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

With no annotations, the description carries the burden and does so well: it discloses the data source ('no LinkedIn pull'), the cost profile ('free' vs. 5 credits for the sibling), and the ranking mechanism (hybrid FTS + cosine). It stops short of stating pagination behavior or result-set shape, but the critical safety/cost signals are present.

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 front-loaded sentences with the capability, the routing rule, and the paid alternative in that order — no filler. The second sentence ('Use this when the caller has a query string...') mildly restates the opening rather than adding new information, which keeps it just below top marks.

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 5-parameter read tool with full schema coverage and no output schema, the description supplies source, cost, ranking model, and the sibling alternative — enough to call it correctly. What it omits is anything about result volume or ranking tie-breaking, which an agent might want given there is no output schema to consult.

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 `mode`, `limit`, `query`, `scope`, and `profile_id` are already documented inline with an enum on `mode`. The description's note on `scope` ('a post_collections.id') restates the schema rather than adding syntax or edge-case guidance, so the baseline 3 applies.

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 and resource ('Keyword + semantic search over persisted posts in the local library') and immediately disambiguates the underlying mechanism (full-text + pgvector cosine). An agent can distinguish it from `get_posts`, `find_similar_posts`, and `linkedin_post_search` without opening any schema.

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?

Explicitly names the selection condition ('when the caller has a query string and wants ranked persisted-post results') and names the alternative with its tradeoff ('For fresh LinkedIn keyword pulls, use `linkedin_post_search` instead (5 credits)'). It also gives the narrowing condition for `scope`, so both the use case and the exclusion are covered.

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