Kontra-KI

MCP server that sends ideas to a locally running LM Studio model for adversarial review. Built as a "devil's advocate" for Claude: instead of presenting proposals unchallenged, Claude has an independent second model interrogate them first.

Setup

1. LM Studio

1. Load a model (e.g. a local Llama/Qwen/Mistral model).

2. Start the local server in the Developer tab (default: http://localhost:1234).

Kontra-KI auto-detects the model: on every call it asks LM Studio's native API (/api/v0/models, not the OpenAI-compatible one - that one doesn't expose load state) for the single chat-capable model currently loaded, and uses that. No configuration needed as long as exactly one non-embedding model is loaded. If zero or more than one are loaded, the call fails with a clear error naming the candidates - set KONTRA_KI_MODEL to disambiguate (see below).

By default Kontra-KI talks to LM Studio at http://localhost:1234. If LM Studio runs elsewhere (a different port, or a different machine on your network), set KONTRA_KI_LM_STUDIO_URL to override it (see below).

2. Install Kontra-KI

cd /path/to/kontra-ki
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

3. Register as an MCP server (Claude Code)

claude mcp add kontra-ki --scope user -- /path/to/kontra-ki/.venv/bin/python -m kontra_ki.server

To pin a specific model instead of auto-detecting (e.g. if you keep several chat-capable models loaded at once):

claude mcp add kontra-ki --scope user --env KONTRA_KI_MODEL=<your-model-name> -- /path/to/kontra-ki/.venv/bin/python -m kontra_ki.server

To point at an LM Studio instance that isn't on localhost:1234:

claude mcp add kontra-ki --scope user --env KONTRA_KI_LM_STUDIO_URL=http://<host>:<port> -- /path/to/kontra-ki/.venv/bin/python -m kontra_ki.server

--scope user makes the server available in every new session, regardless of working directory. It then shows up as the challenge_idea tool.

Related MCP server: brainstorm-mcp

Tool

challenge_idea(idea: str, context: str = "", persona: str = "diabolo", strict: bool = False) -> str

Sends idea (plus optional context) to LM Studio with the chosen persona's system prompt and returns the local model's critique. System prompts are deliberately in English (better instruction-following on smaller local models), but the model replies in whatever language the input was written in.

Strict mode

Only for inquisitor and code_skeptic (the two personas with genuine pass/fail semantics): with strict=True, the persona also issues a verdict (VERDICT: REJECT / VERDICT: PASS). On REJECT, the tool call itself comes back as an MCP tool error (isError=True, via ToolError) instead of plain text - the calling model gets the "must fix this" reflex instead of a skimmable text comment. If the local model doesn't emit the expected verdict format, the tool fails open (text is returned normally, no silent failure). Requesting strict=True on a persona that doesn't support it returns a clear error instead of being silently ignored.

Personas

To add a persona: add an entry to the PERSONAS dict in kontra_ki/personas.py, and a matching one to PROMPT_DESCRIPTIONS in kontra_ki/prompts.py (the two are asserted to stay in sync at import time).

Prompts

Each persona is also registered as an MCP prompt (diabolo, cynic, antithesis, code_skeptic, inquisitor, chief_architect, sycophant_hunter), so clients that show a prompt picker (e.g. Claude Desktop) can select a persona directly instead of only reaching it through the persona string argument of challenge_idea. Each prompt takes idea (required) and context (optional); code_skeptic and inquisitor additionally take strict. A prompt renders to an instruction telling the calling model which challenge_idea call to make - it doesn't call LM Studio itself.

Chaining personas

The server stays single-shot and stateless on purpose (see Design decisions) - there is no built-in multi-persona chain tool. Chaining is the calling agent's job: call challenge_idea once, then feed its output back in as the next call's idea (with the original submission as context). This composes in either direction:

• Same target, different angles: run inquisitor and chief_architect on the same idea independently, then compare verdicts yourself.

• Critique-of-critique: run sycophant_hunter with a persona's critique as idea and the original submission as context, to check whether that critique itself was generic, unearned, or overreaching rather than grounded in the actual input.

Logging

Every call (accepted, rejected, or failed) is logged to stderr with persona, strict, and - for strict calls - the verdict, so you can audit what was reviewed and when. Never logged to stdout: that's the stdio transport's JSON-RPC channel, and writing to it would corrupt the protocol stream. Log level defaults to INFO; override with KONTRA_KI_LOG_LEVEL (e.g. DEBUG, WARNING).

Structure

• kontra_ki/personas.py - persona registry (system prompts, default)

• kontra_ki/prompts.py - one MCP prompt template per persona

• kontra_ki/lm_studio_client.py - HTTP client for LM Studio's chat completions endpoint

• kontra_ki/server.py - MCP server, wires the tool call to persona + client, audit logging

• tests/ - pytest suite (verdict parsing, tool error paths, prompt registration/rendering, LM Studio client error handling)

Design decisions

• Single-shot, no multi-turn state: every call is independent, no session handling needed in the server.

• Several fixed personas, selectable by parameter: no freely-composed system prompts per call, just a curated set in personas.py.

• LM Studio URL defaults to http://localhost:1234, overridable via KONTRA_KI_LM_STUDIO_URL for setups where LM Studio runs on a different port or host (e.g. another machine on the local network).

• isError flag is optional and persona-restricted: only inquisitor and code_skeptic have genuine pass/fail semantics. The pure discussion personas (diabolo, cynic, antithesis, chief_architect) have no verdict - their critique is opinion, not a ruling; strict=True there is rejected with an error instead of silently ignored.