raw_call

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

Without annotations, the description carries burden but only covers return data and revert handling. It does not disclose whether the call is a simulation (no state change) or mention gas implications, leaving behavioral gaps.

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, front-loaded with the core purpose, and contains no redundant information. Every word serves a purpose.

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?

Given no output schema, the description adequately covers the return type (raw returndata, revert selector). It could mention that the tool is for advanced users or that it does not decode ABI, but it is largely complete for its intended niche.

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 description clarifies that the 'data' parameter must include the 4-byte selector and be hand-built. However, it does not explain 'to', 'from', or 'block' parameters beyond what the schema provides, and schema coverage is only 25%.

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 it is a low-level eth_call with hand-built calldata, returning raw returndata and identifying revert selectors. This distinguishes it from higher-level tools like read_contract and build_transaction.

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 specifies a use case—probing whether a function exists on a Diamond—which provides context. However, it does not explicitly compare with siblings or state when not to use it.

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