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ai-dememory

ai-dememory

by GonzaloTorreras
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Import Chat Candidates

memory.import_chats

Import chat files from Claude, Codex, Cursor, or Windsurf into inbox for review. Set a limit or use dry run to preview without importing.

Instructions

Import configured provider chat/session files into inbox/imports/ for review.

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": {
      "provider": "<provider>"
    },
    "name": "memory.import_chats"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
dry_runNo
providerYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.9/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, and the description adds context ('for review') indicating a non-destructive write. However, it does not disclose side effects, permissions needed, or error conditions. With annotations present, the value added is moderate.

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?

A single sentence that is front-loaded with the verb and resource, containing no unnecessary words. Every word earns its place.

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?

The description is too brief for a tool with three parameters and an output schema. It lacks context about success outcomes, error conditions, and operational constraints (e.g., file formats, size limits). The existence of an output schema partially mitigates the return value gap, but overall completeness is low.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not explain any of the three parameters (provider, limit, dry_run). The enum values for provider are self-explanatory, but the description adds no semantics beyond the schema.

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 clearly states the action (import), resource (configured provider chat/session files), target (inbox/imports/), and purpose (for review). It is specific and distinguishable from most siblings, though no explicit differentiation is given.

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?

No guidance on when to use this tool vs alternatives, no prerequisites, limitations, or exclusions. The description implies usage but provides no explicit context for decision-making.

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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