Ask a direct question to get a comprehensive, context-aware answer synthesized from shared memories when no preset answer exists, enabling AI-to-AI consultation.
AI 对 AI 咨询：像直接问老大陈丹本人一样提问，openmem 基于全部记忆给出完整、准确、口语化的答案（走 litellm GwV4F，数秒级，较慢）。只在 mh_tool 里没有对应成品答案、且问题需要理解+综合时才用：要标准答案走 mh_tool（秒回），要原始记忆片段走 mh_search（更快）。
| Name | Required | Description | Default |
|---|---|---|---|
| agent | No | 提问方 agent 名 | unknown-agent |
| query | Yes | 要问老大/问记忆的问题 |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool is slower (seconds-level), uses litellm GwV4F, and produces answers by synthesizing all memories. It doesn't mention error handling or side effects, but for a consultation tool this is adequate. It adds value beyond the schema by noting the performance characteristic.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and then providing usage guidelines with bold for emphasis. It is concise, well-structured, and every sentence earns its place. The key constraints (when to use, speed) are highlighted effectively.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter tool with no output schema, the description provides all necessary information: what it does, when to use it, how it differs from siblings, and its performance characteristics. An agent can confidently decide and call this tool correctly based on this description alone.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, meaning both parameters (query and agent) are already described in the schema. The description adds no additional semantic detail about parameters, so the baseline score of 3 applies. It doesn't compensate for any gaps because there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: an AI-to-AI consultation that answers questions based on all memories, giving complete, accurate, and colloquial answers. It explicitly distinguishes from siblings by naming mh_tool for standard answers and mh_search for raw memory fragments, making the unique role unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit when-to-use guidance: only when mh_tool lacks a ready-made answer and the question requires understanding and synthesis. It also states when not to use it, directing to mh_tool for standard answers and mh_search for raw memory, with speed comparisons. This leaves no ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.