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Glama

imgflip

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint:false, so the safety profile is known. The description adds valuable behavioral context beyond annotations: bundlephobia's first measurement may take 5-30 seconds, partial failures degrade gracefully, and the sources_failed field will indicate timeouts. This helps the agent anticipate latency and incomplete results.

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 information-dense yet well-structured: it opens with the core purpose, then usage, return value summary, ecosystem scope, and failure behavior. Every sentence adds value, and the length is justified given the tool's composite nature. It is front-loaded and logically organized.

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 fully enumerates what is returned (summary block fields, per-advisory details, links, alternative versions). It also covers ecosystem constraints and timeouts, giving the agent a complete picture of tool behavior. This is exceptionally thorough for a complex tool.

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%—both package and version have clear descriptions. The description adds no significant new parameter semantics beyond what the schema already states (e.g., default version, scoped packages). Per the rubric, a baseline of 3 is appropriate when the schema does the heavy lifting.

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 identifies the tool as a composite 'should I add this npm package' check, with specific verb ('check'), resource ('npm package'), and outcome. It distinguishes from sibling research tools by referencing concrete data sources (deps.dev and bundlephobia) and the exact scope of the evaluation.

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 triggers are given with example questions: 'is X safe / popular / small' or 'what does adding lodash cost me'. It also provides a clear when-not case: non-npm ecosystems (PyPI, Maven, Cargo, Go) should go to deps.dev:version directly, effectively naming the alternative.

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

A3.5/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route to the same 5,743-tool catalog, and the six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) all target prediction-market analysis. Long descriptions clarify some boundaries, but an agent could easily misroute a query to the wrong variant.

Naming Consistency3/5

Names are mostly snake_case and readable, with consistent families like ask_pipeworx_* and polymarket_*, but conventions vary widely: verb-first (validate_claim, compare_entities, generate_llms_txt), noun-first (entity_profile, bet_research, recent_changes), and standalone verbs (remember, recall, forget). The pipeworx_ prefix is applied inconsistently, and get_memes sits apart from the data-tool naming style.

Tool Count2/5

32 tools exceeds the comfortable range and the count is inflated by near-duplicate variants (three ask_pipeworx versions, six Polymarket tools). The scope mismatch compounds the issue: the server is named imgflip but nearly all tools belong to Pipeworx data/prediction-market functionality, so the set feels bloated rather than deliberately scoped.

Completeness1/5

As an imgflip/meme server the surface is severely incomplete: get_memes explicitly refers to caption_image for creating memes, but that tool is absent, creating a dead end. The remaining tools cover a broad but unrelated Pipeworx data domain, so no coherent domain gets full lifecycle coverage and the tool set fails its apparent core purpose.

Resources