rca_graph_markov_blanket

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context by explaining the return structure (JSON with parents, children, co_parents, full_blanket) and the conceptual basis (d-separation). No contradictions.

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 very concise: 4 lines with a clear definition, conceptual explanation, and brief args/returns listing. Every sentence adds value, and the structure is front-loaded with the core purpose.

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 the tool's simplicity (single node query, read-only, idempotent) and the existence of an output schema, the description provides adequate completeness. It explains the return format and the conceptual importance. Could mention handling of non-existent nodes, but this is not critical.

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 input schema has 0% description coverage (only node has a brief description in schema). The description mentions graph_id and node in the Args line, adding some meaning for node (the node to compute). However, it does not explain token or client_id, which are required parameters. This partially compensates for the schema gap but not fully.

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 returns the Markov blanket of a node (parents, children, co-parents) and explains its significance. This distinguishes it from sibling graph tools like rca_graph_get or rca_graph_score, which have different purposes.

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 mentions it is 'essential for targeted RCA investigation,' providing clear context for when to use it. However, it does not explicitly state when not to use it or name alternative tools for similar tasks, so a slight gap remains.

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