MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header Authorization: Bearer <token> for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "" } after the user pastes, or with no args to get the link.
| Name | Required | Description | Default |
|---|---|---|---|
| token | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations by explaining the difference between permanent non-expiring connections and session-only logins. It also indicates that calling with no arguments returns a link, which is useful for the agent.
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 two sentences but starts with an awkward parenthetical 'MCP.AI for IDE agents (Cursor, etc.)' that adds clutter. It could be more streamlined, though the core content is well-structured.
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?
For a simple tool with one parameter and no output schema, the description covers the purpose, parameter semantics, and both usage modes. It does not describe the expected response, but that is not critical for an authentication flow and the annotations provide additional constraints.
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 schema has one parameter 'token' with no description, but the description explains it as a JWT for session-only login and clarifies that omitting it yields a link. This compensates for the 0% schema description coverage.
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's purpose: authenticate by logging in and obtaining an access token. It distinguishes itself from siblings by focusing on the authentication workflow, though it does not explicitly compare with 'connect' or other tools.
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?
It provides clear guidance on when to use which method: permanent setup via config header versus session-only login by pasting a token. It also explains the behavior when called with no arguments, giving concrete usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each Open Finance resource has a clearly named list/get pair, and connection status vs provider status vs force sync are distinct. The main ambiguity is between list_accounts (which already carries balances) and get_account_balance, and between list_transactions and list_transactions_by_item, which requires reading the descriptions carefully.
The openfinance_* tools follow a consistent verb_noun pattern (list_*, get_*, update_*, force_sync, search_*). Exceptions like marketplace, toolkit_info, and openfinance_provider_status break the pattern slightly, but the two logical groups are predictable internally.
At 25 tools this is on the heavy end of the scale. Most tools have a real purpose, but several list/get pairs and the multi-purpose marketplace mega-tool could be consolidated without losing functionality, making the overall surface feel larger than strictly necessary.
The Open Finance surface is impressively broad: connections, accounts, balances, transactions, credit-card bills, loans, investments, categorization, sync, provider health, and connector search are all covered. Minor gaps like a single-transaction getter or a consolidated statement export force agents to assemble data with extra calls, but no critical workflow is blocked.