rank_notes

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

With no annotations, the description carries full burden. It discloses filtering defaults (minIncomingLinks: 2), normalization of bridging scores, stub exclusion, and the rationale for defaults. It does not explicitly state read-only behavior or output format, but the behavioral details are rich.

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?

A single paragraph of 6 dense sentences, front-loading the main purpose and then efficiently detailing each parameter's effect. No redundant information; 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?

Given 5 parameters, no output schema, and no annotations, the description is mostly complete but lacks an explanation of the return format, error conditions, or prerequisites (e.g., existence of graph structure). This minor gap prevents a perfect score.

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?

Despite 100% schema coverage, the description adds substantial meaning: mapping metric enum to algorithms, explaining the purpose of minIncomingLinks and includeStubs beyond defaults, and clarifying limit and themeId. Every parameter's behavior is enriched.

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 clearly states the tool ranks notes by importance using two specific metrics (influence and bridging), defined in graph theory terms. It distinguishes itself from sibling tools like find_connections or search by focusing on centrality-based ranking.

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?

The description explains how to choose between metrics (influence vs bridging) and parameter effects, but does not explicitly state when to use this tool over sibling tools like find_connections or dataview_query. Usage context is implied but not contrasted.

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