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RLM MCP Server

RLM MCP Server

by win10ogod
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Get Context Info

rlm_get_context_info
Read-onlyIdempotent

Retrieve metadata, structure type, and content preview for a loaded context to assess its size and format before deciding how to process it.

Instructions

Get metadata and preview of a loaded context.

Returns:

  • Length, line count, word count

  • Detected structure type (json, csv, markdown, code, etc.)

  • Optional content preview

Use this to understand the context before deciding how to process it.

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_get_context_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
context_idNoContext identifiermain
session_idNoSession ID
preview_lengthNoPreview length in characters
include_previewNoInclude content preview

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable detail about what metadata is returned (length, line count, word count, structure type) and that preview is optional. 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.

Conciseness5/5

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

The description is concise and well-structured, with a one-sentence summary, a bulleted return list, and a usage note. Every sentence contributes meaning without padding.

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?

For a read-only metadata tool, the description fully covers purpose, return categories, and usage context. With no required parameters and full schema descriptions, the description is sufficient even without an output schema.

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?

The input schema covers all four parameters with descriptions, giving baseline 3. The tool description does not add further meaning beyond mentioning a preview, so no extra credit is earned.

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

Purpose4/5

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

The description clearly states the tool gets metadata and preview of a loaded context, with a specific verb and resource. It lists return types but does not explicitly differentiate from sibling tools like rlm_read_context, so it misses the highest bar for sibling distinction.

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?

'Use this to understand the context before deciding how to process it' provides clear context for when to invoke the tool. It does not mention exclusions or alternative tools, so it stops short of full guidance.

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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