jambavan_graph_query

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

No annotations are provided, so the description carries the full burden. It states the tool finds nodes and BFS neighbors within a token budget, and uses extracted edges by default. It does not mention side effects, but the operation is clearly a read-like query. The behavior is transparent enough.

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 two sentences: first states purpose, second gives usage hints. Every sentence adds value without fluff. It is front-loaded and easy to parse.

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 no output schema and 5 parameters, the description covers core intent, prerequisite, and key parameter usage. It lacks details on return format but provides sufficient context for an agent to understand the tool's function.

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% (5 parameters have descriptions), so baseline is 3. The description adds extra meaning for include_inferred by clarifying its default behavior and use case, going beyond the schema description. It also relates budget to token budget.

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 queries the knowledge graph to find matching nodes and BFS neighbors, distinguishing it from sibling graph tools like jambavan_graph_path and jambavan_graph_report. The verb 'query' and resource 'knowledge graph' are specific.

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 says to call jambavan_index first, providing a clear prerequisite. It also explains the default edge behavior and when to set include_inferred=true, giving context for parameter usage. However, it lacks explicit comparison to alternative graph query tools.

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