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dave1362

RCA-MCP Connector

rca_analysis_run_async

Submit a long-running root cause analysis as an async task. Returns a task_id immediately, allowing you to poll for results later without blocking.

Instructions

Submit a long-running RCA analysis (bayesian_network, dowhy_causal_inference, or any model against a large dataset) as an async background task. Returns a task_id immediately instead of blocking. Use rca_analysis_poll_task to check progress and retrieve the result once it completes.

Args: params (RunAnalysisAsyncInput): model_id, payload, save, tags

Returns: str: JSON with task_id

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations are minimal (no readOnlyHint, destructiveHint false), so the description carries the burden. It correctly describes the async behavior and immediate return of a task_id, but lacks details on queuing, cancellation, or potential side effects. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise (three sentences) and front-loaded with the main purpose. The last line listing parameters is somewhat redundant with the schema, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simple nature (submitting an async task) and the presence of an output schema, the description adequately covers the return value (JSON with task_id) and the polling mechanism. No major gaps for a straightforward tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description lists parameter names but provides no semantic information beyond what the schema already gives (schema coverage is partial). For instance, it does not explain required parameters like token or client_id, nor the expected values for model_id. The docstring only mentions 'model_id, payload, save, tags' without elaboration.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Submit a long-running RCA analysis') and the resource ('async background task'), and distinguishes it from synchronous runs and polling tools, with explicit reference to sibling tools rca_analysis_run and rca_analysis_poll_task.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool (for long-running analyses) and directs the user to rca_analysis_poll_task for result retrieval. It does not explicitly state when not to use it, but the context with sibling tools implies the alternative.

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

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