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

With no annotations, the description carries the full behavioral burden, and it does well: it calls itself a raw read, identifies the free-key access tier, and explains every returned field's meaning and interpretation. It conveys that this is a non-mutating analytical read. It could add caveats about data freshness or edge cases, but it is substantially transparent for a zero-parameter read tool.

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 front-loaded with a strong purpose statement and organizes return fields clearly. However, the final paragraph repeats curve-type interpretations already listed in the Returns section, adding redundancy. It is not bloated, but a couple of sentences do not earn their 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 tool with no input parameters and no annotations, the description is largely complete: it explains the access tier, the output fields, and the interpretive meaning of each curve type. It omits any note on data recency or limitations, but an agent can call and interpret the tool correctly from the provided information.

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 tool has zero parameters, so there is nothing for the description to explain about invocation arguments. The baseline for a zero-parameter tool is 4, and the description appropriately focuses on output semantics instead of trying to document nonexistent inputs.

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 a raw read of the mempool fee histogram and summarizes who is transacting on-chain right now. It names concrete output concepts like curve_type and actor_profile, going well beyond a restatement of the tool name. It does not explicitly differentiate itself from siblings such as get_mempool_fees, but the core purpose is unambiguous.

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 implies usage context: use it to understand current on-chain actor behavior and fee-curve shape. It offers rich interpretation of curve types, which helps an agent decide what the result means. However, it never states when to prefer this tool over siblings like get_mempool_fees or get_mempool_stats, leaving some selection ambiguity.

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