get_trendingGet currently trending posts on Mastodon. Returns popular statuses with engagement counts, authors, and trending tags.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of trending statuses to return (default: 10, max: 40) |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of trending statuses returned | |
| statuses | Yes | Trending posts |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about return values (engagement counts, authors, tags) but no behavioral traits beyond annotations. No contradictions.
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 concise sentences with front-loaded purpose. No redundant or irrelevant content. Every word adds value.
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 one parameter and good annotations, the description is complete enough. It specifies output content, though it omits potential pagination or rate limits. Still adequate given the tool's simplicity.
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 100%, with the 'limit' parameter fully described (default, max). The description does not add additional semantics beyond what the schema provides, so baseline of 3 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 states the tool retrieves trending posts from Mastodon and lists returned data (engagement counts, authors, tags). It is specific about the resource and action, but does not explicitly distinguish from siblings like 'get_timeline' or 'pipeworx_trending'.
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 on when to use this tool versus alternatives. The description implies it is for trending content but does not discuss exclusions or context, leaving agents to infer usage.
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 have clearly distinct purposes, but there is some overlap between research-oriented tools like ask_pipeworx and deep_research, and between entity_profile and compare_entities. Descriptions help differentiate them, so overall an agent can tell them apart.
Tool names are inconsistent, mixing snake_case (ask_pipeworx, list_subscriptions) and camelCase (bag_research, compare_entities). There is no uniform naming pattern, which can be confusing.
With 35 tools, the set is too large for a server named 'Mastodon'. Only a few tools are actually Mastodon-related (e.g., get_account, get_timeline), while the majority are PipeWorx tools unrelated to the core purpose.
As a Mastodon server, the toolset is severely incomplete: it lacks basic social media operations like posting statuses, following/unfollowing, and engaging with content. The name misrepresents the actual capabilities.