jambavan_graph_report

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

No annotations; description shoulders full burden. Explains default behavior (first 5000 symbols), cost scaling, and edge inclusion logic. Could mention if graph is ephemeral or stored, but overall adequately transparent.

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?

Three sentences, each carrying distinct information: main action, prerequisite, and important details. No superfluous content; well-structured 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 sibling tools and no output schema, description covers key aspects: output type (hub nodes, edge notes), default behavior, and cost. Could elaborate on 'lightweight knowledge graph' but sufficient for selection.

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 covers all parameters with descriptions (100%). Description adds value: clarifies default for symbol_limit and cost trade-off, reinforces include_inferred behavior. Adds no extra for max_nodes but schema already sufficient.

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 it builds a knowledge graph and returns hub nodes with edge confidence notes. It specifies prerequisites and distinguishes behavior for inferred edges, making the purpose unambiguous.

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?

Provides clear prerequisite ('Call jambavan_index first') and conditional behavior ('inferred edges excluded unless include_inferred=true'). Does not explicitly contrast with sibling tools like jambavan_graph_path, but the purpose clarity already differentiates.

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