get_connected_accountReturn the PocketLedger account linked to this chat session, including profile details and connected AI apps. Call when the user asks who they are logged in as, which account is connected, or which apps have access.
| Name | Required | Description | Default |
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No arguments | |||
| Name | Required | Description | Default |
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No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false. The description adds value by specifying the scope 'linked to this chat session' and the nature of returned data (profile details, connected AI apps). No contradictions with annotations.
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, front-loaded with the purpose in the first sentence. Every word contributes value with no redundancy. It is maximally concise while covering purpose, usage, and scope.
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 no parameters, strong annotations, and an output schema (signaled), the description is complete. It explains what the tool returns and when to use it, covering all necessary context for this simple 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?
There are no parameters, so schema description coverage is 100%. The description adds meaning by specifying what the tool returns, which goes beyond the schema. For 0 parameters, a baseline of 4 is appropriate.
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 uses a specific verb 'Return' and clearly identifies the resource as 'the PocketLedger account linked to this chat session, including profile details and connected AI apps.' No sibling tool performs this function, so it fully distinguishes from siblings.
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 explicitly states three scenarios when to call: 'when the user asks who they are logged in as, which account is connected, or which apps have access.' While it does not specify when not to use or alternatives, the context is clear and sufficient for such a simple 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.
Tools are mostly distinct with clear purposes. Potential confusion between get_report and get_summary or log_transaction and record_expense is mitigated by detailed descriptions explaining their specific use cases.
All tools follow a consistent verb_noun pattern in snake_case (e.g., delete_transaction, get_report, parse_expense_text), making the set predictable and easy to navigate.
With 13 tools covering CRUD operations, account management, and utility functions, the count is well-scoped for a personal expense tracking server without being excessive.
The tool surface covers essential expense lifecycle operations (create, read, update, delete, restore) and account settings, though a direct get_transaction by ID or bulk operations are missing but not critical.