conversational_blender_assistant

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

The description discloses that the tool uses multi-step sampling and falls back gracefully, which adds behavioral context beyond the schema. However, it does not clarify if the tool is read-only or modifies state, and lacks details on auth or side effects. With no annotations, the burden is partially met.

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 concise at two sentences, front-loaded with key identity. However, the first sentence includes jargon ('SEP-1577 multi-step sampling') that may obscure clarity. Overall efficient but not perfectly clear.

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 description is too minimal for a conversational tool with multi-step reasoning. It does not explain expected output format, how to phrase queries, or how sampling fallback affects results. Despite an output schema (not shown), the description alone leaves gaps in understanding.

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%, so the baseline is 3. The description adds minimal new meaning beyond schema: 'Natural language question' for user_query and 'keeps it snappy' for max_steps are paraphrases. Context_level is repeated. No additional parameter details provided.

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 labels it as a 'Conversational Blender assistant' but does not explicitly state a specific verb or action (e.g., 'answers questions', 'provides guidance'). The purpose is inferred but not clearly articulated, and it does not differentiate from siblings like 'blender_help'.

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 focuses on internal behavior (multi-step sampling, probing) but omits practical usage context such as common scenarios or exclusion of other tools.

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