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Content Interviewer MCP

Content Interviewer MCP

by zuperRuslana
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

import_chat_history

Import a selected ChatGPT conversations.json file into the local private database to provide content research with historical discussion data.

Instructions

Import one explicitly selected ChatGPT conversations.json export into the local private database.

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": {
      "file_path": "<file_path>"
    },
    "name": "import_chat_history"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYesAbsolute path to the explicitly selected ChatGPT conversations JSON file.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are present, so the description must fully disclose side effects. It states the import destination but does not reveal whether existing data is overwritten or merged, what validation occurs, or other mutation semantics. The 'local private' wording adds some safety context but not enough 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?

A single front-loaded sentence states exactly what is imported, from where, and to where. There is no filler or redundant restatement.

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?

For a one-parameter import tool with an output schema, the description gives enough context to invoke correctly: input file and destination. It could mention overwrite/merge behavior, but the operation and scope are sufficiently clear.

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?

The schema fully documents the single file_path parameter with coverage at 100%, so a baseline of 3 applies. The description paraphrases 'explicitly selected' but adds no additional parameter meaning beyond the schema already provides.

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 uses a specific verb ('Import'), a precise input ('ChatGPT conversations.json export'), and a target ('local private database'). It clearly distinguishes from all listed siblings, none of which handle chat history imports.

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 phrase 'one explicitly selected' gives clear context that this tool is for a single user-chosen export file, not bulk discovery or multiple files. It does not state exclusions or explicitly name alternative tools, but no sibling is a plausible substitute.

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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