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Zehee

Kimi Code Memory MCP Server

by Zehee

load_turn_context

Retrieve complete conversation turn details by session and turn IDs to restore context from prior coding sessions.

Instructions

Load the full detailed content of specific conversation turns by sessionId and turnId.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
referencesYesArray of { sessionId, turnId } references identifying the conversation rounds to load

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.4.2

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the transparency burden; 'load' clearly conveys a retrieval/non-mutating operation and 'full detailed content' sets expectations about output richness. However, it does not describe return format, scoping rules, or any behavioral caveats (e.g., unavailable turns), which would be valuable given the absent 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 entire description is one focused sentence with the action and resource front-loaded; every phrase contributes to identifying the tool's target and selection method. No filler or redundant restatement.

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 one-parameter retrieval tool with no output schema, the description explains what the agent gets ('full detailed content') and how to address turns ('by sessionId and turnId'). It is slightly incomplete on what the returned content looks like for multiple references, but it is otherwise sufficient given the low complexity.

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 schema already describes the references array and its sessionId/turnId properties at 100% coverage, so the description adds little parameter-specific meaning beyond reiterating the identifier-based selection. This meets the baseline for high schema coverage but does not exceed it.

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 names a precise verb ('load'), a specific resource ('specific conversation turns'), and the exact selection keys (sessionId and turnId). This clearly differentiates it from memory search/list siblings: it is a targeted fetch by explicit identifiers, not a discovery tool.

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 phrase 'by sessionId and turnId' implies the tool is appropriate when the agent already has exact conversation identifiers and needs full details, so usage context is implied rather than stated. It does not explicitly mention alternatives such as search_context or load_more_context, nor does it state when not to use it.

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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