Return the most recent artifact (llms_txt or json_ld) for the
authenticated client: content, version, generated_at and status.
Use when an agent needs to read the generated llms.txt or JSON-LD.
artifact_type must be "llms_txt" or "json_ld".
| Name | Required | Description | Default |
|---|---|---|---|
| artifact_type | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation declares the operation is read-only, and the description adds client-scoping, the recency behavior, and the returned fields (content, version, generated_at, status), providing useful context beyond the annotation.
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?
Three sentences, each adding necessary information: what it returns, when to use, and the parameter constraint. No filler.
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?
The tool is simple with one parameter and read-only semantics. The description covers the return fields, the usage context, and the parameter constraint, making it sufficient for an agent without an output schema.
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 only the parameter name and type with no description or enum values. The description compensates by stating the exact allowed values: 'llms_txt' or 'json_ld', which is essential for correct invocation.
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 the specific verb 'Return' and identifies the resource ('most recent artifact') plus its fields and allowed types, clearly distinguishing it from sibling tools like regenerate_artifacts.
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?
It explicitly states the use case: 'Use when an agent needs to read the generated llms.txt or JSON-LD.' However, it does not mention alternatives or when not to use it, so it lacks the full explicit guidance.
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, though analyze_geo overlaps slightly with get_bleed_model and get_competitor_actions. Descriptions clarify the differences, making misselection unlikely.
All tools follow a consistent snake_case verb_noun pattern (activate_artifact, get_bot_activity, mark_applied). Minor deviations like analyze_geo instead of get_geo_analysis don't break the pattern.
16 tools is slightly above the typical 3-15 range, but the server covers multiple subdomains (artifacts, analysis, quick wins, reporting), so each tool has a clear purpose.
The tool set covers the full artifact lifecycle (generate, list, get, approve, activate, regenerate) and the core analysis metrics. Minor gaps like no explicit delete tool for artifacts exist, but regenerate serves that need.