get_follower_demographicsReturn audience demographics for a connected social account. Instagram/Facebook/Threads return follower breakdowns. LinkedIn Company Pages return lifetime follower demographics (country, seniority, industry).
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | ||
| platform | Yes | ||
| accountId | Yes | ||
| breakdown | 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 already establish read-only, non-destructive behavior. The description adds useful platform-specific behavior, especially the LinkedIn Company Pages lifetime demographics detail, but it does not disclose pagination, data freshness, or behavior for unsupported platforms. This is acceptable but not rich.
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 tight sentences with no filler. It front-loads the primary action and then adds platform-specific detail, making it easy to scan.
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 four parameters, the description is not enough for an agent to confidently invoke the tool. It lacks guidance on how `breakdown` changes the response, what `locale` does, and what the actual return shape looks like.
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 carries the burden of explaining parameters. It mentions demographic dimensions like country, seniority, and industry, but does not explain the `breakdown`, `locale`, or `accountId` parameters or how they map to the returned data.
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 tool returns audience demographics for a connected social account, naming the specific resources (followers, LinkedIn Company Pages) and platforms. This distinguishes it from sibling analytics tools like get_account_analytics or get_post_analytics.
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 implies when to use the tool: when audience or follower demographic data is needed for supported platforms. However, it does not explicitly state when not to use it or mention alternatives such as get_account_analytics for general analytics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.