lnkz-mcp

Import a chat from another client

import_conversation

Imports transcripts from multiple formats and normalizes them into portable conversations, with automatic format detection or explicit specification.

Instructions

Normalizes a ChatGPT, Claude, Gemini, LNKZ, Markdown, or plain-text transcript into portable conversations. Format is detected automatically unless one is given.

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": {
      "payload": "<payload>"
    },
    "name": "import_conversation"
  }
}

Input Schema

Name	Required	Default
`tags`	No
`dryRun`	No
`format`	No	auto
`payload`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Sep 7, 2026

TDQS

C2.2/5.0

Behavior1/5

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

The description does not mention side effects such as whether a new conversation is created, whether existing data is overwritten, or whether dryRun prevents mutation. With no output schema, the caller also cannot know what is returned.

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 a single, compact sentence that covers the core function and format flexibility without unnecessary detail.

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

Completeness1/5

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

No return type, error behavior, size limits, or relationship to other tools is described. Given four parameters and no output schema, the description leaves too many operational questions unanswered.

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 descriptions cover 0% of parameters. Only format is vaguely addressed by the mention of automatic detection; payload, tags, and dryRun are undefined, despite the enum on format giving some implicit options.

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 tool normalizes transcripts from several named sources into portable conversations, and the title indicates importing. It could be more explicit about whether it saves the result or just validates/prepares it.

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

Usage Guidelines1/5

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

No guidance is provided for when to use this tool versus siblings like save_conversation or export_conversation. The description mentions format detection but does not clarify typical use cases or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.