rca_pyrca_random_walk

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds algorithmic behavior (random walk, convergence) but does not disclose limitations, performance, or potential errors. It is adequate but not rich.

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 well-structured with purpose, algorithm, args, returns, and attribution. It is slightly verbose (duplicate attribution in first and last line) but overall efficient and front-loaded.

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 complex algorithm, the description covers inputs, algorithm, and output format. It lacks prerequisites (graph must exist) and interpretation details, but given the output schema exists, it is reasonably complete.

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?

Despite 0% schema description coverage, the description explains each parameter (adjacency, anomaly_scores, sli_metric, restart_prob) with clear semantics and context, adding significant value beyond the schema definitions.

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 performs graph-based root cause localisation using personalised PageRank random walk, specifying the mechanism (propagates backward, weighting by anomaly scores) and output (root cause probabilities). This distinguishes it from sibling tools like epsilon or HT diagnosis.

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 explains what the tool does but provides no explicit guidance on when to use it versus alternatives (e.g., epsilon diagnosis, HT diagnosis). It implies usage when a causal graph is available, but does not state prerequisites or compare methods.

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