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Glama

Timestamp

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds critical behavioral traits: partial failures gracefully degrade, bundlephobia's first measurement can take 5-30s, and the sources_failed field indicates timeouts. It also notes the tool is limited to the npm ecosystem in v1.

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 a dense paragraph that front-loads the purpose and use cases, then lists return fields and notes. Every sentence adds value, but it could be slightly more structured (e.g., bullet points for return fields). Still, it is concise for the detail it provides.

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?

With no output schema, the description thoroughly describes the return fields (summary block, per-advisory detail, links, alternative versions) and covers edge cases (partial failures, timing, ecosystem limitation). It provides complete context for an agent to understand the tool's behavior.

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% with both 'package' and 'version' having descriptions. The description does not add extra semantics beyond the schema, such as explaining how the parameters interact with the composite check. Baseline score of 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?

The description clearly states that the tool performs a composite check for deciding whether to add an npm package, fanning out to deps.dev and bundlephobia. It specifies the exact use case and distinguishes 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?

The description explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"' and provides an alternative for non-NPM ecosystems, giving clear guidance on when to use this tool versus alternatives.

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

A3.7/5.0
Disambiguation2/5

Many tools occupy heavily overlapping territory: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research, validate_claim, and even entity_profile/compare_entities all route questions to similar underlying data and could easily be misselected. The Polymarket suite adds another cluster of near-synonymous tools. Descriptions are detailed, but the boundaries require careful reading to keep straight.

Naming Consistency2/5

Tool names mix imperative verbs (remember, subscribe, resolve_entity, validate_claim), noun-phrase descriptors (entity_profile, recent_changes, polymarket_edges), and time-utility names (now, from_timestamp, to_timestamp, relative_time). All-lowercase snake_case is consistent, but there is no unified verb_noun or domain-prefix pattern across the set.

Tool Count2/5

35 tools is heavy and exceeds the comfortable 3-15 range, and most of them are unrelated to the server name 'Timestamp,' which adds confusion. The broad Pipeworx data scope justifies more than a tiny utility server, but the count still feels overstuffed and will burden tool selection.

Completeness3/5

For the broad data-research and prediction-market domain the set actually covers, the lifecycle is fairly complete: query, deep research, entity profiles, comparisons, claim validation, subscription management, and memory storage all have working operations. However, the surface is sprawling and includes one-off tools like generate_llms_txt and scan_dependency that do not fit any coherent domain, making completeness hard to assess and leaving a fuzzy, fragmented impression.

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