Activation Tools

Three tools for querying the connectome through spreading activation, task-based priming, and resonance analysis.

`m1nd.activate`

Spreading activation query across the connectome. The primary search tool – propagates signal from seed nodes through the graph across four dimensions (structural, semantic, temporal, causal), with XLR noise cancellation and optional ghost edge / structural hole detection.

Parameters

Parameter	Type	Required	Default	Description
`query`	`string`	Yes	–	Search query for spreading activation. Matched against node labels, tags, and provenance to find seed nodes.
`agent_id`	`string`	Yes	–	Calling agent identifier.
`top_k`	`integer`	No	`20`	Number of top activated nodes to return.
`dimensions`	`string[]`	No	`["structural", "semantic", "temporal", "causal"]`	Activation dimensions to include. Each dimension contributes independently to the final activation score. Values: `"structural"`, `"semantic"`, `"temporal"`, `"causal"`.
`xlr`	`boolean`	No	`true`	Enable XLR noise cancellation. Filters low-confidence activations to reduce false positives.
`include_ghost_edges`	`boolean`	No	`true`	Include ghost edge detection. Ghost edges are probable but unconfirmed connections inferred from activation patterns.
`include_structural_holes`	`boolean`	No	`false`	Include structural hole detection. Identifies nodes that should be connected but are not.

Example Request

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "m1nd.activate",
    "arguments": {
      "agent_id": "orchestrator",
      "query": "session pool management",
      "top_k": 5,
      "include_ghost_edges": true
    }
  }
}

Example Response

{
  "query": "session pool management",
  "seeds": [
    { "node_id": "file::pool.py", "label": "pool.py", "relevance": 0.95 }
  ],
  "activated": [
    {
      "node_id": "file::pool.py",
      "label": "pool.py",
      "type": "file",
      "activation": 0.89,
      "dimensions": { "structural": 0.92, "semantic": 0.95, "temporal": 0.78, "causal": 0.71 },
      "pagerank": 0.635,
      "tags": ["session", "pool"],
      "provenance": {
        "source_path": "backend/pool.py",
        "line_start": 1,
        "line_end": 245,
        "canonical": true
      }
    },
    {
      "node_id": "file::pool.py::class::ConnectionPool",
      "label": "ConnectionPool",
      "type": "class",
      "activation": 0.84,
      "dimensions": { "structural": 0.88, "semantic": 0.91, "temporal": 0.72, "causal": 0.65 },
      "pagerank": 0.412,
      "tags": ["pool", "session"],
      "provenance": {
        "source_path": "backend/pool.py",
        "line_start": 15,
        "line_end": 180,
        "canonical": true
      }
    }
  ],
  "ghost_edges": [
    {
      "source": "pool.py",
      "target": "recovery.py",
      "shared_dimensions": ["semantic", "causal"],
      "strength": 0.34
    }
  ],
  "structural_holes": [],
  "plasticity": {
    "edges_strengthened": 12,
    "edges_decayed": 3,
    "ltp_events": 1,
    "priming_nodes": 5
  },
  "elapsed_ms": 31.2
}

When to Use

Primary search – the default way to ask “what in the codebase relates to X?”
Exploration – when you know a topic but not the specific files
Context building – before working on a feature, activate its topic to find all related code
Gap detection – enable include_structural_holes to find missing connections

Side Effects

Activate has plasticity side effects: it strengthens edges between activated nodes and decays inactive edges. This makes the graph learn from usage patterns over time.

m1nd.warmup – activate + prime for a specific task
m1nd.seek – intent-aware search (finds code by purpose, not just keywords)
m1nd.perspective.start – wraps activate into a navigable perspective
m1nd.learn – explicitly provide feedback on activation results

`m1nd.warmup`

Task-based warmup and priming. Activates the graph around a task description and applies a temporary boost to relevant nodes, preparing the graph for focused work. The boost decays naturally over time.

Parameters

Parameter	Type	Required	Default	Description
`task_description`	`string`	Yes	–	Description of the task to warm up for. Natural language.
`agent_id`	`string`	Yes	–	Calling agent identifier.
`boost_strength`	`number`	No	`0.15`	Priming boost strength applied to relevant nodes. Range: 0.0 to 1.0. Higher values make the primed nodes more dominant in subsequent queries.

Example Request

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "m1nd.warmup",
    "arguments": {
      "agent_id": "orchestrator",
      "task_description": "Refactor the messaging routing module to support group chats",
      "boost_strength": 0.2
    }
  }
}

Example Response

{
  "task": "Refactor the messaging routing module to support group chats",
  "primed_nodes": 23,
  "top_primed": [
    { "node_id": "file::messaging_routes.py", "label": "messaging_routes.py", "boost": 0.2 },
    { "node_id": "file::messaging.py", "label": "messaging.py", "boost": 0.18 },
    { "node_id": "file::messaging_models.py", "label": "messaging_models.py", "boost": 0.15 },
    { "node_id": "file::handler.py", "label": "handler.py", "boost": 0.12 }
  ],
  "elapsed_ms": 18.5
}

When to Use

Session start – warm up before a focused work session to bias the graph toward relevant code
Context switch – when changing tasks, warm up the new topic
Before complex queries – warmup biases subsequent activate, impact, and why queries toward the warmed-up region

Side Effects

Applies temporary priming boosts to node activations. These boosts decay naturally and are NOT persisted across server restarts.

m1nd.activate – raw activation query without the priming boost
m1nd.trail.resume – restores a full investigation context including activation boosts

`m1nd.resonate`

Resonance analysis: standing waves, harmonics, sympathetic pairs, and resonant frequencies in the graph. Identifies nodes that form natural clusters of mutual reinforcement – the “harmonics” of the connectome.

Parameters

Parameter	Type	Required	Default	Description
`query`	`string`	No	–	Search query to find seed nodes for resonance analysis. Provide either `query` or `node_id` (or neither for global resonance).
`node_id`	`string`	No	–	Specific node identifier to use as seed. Alternative to `query`.
`agent_id`	`string`	Yes	–	Calling agent identifier.
`top_k`	`integer`	No	`20`	Number of top resonance results to return.

Example Request

{
  "jsonrpc": "2.0",
  "id": 3,
  "method": "tools/call",
  "params": {
    "name": "m1nd.resonate",
    "arguments": {
      "agent_id": "orchestrator",
      "query": "authentication flow",
      "top_k": 10
    }
  }
}

Example Response

{
  "seed": "authentication flow",
  "harmonics": [
    {
      "node_id": "file::auth_discovery.py",
      "label": "auth_discovery.py",
      "amplitude": 0.92,
      "harmonic_order": 1
    },
    {
      "node_id": "file::middleware.py",
      "label": "middleware.py",
      "amplitude": 0.71,
      "harmonic_order": 2
    },
    {
      "node_id": "file::principal_registry.py",
      "label": "principal_registry.py",
      "amplitude": 0.68,
      "harmonic_order": 2
    }
  ],
  "sympathetic_pairs": [
    { "a": "auth_discovery.py", "b": "principal_registry.py", "coupling": 0.84 }
  ],
  "elapsed_ms": 45.0
}

When to Use

Deep structural analysis – find natural clusters of mutually reinforcing code
Pattern discovery – identify which modules form coherent subsystems
Architecture review – see which modules resonate together (and which do not)
Refactoring – resonance groups suggest natural module boundaries

Side Effects

Read-only. No plasticity side effects.

m1nd.activate – simpler spreading activation without harmonic analysis
m1nd.fingerprint – finds structurally equivalent nodes
m1nd.missing – finds gaps in the resonance structure

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