

Naming Consistency3/5

All tool names use snake_case and many are readable verb_noun constructions (list_feeds, read_feed, fetch_feed, resolve_entity, validate_claim). However, conventions are mixed: some are bare verbs (remember, recall, forget), some are adjective_noun (recent_alerts, recent_changes), and some use domain prefixes or generic labels (ask_pipeworx, polymarket_edges, entity_profile). The naming is not chaotic, but it does not follow a single predictable pattern.

Tool Count2/5

With 34 tools, the server is overstuffed for its stated 'Design Feeds' name, which only accounts for list_feeds, read_feed, and fetch_feed. The vast majority of tools belong to unrelated domains like investment research, prediction markets, entity resolution, and memory management, making the overall surface feel like a general-purpose toolkit rather than a focused design feed server.

Completeness3/5

For the design-feed domain, only list/read/fetch operations exist; there are no create, update, or delete feed-management tools, and the subscribe tool does not support design feeds. For the broader Pipeworx/research surface, coverage is quite strong with memory, subscriptions, discovery, grounded lookup, research, comparison, and claim verification. The mix of two very different domains leaves notable gaps in each.

Resources