data_list_tablesRetrieve the list of tables configured in the app, used for restaurant mode or table management.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Response format: 'json' (default), 'csv', or 'html' | json |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description correctly indicates a read-only retrieval operation, consistent with the readOnlyHint annotation. No additional behavioral details are needed beyond the annotation.
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 a single, front-loaded sentence with no wasted words, efficiently conveying the tool's purpose.
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?
The tool is simple with one optional parameter and no output schema. The description, combined with the schema and annotation, provides all necessary information.
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?
With 100% schema coverage, the baseline is 3. The description does not add additional meaning for the single optional parameter 'format' beyond what the schema already provides.
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 verb 'Retrieve' and the resource 'list of tables', and provides context ('restaurant mode or table management'), distinguishing it from sibling data_list_* 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?
The description gives clear context (restaurant mode/table management) for when to use this tool, but does not explicitly mention when not to use it or compare to siblings.
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 tool targets a distinct entity or action (account, client, order, product, payment, VAT, etc.), with clear naming like 'account_create' vs 'account_edit' and 'data_list_' prefixed listings. No overlapping purposes detected.
All tool names use snake_case and follow a consistent verb_noun pattern (e.g., account_create, client_add, data_list_clients). Even compound names like auth_login_with_otp adhere to this structure.
41 tools is high but justified by the wide scope of a POS/accounting system (accounts, clients, orders, products, payments, VAT, reports). Slightly above the typical range but well-scoped.
The tool surface covers CRUD for all major entities (clients, departments, products, VAT, payment modes, orders), plus queries, reports, and authentication. No obvious gaps for a small business management server.