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log_input

Logs user input with context analysis to preserve conversation history, enabling context recovery and task management in TRAE IDE.

Instructions

记录TRAE Builder的输入,包含上下文分析

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNo相关文件
inputYes用户输入内容
sourceNo输入来源:builder, chat, terminal
projectNo当前项目
estimatedTokensNo预估token数量

Schema Changelog

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

  1. First observedv2.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations present, the description is the sole source of behavioral information. It indicates a recording or logging action ('记录') and mentions 'context analysis,' but it does not disclose side effects, persistence, permissions, or whether the tool returns a value. This is minimal behavioral disclosure for a tool that presumably writes data.

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?

The description is a single compact sentence with no redundant words. It front-loads the action and object, though the trailing '包含上下文分析' is somewhat vague and could confuse rather than clarify.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite full schema coverage, the description omits usage context, side effects, and expected return behavior; there is no output schema to fill these gaps. For a five-parameter tool with no annotations, this one-line description leaves an agent uncertain about invocation outcomes.

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?

All five parameters have descriptions in the schema (100% coverage), so the description does not need to restate them. The phrase '包含上下文分析' loosely relates to the files/context fields but adds no concrete parameter-level meaning. Baseline 3 applies.

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 uses a specific verb '记录' (record) and target resource 'TRAE Builder的输入' (TRAE Builder input), making the core purpose clear. The appended '包含上下文分析' (includes context analysis) is slightly vague, and it does not explicitly distinguish this tool from sibling logging tools like log_conversation and log_chat, but the resource is specific enough.

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

Usage Guidelines2/5

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

The description provides no conditions for use, exclusions, or references to sibling tools. An agent cannot determine when to choose log_input over log_conversation, log_chat, or context from the text alone.

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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