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Glama

Spain Tenders

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?

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses graceful degradation for partial failures, the 5-30s delay on first bundlephobia measurement, and the sources_failed field. These are valuable operational details not captured by schema or annotations.

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?

The description is dense but efficiently worded, front-loading the core composite check and then enumerating return fields. Slightly long but each sentence contributes context, with no filler.

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 the summary block fields, per-advisory details, links, and alternative versions. It also covers edge cases (scoped packages, partial failures). This provides a complete mental model for the agent.

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?

The schema description already fully covers both parameters. The description adds meaningful clarification: package accepts scoped names, version defaults to latest when omitted, and the return includes alternative versions—helping interpret the version param's purpose 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 the tool's function: a composite npm package evaluation check across deps.dev and bundlephobia. It uses a specific verb ('scan'), identifies the resource (npm package), and differentiates from sibling research tools by focusing on dependency vetting.

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: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides an exclusion boundary: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', guiding the agent to an alternative path.

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

All Spain Tenders toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (the beta currently matches stable exactly), and the Polymarket tools all circle around edge/arbitrage detection with fuzzy boundaries. The Spain tender tools and utility tools are distinct, but the overlapping clusters are enough to cause misselection.

Naming Consistency3/5

Names are uniformly snake_case and some families share clear prefixes (es_tender_*, ask_pipeworx_*, polymarket_*). However, conventions are mixed: verb-led names like compare_entities and subscribe sit alongside noun phrases like entity_profile and bet_research, so there is no consistent verb_noun pattern.

Tool Count2/5

34 tools is already in the heavy range, but the bigger problem is scope: a server named 'Spain Tenders' ships 34 tools, only 3 of which are actually Spanish-procurement tools. The rest are a broad Pipeworx/Polymarket/utility toolkit, making the count inappropriate for the declared purpose.

Completeness3/5

The three es_tender_* tools cover the core discovery workflows: keyword search, recent notices, and status filtering with budgets, deadlines, and URLs. Notable gaps remain, though: no tender detail-by-id tool, no tender-specific subscription/alerting, and no explicit region, CPV, or date-range filters.

Resources