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RLM MCP Server

RLM MCP Server

by win10ogod
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Suggest Decomposition Strategy

rlm_suggest_strategy
Read-onlyIdempotent

Analyzes context structure, content type, and size to recommend a suitable decomposition strategy for chunking large content.

Instructions

Get a suggested decomposition strategy based on the context's structure.

Analyzes the content type and size to recommend the best chunking approach.

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": "rlm_suggest_strategy"
  }
}

Input Schema

NameRequiredDescriptionDefault
context_idNoContext identifiermain
session_idNoSession ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool analyzes content type and size and returns a suggestion rather than performing decomposition, which is meaningful behavioral context beyond the annotations.

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 two sentences, front-loaded with the primary action, and every sentence provides useful information. No redundant or unnecessary content.

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 read-only suggestion tool with good annotations, the description covers purpose, behavior, and the output type (suggested strategy). It lacks explicit return format details, but with no output schema and straightforward params, this is sufficient.

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?

Both parameters (context_id, session_id) have descriptions in the schema, providing 100% coverage. The description does not add any additional meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 uses a specific verb ('Get') and resource ('suggested decomposition strategy') and clarifies it analyzes content type and size. This distinguishes it from siblings like rlm_decompose_context, which actually performs decomposition.

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

Usage Guidelines3/5

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

The description implies its use for recommending a chunking approach based on context structure, but it does not explicitly state when to use this tool versus alternatives like rlm_decompose_context. No exclusions or prerequisites are provided.

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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