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

Beyond the annotations (readOnly/idempotent/non-destructive), the description discloses genuinely useful behavior: reads are sequential and not one atomic snapshot, each item keeps its own provenance/revision, unsupported selections surface as per-item errors, and the 200000-observation and response-byte bounds apply to the combined result. This is exactly the operational context annotations cannot carry.

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?

Four tight sentences with the defining distinction ('multiple...sequentially', 'not one atomic snapshot') front-loaded, followed by error model and resource bounds. No filler.

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?

With no output schema, the description covers atomicity, per-item error semantics, and resource bounds, which are the high-risk unknowns. It stops short of describing the actual response shape, leaving some ambiguity about how results map to the request list.

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?

Top-level schema description coverage is 0%, and the description never explains the request fields (metric, start, end, asset, interval, smoothing, sma, publication) — it only refers to 'metric selections' abstractly. It adds essentially no meaning beyond what the nested subfield descriptions already provide.

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 gives a specific verb and resource ('Read multiple metric selections') and the word 'multiple' distinguishes it from the singular sibling fetch_metric. It does not explicitly name which sibling to prefer, but the bulk framing makes the scope 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?

Usage is implied by 'multiple metric selections' versus the singular fetch_metric, but there is no explicit when-to-use or when-not statement and no alternative named. The agent can infer the bulk use case but must reason about it.

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