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

With no annotations, the description must carry the behavioral burden. It discloses the return shape of both modes, but says nothing about whether this is a cheap or expensive corpus-wide computation, whether results are ranked, or any caching/freshness behavior. Adequate but incomplete for an analysis tool with zero annotation coverage.

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 short sentences, front-loaded with the core purpose and immediately followed by the branching logic. The closing 'Reveals hidden affinities between domains' is mildly promotional but frames the value; nearly every sentence earns its place.

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 2-parameter read tool with no output schema and no annotations, the description covers both invocation paths and roughly what comes back. It could say more about result fields or ranking, but nothing an agent needs in order to invoke it correctly is missing.

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 baseline is 3, but the description adds genuine meaning: it explains the default behavior when principle_id is omitted (top N pairs corpus-wide), which the schema's 'Optional: show co-occurrences for this specific principle' does not convey. The limit parameter remains schema-only.

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 analytical verb and resource — finding which principles co-occur across pieces — with two clearly delineated modes of operation. An agent can tell this apart from list_principles (enumeration) or get_principle (single lookup) without opening the 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 tells the agent which mode applies: pass principle_id for one principle's partners, omit it for corpus-wide top pairs. This is real conditional guidance. It stops short of naming sibling alternatives (get_connections, find_tensions) or stating exclusions, so not a 5.

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