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Read my last turn

read_my_last_turn
Read-onlyIdempotent

Inspect the last LLMxRay turn to get token counts, timing, VRAM fit, and prompt mismatch data. Use it to budget your next call or verify a change helped.

Instructions

The most recent turn seen through LLMxRay, as numbers: prompt tokens and cached share, prefill time and speed, generated tokens and speed, stop reason, prompt make-up (system, tools, history, new message), context requested, VRAM fit, and where the prompt stopped matching the previous turn. Use it to budget the next call or to confirm a change helped. For a verdict across turns use diagnose_my_turns. For what is installed and loaded use list_local_models. Returns text, one fact per line. Explains how to route through 11435 when no turn was seen. client: a substring of the label LLMxRay shows, which is the name you set in X-LLMxRay-Client. On a shared machine the latest turn may be another tool's, so pass your own name. Omit it when you are the only caller. Needs LLMxRay running (npx llmxray) and says so if not. Under a second, from records already kept. No model is called.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clientNoPart of the client label or id, case-insensitive substring. Omit for the latest turn from any client.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.8.5

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover the safe read-only/idempotent profile, and the description adds materially: it requires LLMxRay running (npx llmxray) and errors otherwise, runs under a second from retained records, calls no model, and discloses the shared-machine caveat that the latest turn may belong to another tool.

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?

Dense but front-loaded: the opening sentence establishes what is returned before any usage or caveat text. The em-dash enumerations are efficient, though the second half of the description is packed tightly enough to be slightly heavy.

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 stating the return format ('text, one fact per line') and handling the empty-result edge case (how to route through 11435 when no turn was seen). An agent has everything needed to call it and interpret the result.

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 real meaning the schema lacks: the label comes from the X-LLMxRay-Client header you set, why to pass your own name on a shared machine, and that omission means 'latest from any client'.

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?

States a specific verb and resource ('the most recent turn seen through LLMxRay') and enumerates exactly what is read: token counts, prefill/generation speed, stop reason, prompt make-up, context, VRAM fit. It also names the sibling tools that cover adjacent needs, so an agent can distinguish it without opening a schema.

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?

Gives explicit use cases ('budget the next call or to confirm a change helped') plus named alternatives with their selection conditions: diagnose_my_turns for a cross-turn verdict, list_local_models for what's installed. Nothing is left to inference.

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