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

With no annotations, the description carries the full burden and does well: it discloses that this is a pure DB read, that no LinkedIn pull occurs, and that no credits are charged — exactly the cost/side-effect facts an agent needs. It also explains how the warmth score is derived (engagement count, comment signal, recency). It stops short of stating behavior for a missing/unknown person or how person_id and linkedin_url are resolved when both are omitted.

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 tight sentences: what it summarizes, how the score is computed, and the cost/side-effect profile. The most decision-relevant facts (scope and no-credits) are front-loaded with 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?

There is no output schema, and the description compensates by enumerating the returned evidence categories and the computed score, which is sufficient for an agent to know what comes back. The remaining gap is the unresolved question of which identifier to supply when both person_id and linkedin_url are optional.

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 all three parameters are already documented in the schema and the baseline is 3. The description adds only the notion of recency feeding the score, not any clarification of days_back bounds or the person_id vs. linkedin_url precedence — a real ambiguity given zero required parameters.

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 names a specific verb (summarize) and a precise resource (engagement evidence between a target person and your collected posts), then enumerates the evidence types and the derived warmth score. It is unambiguous what the tool produces, but it never names or contrasts with the close sibling get_engagement_brief, so an agent must infer the distinction.

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?

"For a target person" implies the usage context (per-person analysis) and the closing sentence implies it is the cheap, no-credit option, but there is no explicit when-to-use/when-not guidance and no mention of get_engagement_brief or get_engagement_trends as alternatives. Usage is inferable rather than stated.

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