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LLMxRay-MCP

LLMxRay-MCP

by LogneBudo
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List my turns

list_my_turns
Read-onlyIdempotent

List recent turns sent by your client through LLMxRay, one line each; filter by client, time, and count to show the turn-by-turn sequence and when a change took effect.

Instructions

What happened, turn by turn: the turns your client sent through LLMxRay on port 11435, newest last, one line each: turn number, time, client, model, prompt tokens with cached share, prefill ms, generation speed, stop reason, and flags (reloaded, rejected or cut by the context, error). Four tools read your turns, one question each: diagnose_my_turns, is anything wrong (a verdict over several turns); read_my_last_turn, what exactly happened on the latest turn (one turn in full); list_my_turns, what happened turn by turn (the sequence, one line each); compare_my_turns, did a change help (before against after). Use it to see the sequence behind a finding, or to find when a change took effect before calling compare_my_turns with that many minutes. client: your X-LLMxRay-Client name; without it on a shared machine you read every tool's turns. since_minutes: 1 to 10080. limit: 1 to 100, default 20, the newest kept. The three combine. Needs LLMxRay running (npx llmxray). Under a second. No model is called.

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": "list_my_turns"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many of the newest turns to show, 1 to 100. Default 20.
clientNoSubstring of the client label, case-insensitive. Omit for all clients.
since_minutesNoOnly turns from the last N minutes, 1 to 10080.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.8.9
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Addedv0.8.6

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, yet the description still adds real behavioral context: it requires LLMxRay running (npx llmxray), responds under a second, calls no model, and warns that omitting client on a shared machine exposes every tool's turns. It stops short of explaining ordering/pagination edge cases beyond 'newest last'.

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

Conciseness4/5

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

Front-loaded with what it returns, then the sibling-routing matrix, then usage, then parameter notes and operational requirements. The four-tool comparison is slightly long but directly serves tool selection, so it earns its place; every other sentence is compact.

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?

With no output schema, the description compensates by describing the returned line format field by field. Prerequisites (LLMxRay must be running), latency, filter interaction, and client-scoping behavior are all covered, which is everything needed to call it correctly.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds semantics beyond the schema: the client param is tied to the caller's X-LLMxRay-Client identity and its omission has a scoping consequence, and it states that the three filters combine. Ranges and default 20 are repeated from the schema rather than added.

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?

Names a specific verb+resource (list the turns sent through LLMxRay) and enumerates exactly what each line contains: turn number, time, client, model, tokens with cached share, prefill ms, generation speed, stop reason, flags. It also positions itself against the sibling tools by naming the four turn-reading tools and the distinct question each answers.

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 'four tools read your turns, one question each' paragraph gives explicit when-to-use routing against diagnose_my_turns, read_my_last_turn, and compare_my_turns. It goes further by naming the concrete trigger ('to see the sequence behind a finding') and the hand-off ('find when a change took effect before calling compare_my_turns with that many minutes').

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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