Swarmrails MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It successfully discloses cost ('$0.005 per call') which is critical behavioral information, but omits other important traits like idempotency, whether conversations are stateful, rate limits, or output format details.

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 efficiently structured: definition, use cases, and cost. Every sentence provides distinct value (what it is, when to use it, financial impact) with no redundancy or fluff.

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 single-parameter tool, the description covers the essential purpose and cost. However, given the lack of output schema and annotations, it could be improved by mentioning the return type (generated text), prompt length constraints, or latency expectations. Adequate but has clear gaps.

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 schema has 100% description coverage ('Your text prompt') and only one parameter. The description adds context about what the prompt is used for (conversational AI) but does not add constraints, format specifications, or examples beyond what the schema provides. Baseline 3 is appropriate for high schema coverage.

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 tool as 'Conversational AI via Bittensor subnet 1 (Text Prompting)' with specific use cases (general questions, summaries, chat). It distinguishes itself from specialized siblings like bittensor_image or bittensor_3d by emphasizing 'general' text tasks, though it doesn't explicitly clarify when to use this versus bittensor_llm or bittensor_reasoning.

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 provides clear use cases ('Good for general questions, summaries, and chat') which implies when to use it, but lacks explicit guidance on when NOT to use it or which sibling tools to use for specialized tasks like coding, image generation, or reasoning.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.