Get detailed information about a community post, including title, content, tags, input/output types, and engagement stats. Use this to preview a post before forking it.
| Name | Required | Description | Default |
|---|---|---|---|
| post_id | Yes | The community post ID (from search_community results). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds useful return field details (title, content, tags, etc.) but does not disclose operational behaviors such as authorization requirements, rate limits, or side effects. With annotations covering safety, this is adequate but not outstanding.
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 long, front-loaded with the tool's purpose and a clear usage tip. It is concise, with no redundant words or filler. Every sentence earns its place.
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 no output schema, the description covers the key aspects: what it does, what data it returns, and when to use it. The only minor gap is that it doesn't specify the exact return structure or pagination, but this is not critical for a 'get details' tool. Annotations fill in the safety context, making the overall definition sufficiently complete.
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 input schema provides a complete description of the single parameter 'post_id' including its type, required status, and provenance ('from search_community results'). Schema description coverage is 100%, so the description does not need to add parameter semantics. The baseline of 3 applies since the schema carries the heavy lifting.
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's function: 'Get detailed information about a community post' and lists the types of information (title, content, tags, input/output types, engagement stats). This is a specific verb+resource combination that distinguishes it from siblings like search_community (find posts) and create_community_post (create posts).
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 provides a clear use case: 'Use this to preview a post before forking it.' While it doesn't explicitly name alternative tools, the context of previewing implies it is for reading details rather than searching or creating. The schema's reference to 'from search_community results' further clarifies the workflow, but that is outside the description itself.
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 and action, with clear separation between flow lifecycle, execution, model exploration, community, and account tools. Even similar-sounding tools like create_flow, preview_flow, and suggest_flow have clearly different purposes (actually creating, dry-running, and recommending models). Descriptions prevent misselection.
All tools follow a consistent verb_noun pattern in snake_case (e.g., create_flow, list_flows, run_batch, cancel_flow). No mixed conventions or vague verbs like 'process' or 'handle'. The naming is uniform and predictable.
At 33 tools, this is a large surface, but each tool addresses a distinct feature of the cnaps.ai platform, from flow CRUD and execution to community features and notifications. Still, it exceeds the typical well-scoped range and feels heavy, making it a borderline case between appropriate and too many.
The core flow lifecycle (create, read, update, delete, restore, duplicate) and execution (run, batch, cancel) are covered, but structural editing of flow graphs is missing—update_flow only changes parameters, not topology. Additionally, there is no run history, batch list/cancel, or community post update/delete, leaving notable gaps for a platform API.