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mcp-knowledge

by io.github.getgapup

onboarding_salaries

Read-only

Onboarding opérationnel des salariés — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Pennylane (FR fintech SaaS, ~250 FTE) — 5 parcours 30/60/90 jours · Engineering / Sales / CS / Design / People Ops. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
focusNo
rolesYes
companyYes

TDQS

C2.1/5.0
Behavior2/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true, but the description does not clarify the behavioral implications. It does not disclose whether the tool modifies anything, requires permissions, or has side effects. The phrase 'Gapup agent-payable C-suite expertise (COO)' is opaque and adds no behavioral insight.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively short but includes extraneous information (reference case, cryptic phrase) that does not clarify the tool's purpose. The main action is front-loaded, but the reference case adds noise. It could be more concise and focused.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the nested input schema, low coverage, and ambiguous annotations, the description is incomplete. It does not describe the output format, behavior for async mode, or any prerequisites. The tool's complexity is not adequately matched by the description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 25% (only 'async' is described). The description adds no parameter-level details beyond 'send the documented case fields', which is unhelpful. It does not explain the purpose of 'focus', 'roles', or 'company' fields, leaving the agent to rely solely on the bare schema names and types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description vaguely states it 'returns a structured, audited deliverable' for 'Onboarding opérationnel des salariés', but fails to specify what the deliverable contains or how it relates to salaries/onboarding. The reference case is specific and not generalizable. It does not clearly distinguish from sibling tools like 'recruiting_architect' or 'talent_intelligence'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus alternatives. The only instruction is 'send the documented case fields', which is about input format, not usage context. There are no explicit when-to-use or when-not-to-use conditions.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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