Ein MCP-Server, der Akahu-Daten (neuseeländisches Open-Banking) für LLM-Agenten wie Claude bereitstellt. Er ermöglicht es dem Agenten, Ihre Bankkonten aufzulisten, Ihre Anlagebestände zu prüfen und Transaktionen zur Analyse abzurufen.
Ein lokaler SQLite-Cache (cache.db) speichert die Transaktionen der letzten ca. 90 Tage auf der Festplatte und aktualisiert sie schrittweise. Die Cache-TTL beträgt 24 Stunden, um der täglichen Upstream-Aktualisierung von Akahu Personal zu entsprechen; Agenten können bei jedem Tool force=True übergeben, um dies zu umgehen.
list_accounts(force=False) — Bank-/Einlagenkonten mit Salden. Sharesight ist ausgeschlossen.
get_share_holdings(force=False) — Sharesight-Portfolio: Gesamtwert, Aufschlüsselung (Renditen / Kapital / Währung / Dividenden) und Zeilen pro Bestand.
list_transactions(account, start=None, end=None, limit=100, force=False) — Transaktionen für ein Konto aus dem lokalen Cache, wobei zuerst von Akahu aktualisiert wird, wenn der Cache älter als 24 Stunden ist. account wird über die ID oder einen unscharfen Teilstring des Namens abgeglichen.
Related MCP server: financy
Installieren Sie uv, falls Sie es noch nicht haben.
Richten Sie eine Akahu Personal App ein — dies sind kostenlose Single-User-Apps, die Sie für Ihr eigenes Akahu-Konto erstellen. Sie erhalten ein app_token (die ID der persönlichen App) und ein user_token für sich selbst.
Erstellen Sie eine .env-Datei im Projektstammverzeichnis:
AKAHU_USER_TOKEN=user_token_xxx
AKAHU_APP_TOKEN=app_token_xxxuv sync, um die Abhängigkeiten zu installieren.
Smoke-Test: uv run python -m akahu_mcp.sync — sollte Ihre Konten auflisten und Transaktionen für das erste Konto abrufen.
claude mcp add akahu --scope user -- uv --directory /absolute/path/to/akahu-mcp run akahu-mcpFügen Sie dies zu ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) oder dem entsprechenden Pfad auf Ihrer Plattform hinzu:
{
"mcpServers": {
"akahu": {
"command": "uv",
"args": ["--directory", "/absolute/path/to/akahu-mcp", "run", "akahu-mcp"]
}
}
}Wenn Ihr Host uv nicht im PATH finden kann, ersetzen Sie "uv" durch den absoluten Pfad, den Sie mit which uv erhalten.
Entwickelt und getestet für Akahu Personal Apps, die Upstream-Daten nur einmal täglich aktualisieren — daher die 24-Stunden-Cache-TTL. Die gleichen Endpunkte existieren auch in kommerziellen Plänen, dort könnte es jedoch sinnvoll sein, die TTLs zu verkürzen.
legacy/ enthält die beiden ursprünglichen Skripte (akahu.py, list_accounts.py), aus denen dieses Projekt hervorgegangen ist. Sie funktionieren weiterhin eigenständig — installieren Sie deren Abhängigkeiten mit uv sync --group legacy, dann 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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