Skip to main content
Glama

Linkedin_ads

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, open-world. Description adds significant behavioral context: composite call to two external sources, partial failure handling, bundlephobia's first-measurement delay (5-30s), and that sources_failed lists timeouts.

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?

Description is thorough and front-loaded with purpose, but slightly verbose due to completeness. Every sentence adds value, but could be trimmed slightly without losing clarity.

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 lacking an output schema, the description fully explains the return structure (summary block, per-advisory, links, alternative versions) and handles edge cases like partial failures. Complete for a composite tool with external dependencies.

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?

Schema coverage is 100%. Description adds meaning by clarifying package names accept scoped packages and version defaults to latest when omitted, beyond the schema descriptions.

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 performs a composite check for npm packages, covering license, advisories, version history, and bundle size. It distinguishes itself by being a single-call aggregator, contrasting with sibling tools like deps.dev:version.

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 says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also notes NPM-only scope and directs users to deps.dev:version for other ecosystems.

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

All Linkedin_ads 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

Many tools have overlapping purposes (multiple ask_pipeworx variants, several polymarket tools, and similar research functions). The agent would struggle to pick the right tool among 36, especially with unclear boundaries between some tools.

Naming Consistency2/5

Tool names lack a consistent pattern. The li_ prefix for LinkedIn tools is isolated; other tools use random prefixes (pipeworx_, polymarket_) or no prefix at all (ask_pipeworx, bet_research). Mix of verb_noun and noun structures.

Tool Count2/5

36 tools for a server named 'Linkedin_ads' is excessive. Only 5 tools are actually LinkedIn ads-related; the rest are unrelated, making the server feel bloated and unfocused. A focused server would have 5-10 tools.

Completeness2/5

For the stated LinkedIn ads domain, the tool set is incomplete: missing create/update/delete campaigns, budget management, and targeting. While the server includes many other data tools, its advertised purpose is poorly served.

Resources