get_analyticsRetrieve analytics breakdowns for Instagram comment-to-DM automations by kind and time range to monitor performance, sentiment, and triggers.
Analytics breakdowns. kind = overview | timeseries | sentiment | top-automations | by-trigger | follow-unlock. range = 7d | 30d | 90d.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | ||
| range | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so safety is covered. Beyond that the description adds nothing behavioral: it does not say what happens when kind/range are omitted (both are optional), whether a default is applied, or what shape the response takes despite there being no output schema.
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?
Two short, front-loaded sentences with no filler; the lead clause states the purpose and the value lists follow. It is efficient, though so terse that it borders on under-specification for a six-variant parameter.
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?
With no output schema and zero required parameters, the description should explain defaults and return content per kind, and it does neither. For a tool whose whole value is the kind/range combination, this is materially incomplete.
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?
Schema description coverage is 0%, so the description must carry the burden, and it does restate the allowed values for both enums. However, those values are already present in the schema enums, and it never explains what each kind actually returns or how range interacts with kind.
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 names the resource (analytics) and enumerates the six breakdown kinds, so an agent knows it retrieves analytics data broken down by dimension. It does not differentiate itself from the sibling get_dashboard, which is also an analytics/read tool, leaving ambiguity about which to pick.
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?
There is no explicit when-to-use guidance, no statement of when not to use it, and no mention of the sibling get_dashboard. The kind enum implies what can be requested but the agent gets no criteria for choosing this tool over the dashboard tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.