Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@akahu-mcp list my bank accounts"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that exposes Akahu (New Zealand open-banking) data to LLM agents like Claude. Lets the agent list your bank accounts, inspect your investment holdings, and pull transactions for analysis.
A local SQLite cache (cache.db) keeps the last ~90 days of transactions on disk and refreshes incrementally. The cache TTL is 24h to match Akahu Personal's once-a-day upstream refresh; agents can pass force=True on any tool to bypass it.
list_accounts(force=False) — bank/depository accounts with balances. Sharesight is excluded.
get_share_holdings(force=False) — Sharesight portfolio: total value, breakdown (returns / capital / currency / dividends), and per-holding rows.
list_transactions(account, start=None, end=None, limit=100, force=False) — transactions for one account from the local cache, refreshing from Akahu first if the cache is older than 24h. account matches by id or fuzzy name substring.
Related MCP server: financy
Install uv if you don't have it.
Set up an Akahu Personal App — these are free, single-user apps you create against your own Akahu account. You'll get an app_token (the personal app's id) and a user_token for yourself.
Create a .env file in the project root:
AKAHU_USER_TOKEN=user_token_xxx
AKAHU_APP_TOKEN=app_token_xxxuv sync to install dependencies.
Smoke-test: uv run python -m akahu_mcp.sync — should print your accounts and fetch transactions for the first one.
claude mcp add akahu --scope user -- uv --directory /absolute/path/to/akahu-mcp run akahu-mcpAdd to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the equivalent on your platform:
{
"mcpServers": {
"akahu": {
"command": "uv",
"args": ["--directory", "/absolute/path/to/akahu-mcp", "run", "akahu-mcp"]
}
}
}If your host can't find uv on PATH, replace "uv" with the absolute path from which uv.
Built and tested against Akahu Personal Apps, which only refresh upstream data once per day — hence the 24h cache TTL. The same endpoints exist on commercial plans, but TTLs may be worth shortening there.
legacy/ contains the two original scripts (akahu.py, list_accounts.py) that this project grew out of. They still work standalone — install their deps with uv sync --group legacy, then 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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