CountBean

add_transactions

Append Beancount transactions to your ledger, validated automatically before commit; invalid entries return errors and write nothing, keeping books balanced.

Instructions

Append one or more transactions (raw Beancount syntax) to the book.

The text is validated with bean-check and only committed if valid; on failure nothing is written and the validation errors are returned. Postings must balance. Open any new accounts first (open_accounts).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "beancount_text": "<beancount_text>"
    },
    "name": "add_transactions"
  }
}

Input Schema

Name	Required	Description	Default
`beancount_text`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 5, 2026

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden and does so well: it discloses validation via bean-check, atomic commit semantics ('only committed if valid'), no partial writes on failure, and the balancing requirement for postings. This gives the agent confidence about side effects and failure behavior.

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 compact and front-loaded with the core purpose, followed by concise behavioral notes. Every sentence earns its place: purpose, validation/commit behavior, and prerequisite.

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?

For a single-parameter append operation with an output schema, the description covers the key contextual gaps: what is appended, how validation works, what happens on failure, posting balance requirements, and the prerequisite for opening accounts. No critical information is missing.

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 description coverage is 0%, so the description must explain the parameter. It does: the parameter is described as raw Beancount syntax, and the text must be valid and balanced. While no explicit example or detailed format is given, the meaning of 'beancount_text' is clearly conveyed.

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 begins with a specific verb 'Append' and identifies the resource: 'one or more transactions (raw Beancount syntax) to the book.' It clearly distinguishes this from sibling tools like open_accounts and get_ledger by specifying the append action and the Beancount syntax requirement.

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 gives clear usage context by explaining when validation occurs and explicitly instructs 'Open any new accounts first (open_accounts).' It does not explicitly list when not to use the tool or name alternatives, but the prerequisite guidance is actionable and sufficient.

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