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 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate idempotentHint=true and destructiveHint=false. The description adds behavioral context beyond annotations by explaining the two authentication flows (permanent vs session) and that calling with no args yields a link. No contradictions.
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 informative but slightly verbose; however, every sentence adds value. It is front-loaded with the main purpose and clearly structured around the two usage modes.
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 tool has only one optional parameter and no output schema, the description completely covers all necessary information: both authentication methods, behavior without arguments, and integration with server config. No gaps.
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% description coverage for the 'token' parameter. The description compensates fully by explaining that the token is a JWT string and how to use it in calls, as well as the behavior without the parameter.
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 authenticates users via MCP.AI for IDE agents, specifying two methods: adding a permanent token to config or passing a JWT for session login. It distinguishes itself from sibling tools like 'connect' or 'marketplace' by focusing solely on authentication.
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 provides explicit guidance on when to use each authentication method (permanent config vs session token) and explains the behavior with no arguments (returns login link). It does not explicitly mention when not to use this tool, but the context is clear.
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.
Most tools have clearly distinct purposes within the openfinance_* namespace, but some close pairs exist: get_account_balance vs get_accounts_detail, list_transactions vs list_transactions_by_item, and get_item_status vs provider_status vs force_sync all require careful reading. The detailed descriptions resolve most ambiguity, but selection is not always instant.
The openfinance_* tools follow a consistent snake_case verb_noun pattern (list_accounts, get_account_balance, update_transaction_category, force_sync). However, the generic platform tools like authenticate, connect, marketplace, and toolkit_info break the pattern, and provider_status is a noun phrase rather than verb_noun.
At exactly 25 tools, this sits at the heavy boundary. The Open Finance domain justifies many of them, but the set feels bloated because marketplace alone bundles a large number of sub-operations, and several platform utilities could arguably be consolidated.
The Open Finance surface is quite complete: connections, accounts, balances, transactions, credit card bills, investments, loans, categories, sync, status, and provider health are all covered. Minor gaps exist around direct connection creation (delegated to URLs) and single-item transaction detail, but agents can work around them.