KumoRFM MCP Server

Official

by kumo-ai

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

🕸️ Assembling graph…

materialize_graph

Idempotent

Re-materialize the graph after metadata changes to make it available for prediction and evaluation. Required before running any inference.

Instructions

Materialize the graph based on the current state of the graph metadata to make it available for inference operations (e.g., predict and evaluate).

Any updates to the graph metadata require re-materializing the graph before the KumoRFM model can start making predictions again.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "materialize_graph"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description
`num_nodes`	Yes
`num_edges`	Yes
`time_ranges`	No	Earliest to latest timestamp for each table in the graph that contains a time column

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 18, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already provide idempotency and non-destructiveness. The description adds important context about the dependency chain (needs re-materialization after metadata updates). However, it doesn't discuss performance or additional side effects.

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 well-structured sentences with no redundant information. Purpose and dependency are clearly stated upfront.

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

Completeness5/5

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

Given no parameters, an output schema exists, and annotations cover idempotency, the description provides complete guidance: prerequisite (metadata updates), purpose (enable inference), and the need for re-materialization. No gaps.

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

Parameters4/5

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

No parameters are present, so schema coverage is 100%. The baseline for 0 parameters is 4, and the description adequately explains the tool's action without needing parameter-level details.

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?

Description clearly states the tool materializes the graph to make it available for inference operations (predict and evaluate). It distinguishes from siblings like update_graph_metadata (which updates metadata) and predict/evaluate (which use the graph).

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?

Description explains that materialization is required after graph metadata updates before inference. It implicitly tells when to use it, but doesn't explicitly state when not to use it or mention alternatives, though siblings provide context.

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

Deploy Server