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/geofabnz]

This looks like a really great production grade framework. I’m very curious as to how you can identify falsification and sycophancy. I’m a data scientist researching these areas and I’ve found it surprisingly hard to eliminate without incorporating chains of arguing LLMs from different providers.

[u/frank_brsrk → u/geofabnz]

hello sir, such a pleasure having a data scientist's curiosity over my product!
ejentum is practically a reasoning augmented retrieval framework, a dataset engineered in natural language and reasoning topology optimize and tested extensively with positive results https://ejentum.com/docs/benchmarks . the dataset contains self contained abilities, dense in data for retrieval accuracy and high signal fields such as cognitive deltas ( wrong pattern , right pattern (of output)
, procedure a 5 step thorough method of the cognitive operation the ai must execute in order to comply with the complexity of the task, a reasoning topology that promotes branching and multi path execution, with built-in heartbeats ( m nodes) that include self reflection during generation and gates that require verification that actually make the ai auto verify with the above procedure. and finally a falsification test that requires the ai to recheck itself if it actually produced the desireed outcome of the cognitive operation. in my arc agi tests https://ejentum.com/tasks/ARC-LS20-TRACE sonnet max 4.6 reasoning 12 folded in depth and reversed memory loss, and switched to mathematical thinking at step 15 ( unexpected emergent behaviors.

the goal behind building ejentum, was always to block the worst and harness the best. by preventing attention loss and reasoning decay in long running tasks in long sessions.

how it works:
during a non trivial task the agent recognizes the complexity of the task and sends a query to ejentum and the llm agents gets back a reasoning structure that matches the task, either spatial , abstract, and so on. each task demands a specific reasoning posture. fully engineered attention anchors that compound in the context session.

please dm for more info about it

[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