ai-memory-mcp

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add_decision

Record key decisions made during a conversation session, including type, description, and reasoning, to preserve architectural choices.

Instructions

为指定会话添加关键决策记录。

Args: params (AddDecisionInput): 包含： - session_id (str): 关联的会话 ID - decision_type (str): 决策类型，如 tech_stack / api_design / architecture - description (str): 决策描述 - reasoning (Optional[str]): 决策理由

Returns: Dict: {"success": bool, "message": str}

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "params": "<params>"
    },
    "name": "add_decision"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0May 26, 2026

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already indicate readOnlyHint=false. The description adds the return format (Dict with success and message) which is behavioral. However, it does not disclose edge cases like duplicate decisions or invalid session IDs. Overall adequate but not rich.

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 front-loaded with a clear one-sentence purpose. The docstring is structured with Args and Returns. It is not overly long, though it repeats schema information.

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

Completeness3/5

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

The tool is simple (add a decision), and the description covers the basic purpose and parameters. However, it lacks details on error behavior, constraints (e.g., duplicate decisions), and the exact output schema is not fully specified (though the return dict is described). With an output schema present, some completeness is assumed, but the description could be more thorough.

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?

Schema description coverage is 0% per context, so the description must compensate. It lists all four parameters with descriptions in a docstring, which mirrors the schema. While it adds no new meaning beyond the schema, it provides the information in a readable format, meeting the baseline for low coverage.

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 (add) and resource (decision record for session) in the first sentence. It is distinct from sibling tools like search_summaries or update_summary, which are update/query operations.

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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites or when not to use it. The agent is left to infer usage context from the tool name and purpose 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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