Skip to main content
Glama

Bamboohr

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, idempotentHint, and non-destructive. The description adds substantial behavioral context: it performs composite fan-out across two services, handles partial failures gracefully, warns about 5-30s latency for first bundlephobia measurement, and discloses that failures appear in sources_failed. This goes well beyond the annotation baseline.

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 concise for the richness of content, front-loading the core purpose in the first clause. It efficiently covers composite behavior, usage triggers, return summary fields, ecosystem limitations, and edge-case latency. Every sentence contributes unique value.

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?

The tool is complex (multi-source composite with potential timeouts), but the description covers purpose, when to use, return values (summary block fields, per-advisory detail, links), ecosystem scope, and graceful degradation. With no output schema, this description sufficiently fills the gap.

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%, so the schema already fully documents both parameters (package and version). The description does not add parameter-level meaning beyond what the schema provides; the mention of 'latest published version' in schema is clear. Baseline 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 describes a composite npm package analysis tool ('should I add this npm package to my project' check in ONE call) that fans out across deps.dev and bundlephobia. It specifies the exact resource (npm packages) and the specific aggregate function, distinguishing it from sibling research tools.

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 states when to use this tool ('Use whenever an agent asks...') and gives concrete examples. It also excludes non-NPM ecosystems and points to deps.dev:version directly for PyPI/Maven/Cargo/Go, which serves as an alternative guidance.

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

All Bamboohr 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

B3.4/5.0
Disambiguation3/5

The five BambooHR tools are distinct, but the set is dominated by overlapping Pipeworx/Polymarket search and research tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve similar lookup purposes, and polymarket_edges/polymarket_edge_tracker/polymarket_arbitrage/polymarket_fill_risk/polymarket_kalshi_spread occupy closely related prediction-market territory. Descriptions help differentiate them, but an agent could easily select the wrong one.

Naming Consistency2/5

Naming conventions are heavily mixed: camelCase (ai_visibility_check, ask_pipeworx_grounded), snake_case with varying verb positions (bamboohr_get_directory, list_subscriptions, resolve_entity), and domain-prefixed families that do not share a consistent pattern. Some tools are named by action (bet_research, compare_entities) rather than resource-object style, and the Pipeworx meta-tools follow a different convention than the BambooHR tools.

Tool Count2/5

36 tools is excessive for a server ostensibly named Bamboohr, and the vast majority are unrelated to HR—they cover general data research, prediction markets, AI visibility, and memory storage. The BambooHR-specific surface is only 5 tools buried inside a much larger third-party platform, making the count disproportionate to the stated server purpose.

Completeness2/5

The BambooHR domain is severely under-covered: read operations exist for directory, employees, employee files, and time off, but there are no create/update/delete operations, no time-off request management, no org chart access, no payroll or benefits tools, and no employee lifecycle workflows. Meanwhile, the many non-HR tools are extensive for their own domains but do not fill the obvious HR gaps.

Resources