get_growthThe user's follower counts and 7-day growth per connected network, plus which network is growing fastest. Use this to decide where a post matters most or to report progress.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
No annotations are provided, so the description carries the full burden. It discloses the return content (follower counts, 7-day growth, fastest-growing network) but does not explicitly state that it is a read-only operation or describe any potential limitations such as data freshness or network availability. For a simple read tool, this is acceptable but not fully transparent.
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 sentences: the first states the data returned, the second gives the intended use case. Both sentences are concise, front-loaded, and free of filler, making it highly efficient.
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 zero-parameter read tool with no output schema, the description adequately covers return semantics (counts, growth, fastest network) and intended use. It lacks a detailed return format or network details, but the tool is simple enough that this is not a major gap.
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?
The tool has zero parameters, and the schema coverage is 100% (empty properties). The description adds context about what data the tool returns, which supplements the empty schema. Per the baseline for 0 params, a score of 4 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 clearly identifies the resource (follower counts and growth per connected network) and distinguishes it from content-creation siblings by specifying the analytics focus. Although it uses a noun phrase rather than an explicit verb like 'retrieves', the intent 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 states when to use the tool: 'to decide where a post matters most or to report progress.' It does not mention alternatives or when not to use it, but given the sibling tools are unrelated (drafting, listing, shaping posts), the context is clear enough.
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 has a clearly distinct purpose: creating drafts, retrieving growth analytics, listing posts, and shaping content. There is no functional overlap.
All names follow a verb_noun pattern with underscores, but verbs vary (create, get, list, shape). The pattern is consistent, though 'shape_post' is less conventional.
Four tools is slightly below average for a social media scheduling server but still covers core workflows. The count feels reasonable and not excessive.
Core operations (create draft, list posts, get analytics, generate content) are present. Minor gaps like update/delete drafts exist, but the tool set covers the primary use cases.