rootcause-mcp

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rc_add_causal_link

Add causal links between root cause nodes to capture escalation loops, feedback cycles, or mitigation effects beyond linear chains.

Instructions

Add a directed or bidirectional causal relationship between Why nodes. Use this to capture escalation loops, feedback cycles, or mitigation links that are not visible in a simple linear 5-Why chain.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "session_id": "<session_id>",
      "source_node_id": "<source_node_id>",
      "target_node_id": "<target_node_id>"
    },
    "name": "rc_add_causal_link"
  }
}

Input Schema

Name	Required	Description	Default
`session_id`	Yes	The session ID
`source_node_id`	Yes	The source WhyNode ID
`target_node_id`	Yes	The target WhyNode ID
`relationship`	No	Type of causal relationship	feedback
`strength`	No	Relationship strength (0.0-1.0)
`bidirectional`	No	Whether the influence also goes from target back to source
`note`	No	Optional explanatory note for this link
`evidence`	No	Optional evidence supporting the link

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 26, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations provided; description only says 'adds a relationship' without disclosing mutation effects, prerequisites, or error states. Does not explain behavior on duplicate links or required permissions.

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

Conciseness5/5

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

Two concise sentences: first defines action, second provides context. No redundant or filler content.

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

Completeness2/5

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

With 8 parameters and no output schema, the description lacks guidance on parameter selection (e.g., when to use each relationship type) and does not mention return value or validation outcomes.

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

Parameters3/5

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

Schema coverage is 100% with detailed parameter descriptions. The description adds no extra meaning beyond 'directed or bidirectional' which maps to the bidirectional field. Baseline 3 applies.

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?

Clearly states verb 'Add' and resource 'causal relationship between Why nodes'. Distinguishes from linear 5-Why chain, providing specific use cases (escalation loops, feedback cycles, mitigation links).

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?

Explicitly states when to use (non-linear relationships). Implicitly differentiates from rc_add_cause but lacks explicit 'when not to use' or alternative references.

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