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knishioka

freee Accounting MCP Server

by knishioka

freee_compare_periods

Compare two financial periods with pre-computed diffs and percentages for YoY/MoM analysis. Returns both period metrics, absolute and percentage changes, and significance highlights.

Instructions

Compare two financial periods with pre-computed diffs and percentages - Single call for YoY/MoM analysis. Returns metrics for both periods, absolute and percentage changes, and significance highlights. Eliminates LLM-side math for period comparisons.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyIdNoCompany ID (optional, uses FREEE_DEFAULT_COMPANY_ID if not provided)
reportTypeYesType of financial report to compare
period1YesFirst period to compare
period2YesSecond period to compare
breakdownDisplayTypeNoBreakdown display type (partner: 取引先, item: 品目, section: 部門, account_item: 勘定科目)
Behavior3/5

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

No annotations provided, so the description must cover behavioral traits. It discloses output (metrics, diffs, percentages, significance) but omits safety (read-only nature), authentication needs, error handling, or rate limits. It adds some context beyond schema (pre-computed, significance highlights) but lacks full disclosure.

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?

Two sentences: first states the function and benefit, second lists the outputs. No redundant information, front-loaded with the core purpose. Every sentence adds value.

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

Completeness3/5

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

The tool is complex with 5 parameters, nested objects, and no output schema. The description gives a high-level summary of outputs but lacks detail on return structure, error conditions, or edge cases. It leaves ambiguity for an AI agent to understand exact output format and potential failure modes.

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%; each parameter is well-described in the schema. The tool description adds high-level context (compare two periods) but does not provide additional semantics for individual parameters beyond what the schema already offers.

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 it compares two financial periods with pre-computed diffs and percentages for YoY/MoM analysis. It specifies what it returns: metrics, absolute/percentage changes, and significance highlights. It differentiates from siblings by eliminating LLM-side math, making its unique value clear.

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

Usage Guidelines4/5

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

The description implies usage for period comparisons (YoY/MoM) by stating 'Single call... Eliminates LLM-side math.' However, it does not explicitly state when not to use this tool or list alternative tools for other comparison needs.

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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