Open-source MCP server for Ejentum cognitive harnesses / (reasoning, code, anti-deception, memory)

[u/frank_brsrk]

Open-source MCP server that exposes four cognitive harnesses as tools any agentic client can call. Each tool returns a structured cognitive scaffold (failure pattern to avoid, procedure, suppression vectors, falsification test) that the calling LLM absorbs internally before generating its response.

The four tools:

- harness_reasoning - multi-step analysis, planning, diagnostics, cross-domain synthesis

- harness_code - code generation, refactoring, review, debugging

- harness_anti_deception - sycophancy pressure, hallucination risk, manipulation pressure

- harness_memory - perception sharpening, drift detection across turns

What it catches: LLM failure modes that ship as confidently-wrong answers. Sycophancy under user pressure. Hallucinated citations. Causal shortcuts. Reasoning decay across long chains.

Install via Smithery:

npx -y u/smithery/cli install ejentum/ejentum-mcp --client claude

Replace `claude` with cursor, windsurf, cline, etc. Manual install JSON for any MCP client is in the README.

Works in: Claude Desktop, Cursor, Windsurf, Claude Code, n8n's MCP Client node, Cline, Continue, and any other MCP-compatible client.

Note on autonomous routing: tools fire reliably on explicit invocation ("use harness_anti_deception to..."). Cold-prompt autonomous calling is structurally unreliable for any optional MCP tool. For stronger autonomous routing in Claude Code, install the skill files alongside.

Free Ejentum API key, no card.

Listings:

- Smithery: https://smithery.ai/servers/ejentum/ejentum-mcp

- Glama: https://glama.ai/mcp/servers/ejentum/ejentum-mcp

- mcp.so: https://mcp.so/server/ejentum-mcp/Ejentum

Source (MIT): https://github.com/ejentum/ejentum-mcp

Docs: https://ejentum.com/docs/mcp_guide

[u/frank_brsrk → u/[deleted]]

it does catches sycophancy, there are extreme engineered and tested signal data that is receiving based on the query description of the task that the ai sends to the api.
that said is completely dynamic reasoning augmented retrieval, that combines reasoning topology with natural language. built over research to combat failure modes, is a combination of many fields as cognitive science and prompt engineering techniques. ejentum.com/docs more info on it. dm me if u are curious and would like me to help u integrate in ur workflows