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More AI - ChatGPT & Claude MCP (Fortnox)

Personalkostnader

get_personnel_costs
Read-onlyIdempotent

Personalkostnader: löner, pensioner och sociala avgifter per månad och kontogrupp. Example question: "Vad kostade personalen i år, och hur fördelar det sig på löner, avgifter och pensioner?" Computed on the server from live Fortnox data (Fortnox API, https://api.fortnox.se/apidocs), over all pages.

Input Schema

NameRequiredDescriptionDefault
dateNoÅÅÅÅ-MM-DD för att välja räkenskapsår Date as YYYY-MM-DD unless the text allows more.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / date / description
      Previous value: -"ÅÅÅÅ-MM-DD för att välja räkenskapsår"New value: +"ÅÅÅÅ-MM-DD för att välja räkenskapsår Date as YYYY-MM-DD unless the text allows more."
  2. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive and closed-world. The description adds genuinely new behavioral context: the figures are computed server-side from live Fortnox data and aggregated across all pages, so the agent knows results are complete rather than a single page and are not cached/stored data. That is real value beyond the annotations.

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?

Three tight sentences: definition first, example scenario second, provenance third — appropriately front-loaded with zero filler. The example question is slightly verbose but does real work by illustrating intent.

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

Completeness4/5

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

With an output schema, full parameter documentation and rich annotations, the description only needs to cover scope and computation. It does both. Minor gaps: no statement of what period is used when date is omitted, and no differentiation from check_payroll, but for a single optional-param read tool this is close to complete.

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% and the single optional date parameter carries its own YYYY-MM-DD explanation in the schema. The description adds only the granularity (per month and account group), not the semantics of the date parameter or what happens when it is omitted. Baseline 3 applies.

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

Purpose4/5

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

States a concrete resource and breakdown explicitly: personnel costs (salaries, pensions, social contributions) per month and per account group. That is specific verb+resource clarity. It stops short of naming which sibling it is not — check_payroll and get_profit_and_loss overlap thematically and the description doesn't draw the boundary.

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

Usage Guidelines3/5

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

The embedded example question ('Vad kostade personalen i år, och hur fördelar det sig...') gives a concrete usage scenario, which is better than nothing. However there is no explicit when-to-use/when-not guidance and no alternative sibling is named, so the agent must infer boundaries from the sibling list alone.

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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