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Glama

Flickr Public

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

The description adds notable behavioral context beyond the annotations: it explains partial failure degradation, warns about 5-30s latency for first bundlephobia measurement, and mentions the sources_failed field. It also discloses the NPM-only limitation. 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 detailed but every sentence carries useful information: purpose, use case, return fields, failure behavior, and ecosystem scope. It is well-structured with semicolons and a list of output fields, ensuring high information density without fluff.

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?

Given the tool's complexity (composite of two external services, multiple output dimensions) and lack of an output schema, the description is exceptionally complete. It enumerates all return components, explains the failure mode, and notes timing delays. Nothing essential is missing.

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?

The input schema already covers 100% of parameter semantics with descriptions for 'package' and 'version', including scoped package support and defaulting behavior. The description restates these points but does not add significant new meaning beyond the schema, so a 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 defines the tool as a composite npm package evaluation check, specifying the source services (deps.dev, bundlephobia) and the exact question it answers ('should I add this npm package to my project'). It is distinct from the sibling tools, which focus on other domains like research, alerts, or subscriptions.

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?

Explicit usage guidance is provided: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also notes an ecosystem limitation (NPM only in v1) and points to an alternative for other ecosystems (deps.dev:version directly), giving clear when-to-use and when-not-to-use context.

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

B3.1/5.0
Disambiguation1/5

The tool set is extremely confusing because the server name 'Flickr Public' suggests a photo-sharing focus, but the vast majority of tools are for financial data, prediction markets, and other unrelated domains. Only 3 out of 33 tools are about Flickr, making it nearly impossible for an agent to understand the server's purpose or select appropriate tools.

Naming Consistency2/5

Tool names are all in snake_case, which is consistent, but there is no semantic pattern across the set. Verbs vary widely (ask, bet, by, compare, deep, discover, etc.) and many tools have descriptive but overly long names, mixing different styles. The lack of a common naming framework adds to the confusion.

Tool Count1/5

With 33 tools, the count is far too high for a server that should be about Flickr. The scope is massively overextended, covering multiple unrelated domains like SEC filings, Polymarket betting, and drug data. This mismatch makes the tool count feel bloated and inappropriate for the server's stated purpose.

Completeness1/5

For a Flickr server, the tool surface is severely incomplete. It only offers basic read operations (by_group, by_user, recent) and lacks core Flickr functionality such as uploading, editing, searching photos, or managing albums. The addition of hundreds of unrelated tools does not compensate for this gap, leaving the server's primary domain largely unaddressed.

Resources