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

With no annotations, the description carries the burden and does well: it discloses the backend (pgvector cosine), that it reads only persisted local data (no LinkedIn pull), and that it is free. It doesn't describe the shape of returned results or ranking/tie-breaking, which is the remaining gap.

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?

Front-loaded with what it does and on what data, then use-case, then alternatives, then scoping. Every sentence carries distinct information; no filler despite the length.

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?

Covers mechanism, cost, data source, use case, alternatives, and scope resolution for a 4-param tool with no annotations and no output schema. The only omission is what the results look like (fields, ordering), which an agent might want before calling.

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%, so the baseline is 3, and the description goes beyond it by explaining the scoping model — profile_id scopes to a tracked profile, collection_id scopes to a collection, and specifying neither searches the whole library. That mutual-exclusivity/default behavior is not stated in the schema itself.

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?

States a specific verb (find similar posts) plus the mechanism: pgvector cosine over the local post library. It explicitly contrasts with `search_posts` and `linkedin_post_search`, so an agent can pick it out of a large sibling set without opening schemas.

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 the exact triggering condition ('a block of text — a draft, a paragraph, a competitor's post') and names the two alternatives with the conditions that select them (keyword search, fresh LinkedIn pull). Scoping options are also explained: profile_id, collection_id, or neither.

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