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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds genuine behavioral value by disclosing the shape of the result: graphs come grouped by kind with a defined taxonomy, which the annotations cannot convey.

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?

Two sentences, zero filler: the enumeration of graph kinds is front-loaded and the follow-up pointer is placed after it. Every clause carries information an agent needs.

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?

With no output schema, the description carries the burden of explaining return values, and it does so by describing the grouping taxonomy. Combined with annotations covering safety and the absence of parameters, nothing material 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?

The tool takes zero parameters, so there is nothing for the description to explain and the baseline is 4. The only identifier mentioned, graph_schema(graph), belongs to a sibling tool rather than this one.

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 (list) and resource (FalkorDB graphs) and immediately enumerates the four categories those graphs fall into (consortium, per-bank, per-case, typology KB). This is enough to distinguish it from siblings like graph_schema or engine_overview 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 routes the agent to graph_schema(graph) for labels, relationship types and indexes, which is a real follow-up need after enumeration. However it never states when this tool should be preferred over other discovery tools or what a caller should do with the kind grouping, so usage context is clear but not fully bounded.

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