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

Annotations already indicate mutability and non-idempotence. The description adds context by stating that rel_type must match a schema key and that from_id/to_id must be entity_ids of existing nodes, which are important validation constraints not visible in the schema alone. It also implies a deployed graph project is required. No contradictions with annotations.

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 concise and well-structured: a purpose statement, two validation rules, a concrete example, and a requirement note. Every sentence adds value, and the example is placed near the top for quick comprehension.

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 mutation tool with annotations, the description covers preconditions (valid rel_type, existing nodes), shows a full example, and defines the data shape. It does not describe return values or async behavior, but no output schema is present and the provided context is strong enough for an agent to invoke correctly. Slightly incomplete regarding what happens on success.

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 a baseline of 3 applies. The description enriches key parameters: it explains rel_type must match a schema key with an example, and clarifies that from_id and to_id must be existing node entity_ids. The data parameter is also illustrated with an example. Project_id and environment are left to the schema, which is sufficient given the high base coverage.

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's function: 'Create a relationship between two nodes in a deployed graph project.' It uses a specific verb (create) and resource (relationship between nodes), and the example with rel_type 'authored' and node IDs distinguishes it from sibling tools like create_graph_node and bulk_create_graph_relationships.

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 explicitly instructs users to 'Use get_graph_data_schema to see available relationship types,' which is a clear prerequisite and alternative guidance. However, it does not explicitly contrast with the bulk version or other alternatives, so it lacks full exclusion guidance.

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