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 provide idempotentHint=true and destructiveHint=false, and the description adds meaningful context beyond that: the token is a JWT, login can be permanent via server config or session-only, and calling with no args returns the login link. This transparently explains the auth flow and side effects.
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 dense and front-loaded, immediately explaining the login flow. It is slightly run-on and mixes best-practice advice with invocation instructions, but every sentence adds useful information and there is no padding.
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 single optional parameter and no output schema, the description covers all necessary invocation paths and even explains the recommended permanent configuration. An agent has enough context to authenticate successfully or ask the user for the token.
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 only defines token as a string with no description, so the description carries the full burden. It explains that token is optional, expects a JWT pasted by the user, and that omitting it returns the login link. This fully compensates for the schema's lack of detail.
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 identifies this as an authentication/login tool with specific behaviors: get a login link, accept a token, and establish either permanent or session-only access. It is easily distinguished from the many data-operation sibling tools like organizze_list_transactions.
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 gives explicit instructions for two usage modes: configuring a permanent Authorization header vs. passing a token for session-only login, plus the no-args option to obtain the login link. This leaves little ambiguity about when and how to invoke the tool.
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 target distinct resource+action pairs, but the reporting/query side is crowded: get_balances, transactions_list_results, get_financial_summary, get_monthly_overview, and the category/tag reports overlap heavily. Long descriptions disambiguate bases and use cases, but an agent must read carefully to avoid picking the wrong report or list variant.
Organizze tools overwhelmingly follow a consistent organizze_<verb>_<noun> snake_case pattern with create_, list_, get_, update_, and delete_ prefixes. Deviations like transactions_list_results, the mass_* versus update_transactions_* bulk variants, and unprefixed platform tools keep it from being perfect.
68 tools is far beyond the practical agent surface and includes many near-variant list/report/mass tools plus six unrelated platform-level tools. Even though the finance domain is broad, this count creates an extreme mismatch for efficient tool selection.
The core finance lifecycle is well covered: accounts, cards, categories, budgets, transactions, invoices, transfers, and reports all have substantial CRUD or equivalent support. However, some referenced operations are missing entirely, such as delete_recurrence, clear_latest_imports, and remove_from_latest_imports, and transfers/recurrences lack full lifecycle coverage.