get_agent_dependency_graph

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

No annotations are provided, so the description carries full burden. It discloses that the tool returns a directed graph with edges recording call_count, first_seen, and last_seen timestamps. It does not mention permissions, rate limits, or potential performance impact, but the read-only nature is implied. Overall, good transparency but could note if the graph is expensive to compute.

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: two short paragraphs, no filler. The purpose is front-loaded in the first sentence. Every sentence adds value: output definition and use case. Ideal structure for quick scanning.

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 zero-parameter tool with no output schema or annotations, the description covers the purpose, output details, and a concrete use case. It is sufficient for an agent to decide to use it. Missing a note on performance or when not to use, but overall complete given the tool's simplicity.

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 has zero parameters, and schema coverage is 100%, so baseline is 4. The description adds value by explaining the edge structure and fields beyond what the schema provides (which is empty). No further parameter semantics needed.

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 'Return the full directed dependency graph for all agents in the organisation' with specific verb and resource. It distinguishes from sibling tools by detailing edge types (agent→model, agent→connector, agent→agent) and the use case of impact analysis, which is not mentioned in any sibling tool.

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 mentions a key use case: 'impact analysis — if I replace this LLM model, which agents are affected and how frequently do they call it?' This provides clear context for when to use. However, it does not explicitly state when not to use or compare with sibling tools like get_agent_dependencies, which might offer a simpler view.

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