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Glama

Oeis

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?

Annotations declare readOnlyHint, openWorldHint, and idempotentHint. The description goes beyond by explaining partial failure behavior (bundlephobia timeout, sources_failed listing) and multi-source fan-out. No contradiction with 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 front-loaded with the core purpose and uses efficient multi-sentence structure. Each sentence adds distinct value (purpose, data sources, usage guidance, ecosystem scope, failure behavior). No wasted words.

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?

Despite lacking an output schema, the description fully documents the return structure (summary fields, per-advisory details, links, alternatives) and handles edge cases like partial failures. Given the tool's complexity, this is comprehensive.

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 baseline 3 is appropriate. The description confirms the npm ecosystem and default version behavior but does not add significant new meaning beyond what schema descriptions already provide.

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 is a composite check for deciding whether to add an npm package, specifying it combines deps.dev and bundlephobia data. It differentiates from other ecosystems by noting NPM-only scope in v1, and no sibling tool offers the same functionality.

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 instructs to use when agent asks about safety, popularity, or size of an npm package. Provides clear when-not-to-use for other package ecosystems by directing to deps.dev:version directly. This is actionable guidance.

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

All Oeis toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap, especially among ask_pipeworx variants (ask_pipeworx, ask_pipeworx_grounded, ask_pipeworx_beta) and prediction market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research). While descriptions help distinguish them, the boundaries are somewhat fuzzy.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use verb_noun (search_sequence, compare_entities), others use noun_verb (entity_profile, recent_changes), and some have prefixes (pipeworx_feedback, pipeworx_trending) while others do not (remember, forget). There is no uniform pattern, which reduces predictability.

Tool Count2/5

The server name 'Oeis' suggests a focus on integer sequences, but only 2 of 33 tools are OEIS-related. The remaining tools cover a vast array of unrelated domains (data retrieval, prediction markets, memory, subscriptions). This mismatch between name and scope makes the tool count feel excessive and poorly scoped.

Completeness4/5

Within the broad domains covered, the tool surface is quite complete. For memory, there are remember/recall/forget; for subscriptions, subscribe/unsubscribe/list_subscriptions/recent_alerts; for data retrieval, multiple entry points. Minor gaps exist, such as lack of OEIS sequence contribution tools, but overall the set is well-rounded.

Resources