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wyh0626

evermemos-mcp-server

by wyh0626

store_memory

Save important information from conversations into long-term memory to retain project preferences, coding conventions, and decisions across sessions.

Instructions

Save a conversation message into EverMemOS long-term memory.

Use this tool when the user shares important information that should be remembered across sessions, such as: project preferences, coding conventions, architecture decisions, deployment procedures, personal preferences, etc.

Args: content: The message content to remember. Be specific and include key details. role: Who sent this message - "user" for human messages, "assistant" for AI responses. sender: User ID for memory ownership. Defaults to EVERMEM_USER_ID env var. group_id: Project/group identifier to organize memories. Defaults to EVERMEM_GROUP_ID env var. flush: If True, force immediate memory extraction instead of waiting for natural conversation boundary detection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNouser
flushNo
senderNo
contentYes
group_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Without annotations, the description must disclose behaviors. It explains the flush parameter's effect on memory extraction timing and implies persistence across sessions. However, it does not discuss idempotency, duplicate handling, or whether the tool modifies existing memories, leaving gaps for a write operation.

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 description is concise and well-structured: a one-sentence purpose, a bulleted list of use cases, and a clear Args section. Every sentence is informative, and the most critical information is front-loaded. No redundant text.

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?

Given the presence of an output schema (though not shown), the description adequately covers purpose, usage, and parameters. It lacks some behavioral specifics (e.g., side effects) but is otherwise comprehensive for a straightforward storage tool. Slight deduction for missing behavioral clarity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite the context indicating 0% schema description coverage, the description's Args section thoroughly explains each parameter: content includes advice to be specific, role clarifies acceptable values, sender and group_id specify defaults and purposes, and flush describes its effect. This adds substantial meaning beyond the schema's titles and defaults.

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 clearly states the verb 'Save' and resource 'conversation message into EverMemOS long-term memory'. It distinguishes from siblings (delete, get, search) by focusing on storing new information, and provides specific examples of when to use.

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

Usage Guidelines4/5

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

The description explicitly says 'Use this tool when the user shares important information...' and gives concrete examples, which is strong guidance. However, it does not explicitly contrast with siblings or state when not to use it, slightly lowering the score.

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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