Un servidor MCP que expone datos de Akahu (banca abierta de Nueva Zelanda) a agentes LLM como Claude. Permite al agente listar tus cuentas bancarias, inspeccionar tus participaciones de inversión y extraer transacciones para su análisis.
Una caché local de SQLite (cache.db) mantiene los últimos ~90 días de transacciones en el disco y se actualiza de forma incremental. El TTL de la caché es de 24 horas para coincidir con la actualización ascendente diaria de Akahu Personal; los agentes pueden pasar force=True en cualquier herramienta para omitirlo.
list_accounts(force=False) — cuentas bancarias/de depósito con saldos. Sharesight está excluido.
get_share_holdings(force=False) — cartera de Sharesight: valor total, desglose (rendimientos / capital / divisa / dividendos) y filas por participación.
list_transactions(account, start=None, end=None, limit=100, force=False) — transacciones para una cuenta desde la caché local, actualizando desde Akahu primero si la caché tiene más de 24 horas. account coincide por ID o subcadena de nombre difusa.
Related MCP server: financy
Instala uv si no lo tienes.
Configura una Aplicación Personal de Akahu: son aplicaciones gratuitas de usuario único que creas para tu propia cuenta de Akahu. Obtendrás un app_token (el ID de la aplicación personal) y un user_token para ti.
Crea un archivo .env en la raíz del proyecto:
AKAHU_USER_TOKEN=user_token_xxx
AKAHU_APP_TOKEN=app_token_xxxuv sync para instalar las dependencias.
Prueba de funcionamiento: uv run python -m akahu_mcp.sync — debería imprimir tus cuentas y obtener transacciones para la primera.
claude mcp add akahu --scope user -- uv --directory /absolute/path/to/akahu-mcp run akahu-mcpAñádelo a ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) o el equivalente en tu plataforma:
{
"mcpServers": {
"akahu": {
"command": "uv",
"args": ["--directory", "/absolute/path/to/akahu-mcp", "run", "akahu-mcp"]
}
}
}Si tu host no puede encontrar uv en el PATH, reemplaza "uv" con la ruta absoluta obtenida mediante which uv.
Construido y probado con Aplicaciones Personales de Akahu, que solo actualizan los datos ascendentes una vez al día, de ahí el TTL de caché de 24 horas. Los mismos endpoints existen en planes comerciales, pero podría valer la pena reducir los TTL en esos casos.
legacy/ contiene los dos scripts originales (akahu.py, list_accounts.py) de los que surgió este proyecto. Todavía funcionan de forma independiente: instala sus dependencias con uv sync --group legacy, luego ejecuta uv run --group legacy python legacy/list_accounts.py.
List the user's bank/depository accounts (excludes Sharesight, which has its own tool). Cached for 24h; pass force=True to refresh from Akahu.
| Name | Required | Description | Default |
|---|---|---|---|
| force | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses caching (24h) and refresh mechanism (force=True), which is good for a read tool. It does not mention auth requirements or error cases, but the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, then additional details. Every word earns its place, no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (context signal), the description needn't explain return values. It covers purpose, scope, caching, and parameter usage. For a list tool with one optional parameter, this is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% parameter description coverage, so the description compensates by explaining the 'force' parameter: pass force=True to refresh from Akahu. This provides necessary semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists 'bank/depository accounts' and explicitly excludes Sharesight, which is handled by a sibling tool. The verb 'list' and specific resource make the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells when to use (for bank/depository accounts) and excludes Sharesight. It also explains caching behavior and how to refresh with force=True. It could explicitly mention alternatives (e.g., get_share_holdings for Sharesight) but the exclusion is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List transactions for a bank account, served from a local cache that keeps the last ~90 days. The cache is refreshed at most once per 24h (Akahu Personal only refreshes upstream daily); pass force=True to bypass the TTL.
Args: account: account id or fuzzy substring match against account name start: ISO date (YYYY-MM-DD), inclusive lower bound on transaction date end: ISO date (YYYY-MM-DD), inclusive upper bound limit: max rows to return (default 100, newest first) force: bypass the 24h cache TTL
| Name | Required | Description | Default |
|---|---|---|---|
| account | Yes | ||
| start | No | ||
| end | No | ||
| limit | No | ||
| force | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden. It discloses caching (last ~90 days, 24h refresh), force parameter effect, and default limit order. It does not cover error handling or edge cases, but the output schema exists for return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with the main sentence followed by bullet-like Args. It is slightly verbose but each sentence contributes essential information. The purpose is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 5 parameters and output schema, the description covers caching, date range, limit, and force flag. It lacks mention of error handling or account not found, but is largely complete for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description thoroughly explains all five parameters: account (fuzzy match), start/end (ISO dates), limit (max rows, default 100), force (bypass cache). This adds substantial value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action and resource: 'List transactions for a bank account'. The sibling tools (get_share_holdings, list_accounts) deal with distinct resources, eliminating confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains caching behavior and the force parameter to bypass TTL, giving context on when to use this tool. It does not explicitly exclude alternative tools, but the resource difference makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0get_share_holdingslist_accountslist_transactionsScored across 3 tools
Each tool targets a distinct area: share holdings, bank accounts, and transactions. There is no overlap in purpose.
All tool names follow a consistent verb_noun pattern: get_share_holdings, list_accounts, list_transactions.
3 tools cover the core read-only functionalities for personal finance. While limited, it is appropriate for the server's scope.
The set covers accounts, transactions, and investments, but lacks operations like getting a single account detail or investment transactions, leaving some gaps.
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