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RCA-MCP Connector

RCA-MCP Connector

by dave1362
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

rca_graph_create

Create a new empty causal DAG to serve as the starting point for manual graph construction. Add nodes and edges afterward to model root causes.

Instructions

Create a new, empty causal DAG. Populate it with rca_graph_add_node and rca_graph_add_edge afterward, or use rca_graph_discover instead if you have observational data and want the graph inferred rather than hand-built.

Args: params (GraphCreateInput): - name: graph display name (for your own reference) - description: optional free-text notes on this graph's purpose

Returns: str: JSON {graph_id, name, message}

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": {
      "params": "<params>"
    },
    "name": "rca_graph_create"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv4.1.15
    • addedInput schema / $defs / GraphCreateInput / properties / client_id / description
      Added value: +"Client namespace ID"
  2. Changed1 schema field changedv4.1.14
    • addedInput schema / $defs / GraphCreateInput / properties / description / description
      Added value: +"Optional free-text notes on this graph's purpose"
  3. First observedv4.1.13

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark this as non-read-only and non-destructive, and the description adds that the graph starts empty and must be populated afterward. It also discloses the return shape. It does not mention auth requirements or client namespacing, but the schema exposes token and client_id, and the creation behavior is otherwise clearly described.

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?

The description is compact and front-loaded: the core action appears first, followed by workflow guidance and the alternative tool. The Args and Returns sections are minimal and useful, and every sentence earns its place.

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?

For a simple graph-creation tool, the description covers the purpose, the workflow, the alternative, and the return value. The only notable gap is that the prose Args section omits the required token and client_id parameters, though these are present in the input schema.

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?

Schema description coverage is reported as 0%, so the prose description carries the burden of explaining parameters. It only documents name and description, omitting the required token and the optional client_id. While the schema itself contains descriptions for all nested fields, the tool description fails to compensate for the coverage gap and would not fully prepare an agent to invoke it correctly.

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 opens with a specific verb and resource: 'Create a new, empty causal DAG.' It also differentiates itself from rca_graph_discover by clarifying the hand-built versus inferred graph path, and references sibling tools rca_graph_add_node and rca_graph_add_edge for the next steps.

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

Usage Guidelines5/5

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

The description explicitly tells the agent when to use this tool versus rca_graph_discover: use this when hand-building the graph, and use discover when observational data is available for inference. It also states the follow-up tools (add_node, add_edge), leaving no ambiguity about the intended workflow.

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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