Skip to main content
Glama

Madrid Events

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses latency (bundlephobia first measurement can take 5-30s) and partial failure behavior (sources_failed lists timed-out sources, rest still returns). It also notes ecosystem limitation. This adds substantial context over 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 well-structured: front-loaded with the core purpose, then usage, return details, limitations, and failure mode. Every sentence adds substantive information without redundancy. Dense but efficient for a composite tool.

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 no output schema, the description enumerates return fields, mentions failure modes, and specifies ecosystem scope. It covers the important operational details (latency, partial failures) making it complete for an agent to invoke and interpret 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?

Schema coverage is 100%, so baseline is 3. The description adds no extra parameter details beyond what the schema provides; it does mention the default latest version and npm-only, but those are already in the schema descriptions. No additional semantic value.

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 npm package evaluation check, fanning out to deps.dev and bundlephobia. It uses specific terms like 'should I add this npm package' and lists the data sources, distinguishing it from sibling tools like scan_competitor_ai_presence.

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: whenever an agent asks about safety, popularity, or size of an npm package. Also provides an alternative: for non-npm ecosystems, use deps.dev directly. This gives clear when/when-not guidance.

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

All Madrid Events toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The set contains several heavily overlapping clusters: three ask_pipeworx variants (with ask_pipeworx_beta explicitly identical to ask_pipeworx right now) and six polymarket-related tools that all orbit edge detection, arbitrage, and fill risk. An agent choosing among bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_kalshi_spread would have a hard time picking the right one.

Naming Consistency3/5

Most tools use readable snake_case, so the naming is not chaotic. However, the pattern is mixed: some are verb_noun (compare_entities, resolve_entity), some are brand-style (ask_pipeworx, ask_pipeworx_beta), and some are noun-only phrases (events, polymarket_arbitrage, pipeworx_trending). It is consistent in casing but not in structural convention.

Tool Count2/5

32 tools is well beyond the usual well-scoped range, and the count is inflated by multiple near-duplicate clusters for querying, prediction markets, and memory/subscription utilities. For a server named Madrid Events, this is especially disproportionate since only one tool actually relates to Madrid events.

Completeness2/5

Relative to the Madrid Events name, the domain coverage is almost entirely missing: only events addresses the stated purpose, and it is read-only with no detail view, booking, or management operations. If interpreted as the broader Pipeworx platform, coverage is richer, but the server's stated identity makes the gap severe.

Resources