stock_dashboard
Dashboard stock (KPIs)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Dashboard stock (KPIs)
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe read behavior. The description adds no additional behavioral context beyond the tool's name, but does not contradict 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 extremely concise at 3 words, which is efficient but may be too sparse. It is front-loaded but lacks meaningful detail.
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 zero parameters and simple read-only nature, the description is minimally adequate but does not explain what KPIs are displayed or how this differs from sibling tools like 'dashboard_kpis'.
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, and schema description coverage is 100% (vacuous). The description does not need to add parameter details.
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 'Dashboard stock (KPIs)' is vague and does not clearly state what the tool does. It is not a verb+resource pair and does not distinguish itself from sibling tools like 'dashboard_kpis'.
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?
No guidance is provided on when to use this tool versus alternatives such as 'dashboard_kpis' or 'list_stock_alerts'. The description lacks context for appropriate use.
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 resources and actions (e.g., create_client vs. list_clients). However, generic tools like eyeot_call overlap with many specific tools, and there's potential confusion between convert_opportunity_to_quote, create_quote, and accept_quote, though descriptions help distinguish them.
The majority follow verb_noun pattern (create_client, list_invoices), but there are outliers like eyeot_call, eyeot_help, stock_dashboard, dashboard_kpis, whoami, and search. The mix of French and English is consistent but slight inconsistency in structure.
33 tools cover multiple ERP modules (CRM, HR, inventory, sales, IT). While on the higher end, each module has necessary operations. The presence of generic eyeot_call suggests the tool surface could be larger, but the count is still reasonable for the scope.
Only clients have update and no deletes exist. Many resources (products, employees, orders, quotes, invoices, opportunities, tickets) lack update/delete operations. This creates dead ends for agents that need to modify or remove data.