get_follower_historyRaw follower-count time series for a contestant (per platform, ~30-minute resolution). Best for charts, growth analysis, and before/after comparisons around episodes.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Days of history to return (default 30; 0 = full history). | |
| platform | No | Limit to one platform. Default: all platforms. | |
| contestant | Yes | Contestant ID or slug, e.g. "394" or "amora-cachee". |
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 signal read-only, so safety is covered. The description adds useful behavioral context beyond that: data is raw, at approximately 30-minute resolution, and is a follower-count series rather than aggregated or event-based. It doesn't discuss output or pagination, but the barrier for read-only tools is lower, and the description carries its weight.
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 compact: one sentence defines the output nature, resolution, and platform dimension, and a second sentence gives concrete use cases. Every sentence earns its place without padding.
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?
For a simple read-only tool with three parameters, this is mostly complete: it defines data nature, resolution, scope, and suggested applications. Because there is no output schema, the description could be more explicit about response shape or ordering, but the core selection and invocation context are still solid.
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?
All parameters are documented in the input schema with meaningful descriptions, so baseline is 3. The description adds little over the schema. No additional parameter clarification is required, so a middle score 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 and resource: returns a raw follower-count time series for a contestant. It also adds dimensional details (per platform, ~30-minute resolution) that make the tool's role clear and distinguish it from siblings like get_follow_events or get_engagement. The intended data shape is unambiguous.
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 says when to use this tool — for charts, growth analysis, and before/after comparisons around episodes. It does not mention alternatives or state when not to use it, but the semantic focus on raw time-series data makes the use case sufficiently clear.
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 resource or analysis need: cast summaries, single-contestant details, follower time series, follow/unfollow events, follow graph, trend events, and export output are cleanly separated. The descriptions explicitly cross-reference related tools, so an agent should be able to pick the right one without ambiguity.
Tool names follow a consistent get_/noun and list_noun pattern, with export_season_csv as the only slight variation—but it still clearly uses verb_noun convention. camelCase is avoided, and duplicate or vague verbs are absent.
Twelve tools is a well-scoped size for a read-only analytics data API. Each tool contributes a meaningful slice of the domain—discovery, show details, cast metrics, raw series, events, graphs, trends, usage, and export—without redundancy or bloat.
The surface covers the full read-only workflow: discover shows and seasons, list episodes, inspect contestants and cast, retrieve follower histories, engagement, follow relationships, trend events, and export a citation-ready CSV. No obvious lifecycle dead ends exist since the API is inherently data-access-oriented rather than CRUD.