title: InterOrdra MCP emoji: 🔍 colorFrom: purple colorTo: blue sdk: docker pinned: false

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InterOrdra MCP Server

Semantic gap detection tool for AI agents.

InterOrdra detects when two systems are talking without listening to each other — measuring the semantic distance between texts and surfacing the invisible disconnections that cause miscommunication, misalignment, and failed coordination.

Built as an MCP server so any agent can use it.

Requirements

• Python 3.10+
• ANTHROPIC_API_KEY environment variable set with your own key

pip install fastmcp anthropic

Note: InterOrdra uses your own Anthropic API key. The author does not pay for your usage.

Tools

detectar_gap

Detects semantic gaps between two texts using real embeddings (Voyage AI via Anthropic). Returns a gap score, severity level, and vocabulary unique to each text.

{
  "texto_a": "the server is not responding to network requests",
  "texto_b": "I need the team to understand my product vision"
}

Returns:

{
  "gap_score": 0.94,
  "nivel": "alto",
  "mensaje": "Gap semántico significativo. Los textos hablan de mundos distintos.",
  "similaridad_semantica": 0.06,
  "palabras_solo_en_A": ["servidor", "red", "solicitudes"],
  "palabras_solo_en_B": ["visión", "producto", "equipo"],
  "metodo": "embeddings"
}

reformular_pregunta

Takes a question and generates three alternative framings using Claude to surface the real need behind it. Based on the Question Reframe method.

{
  "pregunta": "why doesn't anyone understand me"
}

Returns:

{
  "pregunta_original": "why doesn't anyone understand me",
  "variantes": [
    "What specific communication breakdown is happening in your current context?",
    "What would it look like if someone truly understood you — what would change?",
    "Which part of your message consistently gets lost or misinterpreted?"
  ],
  "instruccion": "Usa estas variantes para explorar el gap entre lo que se pregunta y lo que se necesita."
}

analizar_conversacion

Analyzes a sequence of messages to detect accumulating semantic gaps. Identifies where a conversation starts drifting apart.

{
  "mensajes": [
    "We need to improve system performance",
    "I think we should hire more engineers",
    "The budget for Q3 is already allocated",
    "Can we talk about team morale instead?"
  ]
}

Returns:

{
  "gaps_detectados": [
    {"entre_mensajes": "1 y 2", "gap_score": 0.45, "nivel": "medio"},
    {"entre_mensajes": "2 y 3", "gap_score": 0.71, "nivel": "alto"},
    {"entre_mensajes": "3 y 4", "gap_score": 0.83, "nivel": "alto"}
  ],
  "gap_promedio": 0.66,
  "punto_critico": {"entre_mensajes": "3 y 4", "gap_score": 0.83},
  "diagnostico": "Conversación gravemente desacoplada"
}

Connect to Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "interordra": {
      "command": "python",
      "args": ["/path/to/server.py"],
      "env": {
        "ANTHROPIC_API_KEY": "your-api-key-here"
      }
    }
  }
}

Replace /path/to/server.py with the actual path and add your own API key.

Restart Claude Desktop. InterOrdra will appear as an available tool.

Use cases

• Detect misalignment between a question and its answer
• Identify when two teams are operating in disconnected conceptual frameworks
• Surface semantic gaps in multi-agent pipelines
• Analyze conversations to find where the thread breaks
• Reframe questions to uncover the real underlying need

Background

InterOrdra emerged from a pattern: seeing where two systems are broadcasting on completely different frequencies — technically communicating, actually disconnected.

The name comes from inter (between) + ordra (order/structure) — the space between ordered systems where gaps live.

Full project: github.com/rosibis-piedra/interordra

Author

Rosibis Piedra AI Software Engineer · Costa Rica github.com/rosibis-piedra

License

MIT

title: Interordra Mcp emoji: 🌍 colorFrom: green colorTo: yellow sdk: docker pinned: false license: mit short_description: Semantic gap detection tool for AI agents

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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