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

The description adds crucial behavioral context by detailing *how* the change is implemented: 'by editing the `from` note's frontmatter.' This mechanical detail is a significant addition beyond the simple 'destructiveHint: false' annotation.

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?

The description is extremely concise. It front-loads the core function, provides a live example, and closes with the workflow constraint, maximizing information density without unnecessary padding.

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 the rich schema, annotations, and parameter definitions, the description fills all necessary behavioral and procedural gaps. It tells the agent exactly what, why, and how to use the tool successfully.

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?

Although schema coverage is 100%, the description provides semantic value for the 'relation' parameter by explaining its intent (domain links, overlaps) and giving multiple practical examples of different edge types, which enhances understanding beyond the schema's basic definition.

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 purpose: 'Add a typed, directed edge between two existing concepts.' It provides a concrete, actionable example ('lead-scoring depends_on contact-model') making the intent perfectly clear.

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?

It provides explicit usage guidelines by stating when the tool should be used (when a relationship is missing) and what prerequisite must be met, specifically directing the agent to use `upsert_concept` if the concepts don't exist.

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