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

Annotations already cover read-only/idempotent/non-destructive, but the description adds two non-obvious traits: content is returned backend-authored 'as-is' (so the agent should not expect transformation or normalization), and an unknown meter slug produces a tool error rather than an empty result.

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 dense sentences, each doing distinct work: purpose, mode selection, and error/return behavior. Front-loaded with the core purpose and no filler, though the phrasing is packed enough that it borders on terse.

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?

An output schema exists, so return-value shape need not be explained. For a single optional-parameter read tool, the description covers both modes, the parametrization rule, and the failure case — only the relationship to get_sources/get_meter is left implicit.

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 coverage is 100% and the slug pattern is documented, so the baseline is 3. The description goes beyond the schema by explaining what omitting the parameter means and that the slug must be exact, which is behavioral semantics the schema alone doesn't convey.

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 first sentence names a specific verb and resource ('Explain how TheAIMeters indicators are produced'), and the next two sentences clarify the two operating modes (general vs. per-meter). It does not explicitly distinguish itself from get_sources, which could also surface 'structured sources,' leaving partial sibling ambiguity.

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?

Gives a clear conditional rule: omit slug for the general methodology and mappings, supply an exact meter slug for calculation-level detail. It also warns that an unknown slug is a tool error. It stops short of naming when to prefer get_sources or get_meter instead.

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