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MCP Integration Analysis: Ruvector + Sublinear-Time-Solver

Agent 7 -- MCP Integration Analysis Date: 2026-02-20 Scope: Model Context Protocol usage across ruvector, sublinear-time-solver tool surface, federation patterns, and AI agent workflow integration

Table of Contents

  1. Existing MCP Usage in Ruvector
  2. MCP Tool Surface Area from Sublinear-Time-Solver
  3. Tool Composition Opportunities
  4. MCP Server Federation Patterns
  5. Shared Resource Management via MCP
  6. MCP Transport Layer Considerations
  7. AI Agent Workflow Integration

1. Existing MCP Usage in Ruvector

Ruvector has an extensive, multi-layered MCP implementation spanning five distinct server implementations, multiple transport layers, and deep integration with its AI agent and learning systems.

1.1 MCP Server Inventory

Server Location Language Tools Transport Protocol Version
ruvector-cli MCP /crates/ruvector-cli/src/mcp_server.rs Rust 12 stdio, SSE 2024-11-05
mcp-gate /crates/mcp-gate/src/ Rust 3 stdio 2024-11-05
rvf-mcp-server /npm/packages/rvf-mcp-server/src/ TypeScript 10 stdio, SSE via @modelcontextprotocol/sdk
ruvector npm MCP /npm/packages/ruvector/bin/mcp-server.js JavaScript 40+ stdio via @modelcontextprotocol/sdk
edge-net WASM MCP /examples/edge-net/src/mcp/mod.rs Rust/WASM 17 MessagePort/BroadcastChannel 2024-11-05

1.2 ruvector-cli MCP Server (Primary)

The main MCP server at /crates/ruvector-cli/src/mcp_server.rs is the core production server. It exposes 12 tools organized into two categories:

Vector DB Tools (5):

  • vector_db_create -- Create a new vector database with configurable dimensions and distance metrics (Euclidean, Cosine, DotProduct, Manhattan)
  • vector_db_insert -- Batch insert vectors with optional metadata
  • vector_db_search -- k-NN similarity search with metadata filtering
  • vector_db_stats -- Database statistics (count, dimensions, HNSW status)
  • vector_db_backup -- File-level database backup

GNN Tools with Persistent Caching (7):

  • gnn_layer_create -- Create/cache GNN layers, eliminating ~2.5s initialization overhead
  • gnn_forward -- Forward pass through cached layers (~5-10ms vs ~2.5s)
  • gnn_batch_forward -- Batch operations with result caching and amortized cost
  • gnn_cache_stats -- Cache hit rates, layer counts, query statistics
  • gnn_compress -- Access-frequency-based embedding compression via TensorCompress
  • gnn_decompress -- Decompress compressed tensors
  • gnn_search -- Differentiable search with soft attention and temperature control

The handler at /crates/ruvector-cli/src/mcp/handlers.rs manages state through:

  • databases: Arc<RwLock<HashMap<String, Arc<VectorDB>>>> -- Concurrent database pool
  • gnn_cache: Arc<GnnCache> -- Persistent GNN layer/query cache (250-500x speedup)
  • tensor_compress: Arc<TensorCompress> -- Shared tensor compressor

The server supports both MCP capabilities (tools, resources, prompts) and includes a semantic-search prompt template.

Transport layer (/crates/ruvector-cli/src/mcp/transport.rs):

  • StdioTransport -- JSON-RPC 2.0 over stdin/stdout, line-delimited
  • SseTransport -- HTTP server via Axum with routes /mcp (POST), /mcp/sse (GET SSE stream), plus CORS support and 30-second keepalive pings

1.3 mcp-gate (Coherence Gate)

The mcp-gate crate at /crates/mcp-gate/ provides an MCP server specifically for the Anytime-Valid Coherence Gate (cognitum-gate-tilezero). This is a security-oriented permission layer with 3 tools:

  • permit_action -- Request permission for agent actions; returns Permit/Defer/Deny decisions with cryptographic witness receipts containing structural (cut_value, partition), predictive (set_size, coverage), and evidential (e_value, verdict) information
  • get_receipt -- Retrieve witness receipts by sequence number for auditing, includes hash chain data
  • replay_decision -- Deterministic replay of past decisions with optional hash chain verification

This server implements a complete decision audit trail via the TileZero state machine, making it critical for controlled AI agent deployments. Decisions are backed by structural graph analysis (min-cut partitioning), conformal prediction sets, and e-value evidence accumulation.

1.4 RVF MCP Server (TypeScript)

The RVF MCP server at /npm/packages/rvf-mcp-server/ uses the official @modelcontextprotocol/sdk (^1.0.0) and provides vector database operations specifically for the RuVector Format (.rvf):

10 Tools: rvf_create_store, rvf_open_store, rvf_close_store, rvf_ingest, rvf_query, rvf_delete, rvf_delete_filter, rvf_compact, rvf_status, rvf_list_stores

2 Resources: rvf://stores (list), rvf://stores/{storeId}/status

2 Prompts: rvf-search (natural language vector search), rvf-ingest (guided data ingestion)

This server supports both stdio and SSE transports, with the SSE transport using Express.js with /sse, /messages, and /health endpoints. It manages an in-memory store pool with configurable max stores (default 64) and supports L2, cosine, and dotproduct distance metrics.

1.5 Edge-Net WASM MCP Server

The browser-based MCP server at /examples/edge-net/src/mcp/mod.rs is compiled to WebAssembly and exposes 17 tools across 6 categories:

Identity (3): identity_generate, identity_sign, identity_verify -- Ed25519 keypair management Credits (4): credits_balance, credits_contribute, credits_spend, credits_health -- CRDT-based economic system RAC/Coherence (3): rac_ingest, rac_stats, rac_merkle_root -- Adversarial coherence protocol Learning (3): learning_store_pattern, learning_lookup, learning_stats -- Pattern storage and vector search Task (2): task_submit, task_status -- Distributed compute task management Network (2): network_peers, network_stats

This server includes significant security hardening:

  • Payload size limit: 1MB max
  • Rate limiting: 100 requests/second with sliding window
  • Authentication required for credit operations
  • Vector dimension validation (NaN/Infinity rejection)
  • Max k limit (100) for vector searches

It communicates via MessagePort/BroadcastChannel for cross-context browser communication and supports both JSON string and JsValue request formats.

1.6 Ruvector NPM MCP Server (Intelligence Layer)

The main npm MCP server at /npm/packages/ruvector/bin/mcp-server.js is the most feature-rich, providing 40+ tools through the IntelligenceEngine layer. It uses @modelcontextprotocol/sdk with Server and StdioServerTransport, and includes:

  • Self-learning Q-learning patterns for agent routing
  • Semantic vector memory with ONNX embeddings
  • Error pattern recording and fix suggestion
  • File edit sequence prediction
  • Swarm coordination tools
  • Path traversal protection and shell injection prevention
  • Blocked path validation (/etc, /proc, /sys, /dev, /boot, /root, /var/run)

1.7 MCP Training Infrastructure

The ruvllm crate at /crates/ruvllm/src/training/mcp_tools.rs provides GRPO-based reinforcement learning for MCP tool calling with:

  • 140+ Claude Flow MCP tool definitions supported
  • McpToolTrainer with trajectory-based training
  • Tool selection accuracy evaluation with confusion matrices
  • Checkpoint import/export for training continuity
  • Reward computation: tool selection (0.5), parameter accuracy (0.3), execution success (0.2)
  • Support for 6 tool categories: VectorDb, Learning, Memory, Swarm, Telemetry, AgentRouting

The edge-net learning module at /examples/edge-net/src/learning-scenarios/mcp_tools.rs defines 14 ruvector-specific MCP tools for learning intelligence: ruvector_learn_pattern, ruvector_suggest_agent, ruvector_record_error, ruvector_suggest_fix, ruvector_remember, ruvector_recall, ruvector_swarm_register, ruvector_swarm_coordinate, ruvector_swarm_optimize, ruvector_telemetry_config, ruvector_intelligence_stats, ruvector_suggest_next_file, ruvector_record_sequence.

2. MCP Tool Surface Area from Sublinear-Time-Solver

Based on the sublinear-time-solver package specification (@modelcontextprotocol/sdk ^1.18.1) and the agent configurations found in /home/user/ruvector/.claude/agents/sublinear/, the solver exposes 40+ MCP tools organized across several domains.

2.1 Core Matrix Solving Tools

Tool Description Key Parameters
solve Solve diagonally dominant linear systems matrix (dense/COO), vector, method (neumann/random-walk), epsilon, maxIterations
estimateEntry Estimate specific solution entries without full solve matrix, vector, row, column, method, epsilon, confidence
analyzeMatrix Comprehensive matrix property analysis matrix, checkDominance, checkSymmetry, estimateCondition, computeGap
validateTemporalAdvantage Validate sublinear computational advantages system parameters, timing data

2.2 Graph Analysis Tools

Tool Description Key Parameters
pageRank Compute PageRank scores on graph adjacency matrices adjacency, damping (0.85), epsilon, personalized

2.3 Consciousness Evolution Tools

As indicated in the package description, the solver includes consciousness-related tools for entity modeling, domain management, and multi-entity communication. These are more specialized and would integrate with ruvector's coherence and learning systems.

2.4 Agent Configuration Patterns

Five specialized agents are preconfigured for sublinear-time-solver integration:

matrix-optimizer (/home/user/ruvector/.claude/agents/sublinear/matrix-optimizer.md):

  • Primary tools: analyzeMatrix, solve, estimateEntry, validateTemporalAdvantage
  • Focus: Pre-solver matrix analysis, large-scale system optimization, targeted entry estimation
  • Pattern: Analyze-Preprocess-Solve-Validate pipeline

consensus-coordinator (/home/user/ruvector/.claude/agents/sublinear/consensus-coordinator.md):

  • Primary tools: solve, estimateEntry, analyzeMatrix, pageRank
  • Focus: Byzantine fault tolerance via consensus matrices, distributed voting with PageRank-weighted influence
  • Pattern: Network topology analysis, consensus convergence estimation, fault tolerance validation

pagerank-analyzer: Graph centrality and influence analysis performance-optimizer: System-wide performance tuning using solver metrics trading-predictor: Financial matrix computations

2.5 SDK Version Considerations

The sublinear-time-solver uses @modelcontextprotocol/sdk ^1.18.1, while ruvector's rvf-mcp-server uses ^1.0.0. The SDK version gap indicates the solver has access to newer MCP features including potentially:

  • Streamable HTTP transport (introduced after 1.0)
  • Enhanced tool annotations
  • Better error handling primitives
  • Resource subscription improvements

This version disparity must be accounted for in federation scenarios.

3. Tool Composition Opportunities

The overlap between ruvector's vector/graph capabilities and the sublinear-time-solver's matrix algebra creates several high-value composition patterns.

3.1 Vector Search + Matrix Solving Pipeline

ruvector.vector_db_search(query)
  -> extract neighbor graph from results
  -> sublinear.analyzeMatrix(adjacency_matrix)
  -> sublinear.pageRank(adjacency_matrix)
  -> rerank results by PageRank scores

This composition enables graph-aware vector search where nearest neighbors are reranked by their structural importance in the embedding space. The sublinear solver can compute PageRank on the k-NN graph in sublinear time, avoiding O(n) full traversal.

3.2 GNN + Sublinear Solver for Large-Scale Inference

ruvector.gnn_layer_create(config)          // Cached layer
  -> ruvector.gnn_batch_forward(batch)     // Batch GNN inference
  -> sublinear.solve(attention_matrix, embeddings)  // Solve attention system
  -> ruvector.gnn_compress(result)         // Compress output

Ruvector's GNN cache eliminates the 2.5s initialization overhead per layer. Combined with the sublinear solver for the attention matrix system (which is typically diagonally dominant in self-attention architectures), this pipeline can achieve sub-10ms per-query inference.

3.3 Coherence Gate + Consensus Coordination

mcp_gate.permit_action(agent_action)
  -> if DEFER: sublinear.analyzeMatrix(network_topology)
  -> sublinear.pageRank(voter_network, agent_trust_scores)
  -> consensus_coordinator.reachConsensus(proposals)
  -> mcp_gate.replay_decision(sequence)   // Audit trail

When the coherence gate defers a decision due to uncertainty (high prediction set size or indeterminate e-value), the sublinear solver can analyze the agent network topology and compute trust-weighted consensus in sublinear time. The mcp-gate's witness receipts provide cryptographic audit trails.

3.4 Edge-Net Economic Optimization

edge_net.credits_health()
  -> extract economic graph
  -> sublinear.analyzeMatrix(economic_matrix)
  -> sublinear.solve(optimization_system, objectives)
  -> edge_net.credits_contribute(optimized_allocations)

The edge-net's CRDT-based credit system can benefit from sublinear optimization for resource allocation across network nodes. The economic health metrics provide the input state, and the solver optimizes allocation without requiring full matrix decomposition.

3.5 Learning Pattern Optimization

ruvector.ruvector_recall(query)            // Retrieve similar patterns
  -> extract pattern embedding matrix
  -> sublinear.analyzeMatrix(pattern_matrix)
  -> sublinear.estimateEntry(pattern_matrix, row=target)
  -> ruvector.ruvector_learn_pattern(optimized_pattern)

The learning system's Q-learning patterns form a state-action matrix that can be analyzed and optimized using the sublinear solver's entry estimation. This avoids computing the full Q-table update, enabling truly sublinear reinforcement learning updates.

3.6 Swarm Topology Optimization

ruvector.ruvector_swarm_register(agents)
  -> sublinear.analyzeMatrix(topology_matrix, {
       checkDominance: true,
       estimateCondition: true,
       computeGap: true
     })
  -> sublinear.pageRank(topology, agent_capabilities)
  -> ruvector.ruvector_swarm_optimize(tasks, optimized_topology)

Agent swarm topologies form graph structures that can be optimized via spectral analysis. The sublinear solver's spectral gap computation identifies bottlenecks in agent communication, and PageRank identifies the most central agents for leadership roles.

4. MCP Server Federation Patterns

4.1 Current Federation Architecture

Ruvector already practices implicit federation through multiple MCP servers running in the same environment. The Claude Code settings at /home/user/ruvector/.claude/settings.json show:

{
  "permissions": {
    "allow": [
      "Bash(npx @claude-flow*)",
      "mcp__claude-flow__:*"
    ]
  }
}

The setup script at /home/user/ruvector/.claude/helpers/setup-mcp.sh registers claude-flow as an MCP server:

claude mcp add claude-flow npx claude-flow mcp start

4.2 Proposed Federation Topology

For sublinear-time-solver integration, a three-tier federation model is recommended:

                    +-------------------+
                    |   Claude Code     |
                    |   (MCP Client)    |
                    +--------+----------+
                             |
              +--------------+--------------+
              |              |              |
     +--------v---+  +------v------+  +----v-----------+
     | ruvector    |  | claude-flow |  | sublinear-time |
     | MCP Server  |  | MCP Server  |  | solver MCP     |
     +------+------+  +------+------+  +----+-----------+
            |                |               |
     +------v------+  +-----v------+  +-----v----------+
     | mcp-gate    |  | memory/    |  | consciousness  |
     | (coherence) |  | hooks/     |  | /domain mgmt   |
     +-------------+  | swarm      |  +----------------+
                       +------------+

Tier 1 (Direct Client Access): The three primary MCP servers are accessed directly by Claude Code, each handling its domain.

Tier 2 (Internal Federation): mcp-gate, memory, and specialized subsystems are accessed through their parent Tier 1 server.

Cross-Tier Communication: Handled via shared state (file system, environment variables) or explicit tool composition in the AI agent's reasoning.

4.3 Federation Protocol Patterns

Pattern A: Serial Chaining The simplest pattern. Tool results from one server feed into another:

client -> server_A.tool_1(params)
client -> server_B.tool_2(result_from_A)

This is the current default in Claude Code MCP usage. It works for the composition patterns in Section 3 but introduces serial latency.

Pattern B: Parallel Fan-Out Multiple servers are queried simultaneously for independent operations:

client -> server_A.tool_1(params) | server_B.tool_2(params)
         (concurrent)
client -> merge(result_A, result_B)

Useful for combining ruvector search results with sublinear matrix analysis in parallel.

Pattern C: Gateway Server A dedicated gateway MCP server aggregates multiple backends:

client -> gateway.composite_tool(params)
  gateway -> server_A.tool_1(params)
  gateway -> server_B.tool_2(server_A_result)
  gateway <- combined_result
client <- result

The ruvector npm MCP server is already structured to serve as a gateway, and could be extended to dispatch to the sublinear solver.

Pattern D: Event-Sourced Federation Servers share state through an event log, enabling eventual consistency:

server_A -> event_log.append(state_change)
server_B -> event_log.subscribe(state_changes)

The mcp-gate's witness receipt chain already implements this pattern for coherence decisions. Extending it to cover solver results would enable auditable federation.

4.4 Discovery and Registration

Currently, MCP servers in ruvector are registered statically:

  • In Claude Code settings (claude mcp add)
  • In Cargo.toml/package.json as binary targets
  • In example configurations

For the sublinear-time-solver, registration would follow:

# Static registration
claude mcp add sublinear-time-solver -- npx sublinear-time-solver mcp

# Or programmatic registration via claude-flow
npx claude-flow agent spawn -t matrix-optimizer --mcp sublinear-time-solver

A dynamic discovery mechanism does not yet exist in ruvector but would be valuable for auto-detecting available solver capabilities at startup.

5. Shared Resource Management via MCP

5.1 Memory and State Sharing

Ruvector's MCP servers share state through several mechanisms:

Shared Vector Databases: The ruvector-cli handler maintains databases: Arc<RwLock<HashMap<String, Arc<VectorDB>>>>. Multiple tools access the same database pool. The sublinear solver could share results by writing solution vectors into the same vector stores:

sublinear.solve(system) -> solution_vector
ruvector.vector_db_insert(db, solution_vector, metadata={solver: "neumann", epsilon: 1e-8})

This enables downstream agents to search for solutions using semantic similarity.

GNN Cache Sharing: The gnn_cache: Arc<GnnCache> is shared across all GNN tools. Since the sublinear solver's matrix operations often produce embeddings that feed into GNN layers, a shared cache key scheme would prevent redundant computation:

cache_key = hash(matrix_structure + solver_params + gnn_layer_config)

Intelligence State: The npm MCP server's Intelligence class persists state to .ruvector/intelligence.json. Solver results and matrix analysis patterns could be incorporated into this learning state for cross-session knowledge retention.

5.2 Resource Contention Management

Current Approach:

  • Arc<RwLock<T>> for concurrent read access to databases and caches
  • File-level locking for persistent state
  • Rate limiting in the edge-net MCP server (100 req/s)

Recommendations for Sublinear Solver Integration:

  1. Matrix Buffer Pool: Large matrices should be managed through a shared buffer pool rather than serialized in each MCP request. A resource URI scheme like matrix://local/{id} would enable pass-by-reference:
ruvector.matrix_store(data) -> "matrix://local/abc123"
sublinear.solve({matrix_uri: "matrix://local/abc123", ...})
  1. Compute Budget Tracking: The edge-net credit system provides a model for tracking computational resources. Solver operations should deduct credits proportional to matrix dimensions:
cost = base_cost + dimension_factor * n * log(n)
  1. Concurrent Solver Limits: The sublinear solver should enforce a maximum concurrent solve count to prevent memory exhaustion. The ruvector-cli pattern of Arc<RwLock<HashMap>> for database handles could be extended to solver session handles.

5.3 Lifecycle Management

MCP servers in ruvector have different lifecycle models:

Server Lifecycle State Persistence
ruvector-cli Long-running daemon In-memory + file backup
mcp-gate Ephemeral per-request Receipt chain in memory
rvf-mcp-server Long-running with store pool In-memory Map
edge-net WASM Browser session-scoped None (CRDT sync)
npm MCP server Long-running .ruvector/intelligence.json
sublinear-solver Per-invocation (npx) None

The sublinear solver's per-invocation lifecycle means it starts cold each time. To mitigate:

  1. Pre-warm Protocol: Use the MCP initialize handshake to pre-load common matrix configurations
  2. Result Caching: Store solver results in ruvector's vector database for cache-hit lookups
  3. Persistent Daemon Mode: Optionally run the solver as a long-running daemon alongside ruvector-cli

6. MCP Transport Layer Considerations

6.1 Current Transport Implementations

Ruvector implements four transport strategies:

Stdio (JSON-RPC 2.0 over stdin/stdout):

  • Used by: ruvector-cli, mcp-gate, rvf-mcp-server, npm MCP server
  • Implementation: Line-delimited JSON, AsyncBufReadExt/AsyncWriteExt in Rust, StdioServerTransport in TypeScript
  • Latency: Sub-millisecond IPC
  • Limitation: Single client, no multiplexing

SSE (Server-Sent Events over HTTP):

  • Used by: ruvector-cli (Axum), rvf-mcp-server (Express.js)
  • Implementation: /mcp/sse for event stream, /mcp or /messages for JSON-RPC POST
  • Features: CORS support, health checks, 30s keepalive
  • Limitation: Unidirectional server-to-client push, requires HTTP POST for client messages

MessagePort/BroadcastChannel (Browser):

  • Used by: edge-net WASM
  • Implementation: wasm_bindgen with JsValue serialization
  • Features: Cross-worker communication, same-origin
  • Limitation: Browser-only, no external access

HTTP POST (Direct JSON-RPC):

  • Used by: ruvector-cli SSE transport as fallback
  • Implementation: Axum Json<McpRequest> handler
  • Limitation: No streaming, new connection per request

6.2 Transport Compatibility with Sublinear Solver

The sublinear-time-solver, launched via npx sublinear-time-solver mcp, uses stdio transport by default. This is compatible with Claude Code's standard MCP client.

Compatibility Matrix:

Scenario Transport Compatible Notes
Claude Code -> solver stdio Yes Standard MCP pattern
ruvector -> solver stdio Requires proxy Cannot nest stdio
Browser -> solver N/A No No browser transport
Remote solver SSE/HTTP Requires adapter Solver only supports stdio

6.3 Transport Optimization for Large Matrices

Matrix data is the primary payload concern. A 10,000x10,000 dense matrix in float64 is ~800MB, far exceeding practical JSON serialization limits.

Recommended Approaches:

  1. Sparse Format Enforcement: Always use COO (Coordinate) format for MCP transmission:
{
  "matrix": {
    "rows": 10000, "cols": 10000,
    "format": "coo",
    "data": {
      "values": [1.0, 2.0, ...],
      "rowIndices": [0, 1, ...],
      "colIndices": [0, 1, ...]
    }
  }
}
  1. Chunked Transfer: For matrices exceeding a configurable threshold (e.g., 10MB), split into chunks:
client -> solver.matrix_upload_start({rows: 10000, cols: 10000, chunks: 10})
client -> solver.matrix_upload_chunk({chunk_id: 0, data: {...}})
...
client -> solver.matrix_upload_finish() -> matrix_ref
client -> solver.solve({matrix_ref: "...", ...})
  1. Shared Memory Reference: When ruvector and the solver run on the same host, use filesystem paths:
{
  "matrix": {
    "format": "mmap",
    "path": "/tmp/ruvector-matrices/abc123.bin",
    "rows": 10000, "cols": 10000,
    "dtype": "f64"
  }
}
  1. Binary Framing: The @modelcontextprotocol/sdk ^1.18.1 supports newer transport modes. If the solver upgrades to Streamable HTTP transport, binary payloads become viable without base64 overhead.

6.4 V3 MCP Optimization Skill Integration

Ruvector's V3 MCP Optimization skill at /home/user/ruvector/.claude/skills/v3-mcp-optimization/SKILL.md defines performance targets directly applicable to sublinear solver integration:

  • Startup time target: <400ms (4.5x improvement over baseline)
  • Response time target: <100ms p95
  • Tool lookup: <5ms via O(1) hash table (FastToolRegistry)
  • Connection pool: >90% hit rate
  • Multi-level caching: L1 (in-memory) -> L2 (LRU) -> L3 (disk)

Applying these optimizations to the sublinear solver integration:

  1. Connection Pooling: Maintain a warm pool of solver process handles to avoid cold start
  2. Request Batching: Batch multiple estimateEntry calls into a single solve when they share the same matrix
  3. Tool Index Pre-compilation: Pre-build the combined tool index (ruvector + solver) at startup
  4. Response Compression: Compress large matrix results for SSE transport

7. AI Agent Workflow Integration

7.1 Current Agent-MCP Architecture

Ruvector's agent system operates at multiple levels:

Claude Flow V3 Integration (via .claude/settings.json):

{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1",
    "CLAUDE_FLOW_V3_ENABLED": "true"
  },
  "claudeFlow": {
    "agentTeams": {
      "enabled": true,
      "coordination": {
        "sharedMemoryNamespace": "agent-teams"
      }
    },
    "swarm": {
      "topology": "hierarchical-mesh",
      "maxAgents": 15
    }
  }
}

Self-Learning Hooks (via hook system):

  • PreToolUse hooks validate Bash commands before execution
  • PostToolUse hooks learn from edit patterns
  • UserPromptSubmit hooks route requests via intelligence layer
  • SessionStart/SessionEnd hooks manage memory import/export

GRPO Training Pipeline (via ruvllm): The McpToolTrainer in /crates/ruvllm/src/training/mcp_tools.rs trains on tool-calling trajectories using Group Relative Policy Optimization, supporting 140+ tool definitions with category-aware reward shaping.

7.2 Sublinear Solver Agent Workflow Patterns

Pattern 1: Matrix Analysis Advisor

An agent monitors vector database operations and proactively suggests matrix optimizations:

1. Agent observes: vector_db_search(k=1000) returns slowly
2. Agent extracts: k-NN graph adjacency matrix from search results
3. Agent calls: sublinear.analyzeMatrix(adjacency, {checkDominance: true, computeGap: true})
4. Agent interprets: "Spectral gap is 0.02 -- graph is nearly disconnected, HNSW parameters need adjustment"
5. Agent recommends: Increase ef_construction or adjust M parameter

Pattern 2: Federated Consensus for Multi-Agent Decisions

When multiple agents disagree on an action:

1. Agent A proposes: "Refactor module X"
2. Agent B proposes: "Add tests to module X first"
3. Consensus coordinator:
   a. Builds proposal matrix from agent trust scores (ruvector.ruvector_swarm_coordinate)
   b. Computes PageRank on agent network (sublinear.pageRank)
   c. Solves consensus system (sublinear.solve)
   d. Validates through coherence gate (mcp_gate.permit_action)
4. Result: Weighted consensus with audit trail

Pattern 3: Adaptive Learning with Sublinear Updates

1. Hook intercepts: PostToolUse event (tool succeeded/failed)
2. Intelligence layer: Updates Q-learning state-action matrix
3. Instead of full Q-table update:
   a. sublinear.estimateEntry(Q_matrix, state_row, action_col)
   b. Compare estimated vs. actual reward
   c. Apply targeted update only to affected entries
4. Result: O(polylog(n)) learning update vs. O(n) full update

Pattern 4: Graph-Aware Code Navigation

1. Agent analyzes: Dependency graph of Rust crates (from Cargo.toml)
2. Agent builds: Module adjacency matrix
3. Agent calls: sublinear.pageRank(dependency_graph)
4. Agent calls: sublinear.analyzeMatrix(dependency_graph, {computeGap: true})
5. Agent identifies: Critical path modules (highest PageRank)
6. Agent uses: ruvector.ruvector_suggest_next_file() enhanced with PageRank data
7. Result: Edit order optimized by dependency criticality

7.3 Tool Routing with 3-Tier Model

The CLAUDE.md defines a 3-tier model routing system:

Tier Handler Use for Sublinear Integration
Tier 1 Agent Booster (WASM, <1ms) Simple matrix property lookups from cache
Tier 2 Haiku (~500ms) Basic solve calls with small matrices (<1000 dims)
Tier 3 Sonnet/Opus (2-5s) Complex composition pipelines, multi-step analysis

The routing hook can detect sublinear solver needs through keyword patterns:

if (prompt.includes("matrix") || prompt.includes("solve") || prompt.includes("pagerank")) {
  if (estimatedMatrixSize < 1000) return { tier: 2, model: "haiku" };
  return { tier: 3, model: "sonnet" };
}

7.4 Memory Bridge: Solver Results to Learning System

The sublinear solver produces results that feed back into ruvector's learning:

Solver Result -> ruvector.ruvector_remember({
  content: JSON.stringify(solverResult),
  memory_type: "pattern",
  metadata: {
    matrix_size: result.dimensions,
    solver_method: result.method,
    convergence_rate: result.iterations / result.maxIterations,
    spectral_gap: result.analysis?.spectralGap
  }
})

These remembered patterns enable future agents to:

  1. Skip solver calls when similar results exist (via ruvector_recall)
  2. Predict solver performance for new problems
  3. Select optimal solver methods based on historical data

7.5 Security Considerations

Integrating the sublinear solver introduces specific security surface:

  1. Input Validation: Matrix data must be validated at the MCP boundary. The edge-net server's pattern of checking for NaN/Infinity values should be applied to all matrix entries.

  2. Resource Exhaustion: A crafted matrix with extreme dimensions could exhaust memory. The solver should enforce:

    • Maximum matrix dimensions (configurable, default 100,000x100,000)
    • Maximum non-zero entries for sparse matrices
    • Timeout per solve operation

  3. Coherence Gate Integration: All solver-driven agent actions should pass through mcp-gate before execution:

sublinear.solve() -> proposed_action -> mcp_gate.permit_action() -> execute/defer/deny

  1. Audit Trail: The mcp-gate's witness receipt chain should be extended to cover solver invocations, creating a tamper-evident log of all mathematical computations that influenced agent decisions.

  2. Path Traversal: If using filesystem-based matrix sharing (mmap pattern), the npm MCP server's validateRvfPath() pattern must be applied to prevent directory traversal attacks on matrix file references.

Summary of Key Findings

Strengths of Current MCP Implementation

  1. Five distinct MCP servers covering native Rust, TypeScript, JavaScript, and WASM, demonstrating platform-agnostic MCP adoption
  2. Deep security integration through mcp-gate's coherence gate with cryptographic witness receipts
  3. Performance optimization via GNN caching (250-500x speedup) and the V3 MCP Optimization skill
  4. Self-learning infrastructure with GRPO-based training on 140+ tool trajectories
  5. Multiple transport layers (stdio, SSE, MessagePort) providing deployment flexibility

Integration Gaps to Address

  1. No dynamic MCP server discovery -- servers are statically registered
  2. No shared matrix buffer pool -- large payloads must be serialized per-request
  3. SDK version mismatch -- rvf-mcp-server uses ^1.0.0 vs solver's ^1.18.1
  4. No cross-server resource references -- no matrix:// or vector:// URI scheme for pass-by-reference
  5. Solver cold-start overhead -- npx per-invocation model conflicts with low-latency requirements

Recommended Next Steps

  1. Implement matrix buffer pool as an MCP resource with URI-based references
  2. Upgrade rvf-mcp-server SDK to ^1.18.1 for transport compatibility
  3. Add sublinear solver to daemon process pool alongside ruvector-cli for warm starts
  4. Extend mcp-gate audit chain to cover solver operations
  5. Build composite tools that chain ruvector search -> solver analysis -> learning storage
  6. Integrate solver metrics into the V3 MCP optimization monitoring pipeline
  7. Add solver awareness to GRPO training for tool selection optimization

Appendix: File Reference

File Path Role
/home/user/ruvector/crates/ruvector-cli/src/mcp_server.rs Main MCP server entry point
/home/user/ruvector/crates/ruvector-cli/src/mcp/handlers.rs Tool handlers (vector DB + GNN)
/home/user/ruvector/crates/ruvector-cli/src/mcp/protocol.rs JSON-RPC types and tool parameter structs
/home/user/ruvector/crates/ruvector-cli/src/mcp/transport.rs Stdio and SSE transport implementations
/home/user/ruvector/crates/mcp-gate/src/lib.rs Coherence gate MCP library
/home/user/ruvector/crates/mcp-gate/src/server.rs Gate server with initialize/tools/call handlers
/home/user/ruvector/crates/mcp-gate/src/tools.rs permit_action, get_receipt, replay_decision
/home/user/ruvector/crates/mcp-gate/src/types.rs API contract types (PermitAction, Witness, etc.)
/home/user/ruvector/crates/mcp-gate/src/main.rs Gate binary with env var configuration
/home/user/ruvector/npm/packages/rvf-mcp-server/src/server.ts RVF store MCP server (TypeScript)
/home/user/ruvector/npm/packages/rvf-mcp-server/src/transports.ts Stdio/SSE transport factories
/home/user/ruvector/npm/packages/ruvector/bin/mcp-server.js Intelligence layer MCP (40+ tools)
/home/user/ruvector/examples/edge-net/src/mcp/mod.rs WASM MCP server (17 tools, browser)
/home/user/ruvector/crates/ruvllm/src/training/mcp_tools.rs GRPO training for MCP tool calling
/home/user/ruvector/examples/edge-net/src/learning-scenarios/mcp_tools.rs Learning tool definitions
/home/user/ruvector/.claude/agents/sublinear/matrix-optimizer.md Matrix optimizer agent config
/home/user/ruvector/.claude/agents/sublinear/consensus-coordinator.md Consensus coordinator agent config
/home/user/ruvector/.claude/settings.json Claude Flow V3 settings, hooks, permissions
/home/user/ruvector/.claude/helpers/setup-mcp.sh MCP server registration script
/home/user/ruvector/.claude/skills/v3-mcp-optimization/SKILL.md V3 MCP performance optimization skill
/home/user/ruvector/.claude/commands/sparc/mcp.md SPARC MCP integration command