CountBean

run_query

Run BQL queries against Beancount ledger files to retrieve CSV-style rows. Analyze accounts and transactions directly from your plain-text accounting data.

Instructions

Run a Beancount Query Language (BQL) query and return CSV-style rows.

Example: SELECT account, sum(position) WHERE account ~ 'Expenses' GROUP BY account

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": {
      "bql": "<bql>"
    },
    "name": "run_query"
  }
}

Input Schema

Name	Required	Description	Default
`bql`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 5, 2026

TDQS

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description alone must convey behavioral traits. It does disclose that the operation returns CSV-style rows, which describes the response shape, and the word 'query' suggests a read-only operation. However, it does not state whether this mutates the ledger, what errors occur on invalid BQL, or whether any connection/book must be active.

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 crisp sentences: a definition and a representative example. The main action is front-loaded and there is no filler, so the description is appropriately concise for a single-parameter tool.

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 description is adequate for a simple query tool, and the presence of an output schema relieves it from detailing return values. However, it omits context about which book the query runs against and whether a connection is required, which is relevant given the connection-oriented sibling tools.

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 coverage is 0%, so the description must compensate. The example `SELECT account, sum(position)...` demonstrates valid BQL syntax and clarifies that `bql` is the query string. Still, it does not explain the full scope of BQL constructs, placeholders, or constraints, so the parameter meaning is only partially enriched.

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 opens with a concrete verb phrase 'Run a Beancount Query Language (BQL) query' and specifies the output format ('return CSV-style rows'), making it immediately distinguishable from sibling tools like list_accounts or balances. The example query adds concrete context for what kinds of statements are accepted.

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 description implies the tool is for executing arbitrary BQL, but it does not state when to prefer it over sibling tools such as list_accounts, balances, or get_ledger, nor does it mention any prerequisites (e.g., an active connection). No when-not guidance is present.

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