Which integrations are available for this server?

Routes agent steps to Google Gemini endpoints (gemini-2.5-flash-lite and gemini-2.5-flash) across the FAST_LOCAL and BALANCED tiers, including HTTP 429 fast-trip circuit breaking and failover to peer providers. Routes agent steps to locally hosted Ollama models (e.g., ollama/qwen2.5-coder-7b) as the cheapest FAST_LOCAL tier, with health tracking and latency EMA used for failover and Pareto utility scoring. Routes complex reasoning steps to OpenAI frontier reasoning endpoints (openai/o3-reasoning) as the final cascade tier when lower-cost models fail schema/confidence validation.

How do I use synapse-router?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@synapse-router route this agent step to the cheapest healthy model and show the receipt" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

synapse-router

by codesphere-u

Overview Schema Source Related Servers Discussions Admin

Python

Remote

synapse-router

Cost-Aware Multi-Model AI Agent Router, Confidence-Gated Cascade Escalation, 3-State Circuit Breaker & 64-Bit SimHash Semantic Cache Gateway.

🚀 Live Interactive Developer Portal & Playground: Visit https://synapse-router.vercel.app in your web browser to test routing live, inspect schemas, and copy 1-click configurations for Claude Code, Cursor, Windsurf, and Claude Desktop.

Why `synapse-router`? (The 2026 Multi-Agent Infrastructure Bottleneck)

Production multi-agent systems (LangGraph, CrewAI, Pydantic AI, and MCP tool loops) execute 10–50 LLM calls per user workflow. Sending every trivial intent classification, entity extraction, JSON formatting, or tool-routing step to a frontier reasoning model (o3-reasoning, claude-3-7-sonnet) wastes 75–90% of token budgets and violates sub-second latency SLAs—while relying on a single model provider causes total workflow failure during HTTP 429 rate-limit spikes or regional 5xx outages.

synapse-router is a zero-external-API-key, deterministic Multi-Model Agent Routing, Confidence Cascade, Circuit-Breaking & Semantic Caching Engine with a built-in JSON-RPC 2.0 Model Context Protocol (MCP) Server and reproducible CLI benchmark suite.

Related MCP server: llm-router-mcp

System Architecture

flowchart TD
    Req["AgentStepRequest<br/>(prompt, tools_schema, require_json_schema, latency_sla_ms, max_cost_usd)"] --> Budget["TokenBudgetGovernor<br/>Hard Cap (100%) & Soft-Cap Tier Downgrade (80%)"]
    Budget -->|Allowed| Cache["SemanticPromptCache<br/>64-Bit Char/Word Shingle SimHash (Hamming <= 3)<br/>+ Tool/Schema Context Fingerprint Isolation"]
    Cache -->|Cache Hit (0.0 USD)| Receipt["Signed RoutingDecisionReceipt<br/>(HMAC-SHA256 Audit Trail)"]
    Cache -->|Cache Miss| Classifier["TaskComplexityClassifier<br/>Reasoning Markers + Code/AST Density + Tool Depth + Token Volume"]
    Classifier --> TierSelect["Pareto Utility Endpoint Selector<br/>U(m) = Q_prior - w_c * Cost_norm - w_l * Latency_EMA"]
    Health["EndpointHealthRegistry<br/>3-State Circuit Breaker (CLOSED / OPEN / HALF_OPEN)<br/>HTTP 429 Fast-Trip & EMA Latency Tracker"] <--> TierSelect
    TierSelect --> Fast["Tier 1: FAST_LOCAL<br/>(ollama/qwen2.5-coder-7b, gemini-2.5-flash-lite)"]
    Fast --> Validator["ResponseValidator<br/>JSON Schema + Non-Empty Tool Args + Refusal Check + Confidence >= min_confidence"]
    Validator -->|Valid & Confident| CacheStore["Update SimHash Cache & Budget Ledger"]
    Validator -->|Low Confidence / Invalid JSON / HTTP 429| Balanced["Tier 2: BALANCED<br/>(gemini-2.5-flash, claude-3-5-haiku)"]
    Balanced --> Validator2["ResponseValidator"]
    Validator2 -->|Valid & Confident| CacheStore
    Validator2 -->|Escalated| Frontier["Tier 3: FRONTIER_REASONING<br/>(claude-3-7-sonnet, openai/o3-reasoning)"]
    Frontier --> CacheStore
    CacheStore --> Receipt

Key Capabilities

Sub-Millisecond Prompt & Agent Step Complexity Analyzer (classifier.py):
- Deterministic feature extraction across formal proof/architecture markers, code/AST syntax density, recursive JSON/tool schema depth, and log-scaled token volume.
- Maps steps into Tier.FAST_LOCAL, Tier.BALANCED, or Tier.FRONTIER_REASONING with automatic tight-SLA adjustments (latency_sla_ms < 280ms).
Pareto Utility Router & Confidence-Gated Cascade Engine (router.py):
- Evaluates registered ModelEndpoint profiles against hard capability constraints (supports_tools, supports_json_schema, context_window, latency_sla_ms, max_cost_usd) and ranks healthy candidates by Pareto utility: $$\text{Utility}(m) = Q_{\text{prior}}(m) - w_c \cdot \widehat{C}(m) - w_\ell \cdot \widehat{L}_{\text{EMA}}(m)$$
- Executes on the selected lower-cost tier first, validates output with ResponseValidator (strict JSON schema key/type validation, non-empty tool arguments, refusal/truncation detection, and confidence >= min_confidence), and escalates (FAST_LOCAL -> BALANCED -> FRONTIER_REASONING) only when validation or confidence fails.
- Emits an HMAC-SHA256 signed RoutingDecisionReceipt recording every hop, latency, and exact USD saved vs. frontier-only routing.
Rolling 3-State Provider Circuit Breaker (health.py):
- Tracks per-endpoint rolling error rates, HTTP 429 rate-limit counters, 5xx errors, timeouts, and exponential moving average (EMA) latency.
- Implements CLOSED -> OPEN -> HALF_OPEN state transitions with < 0.05 ms failover to healthy peer endpoints.
64-Bit Locality-Sensitive SimHash Semantic Cache (cache.py):
- Computes 64-bit SimHash fingerprints over weighted word unigrams, word bigrams, and 4-char shingles using blake2b.
- Matches near-identical agent sub-prompts within a configurable bitwise Hamming distance (<= 3 bits) while isolating cache entries by a canonical SHA-256 context_fingerprint of system_prompt, tools_schema, and require_json_schema.
Tenant & Workflow Token/USD Budget Governor (budget.py):
- Enforces per-workflow and per-tenant USD and token caps, automatically downgrading model tiers when utilization reaches 80% (soft_cap_downgrade) and blocking execution at 100%.
Built-In JSON-RPC 2.0 MCP Server (server.py):
- Exposes route_agent_step, classify_task_complexity, inspect_endpoint_health, and check_budget_status over standard MCP tools/list and tools/call.

Reproducible Benchmark Results (500 Multi-Agent Requests across 6 Endpoints)

Run the benchmark suite directly via CLI:

python -m synapse_router.cli benchmark --requests 500

Metric	Measured Value	Notes
Workload Size	`500` requests	Multi-step agent workflow across `6` model endpoints
Routing Engine P50 Latency	`742.56 µs` (`0.74 ms`)	End-to-end classification + SimHash lookup + Pareto scoring + receipt signing
Routing Engine P95 Latency	`1099.67 µs` (`1.10 ms`)	Includes multi-hop cascade escalation & failover paths
Frontier-Only Baseline Cost	`$3.7402 USD`	Baseline cost if all 500 steps routed to frontier reasoning tier
Synapse-Router Actual Cost	`$0.3520 USD`	Actual spend across tiered routing, cascades, and cache hits
Total USD Cost Reduction	`90.59% saved` (`$3.3881 USD` saved)	10.6x reduction in multi-agent token spend
Cascade Quality Retention	`100.00%`	All 500 completed steps met `confidence >= 0.78` and schema validation
Provider Failover Recovery Rate	`100.00%` (`13/13` HTTP 429s)	Zero dropped workflow steps during injected HTTP 429 rate-limit spikes
Semantic Cache Hit Rate	`22.20%`	64-bit SimHash near-duplicate detection (`Hamming distance <= 3`)
Final Tier Distribution	`57% FAST_LOCAL` / `23% BALANCED` / `20% FRONTIER`	`{"FAST_LOCAL": 285, "BALANCED": 115, "FRONTIER_REASONING": 100}`

20-Step Multi-Agent Workflow Simulation (`demo`)

Run the 20-step workflow simulation (featuring injected HTTP 429 rate-limit spikes on google/gemini-2.5-flash, malformed JSON cascade escalation, low-confidence cascade escalation, and SimHash cache hits):

python -m synapse_router.cli demo

==================================================================================================
SYNAPSE-ROUTER: 20-STEP MULTI-AGENT WORKFLOW DEMO (CASCADE + CIRCUIT BREAKER + SIMHASH CACHE)
==================================================================================================
STEP ID                    | SCORE | INIT TIER          | FINAL ENDPOINT               | HOPS |   COST ($)
--------------------------------------------------------------------------------------------------
step-01-intent             |  0.04 | FAST_LOCAL         | google/gemini-2.5-flash-lite |    1 |   0.000022
step-02-intent-near-dup    |  0.04 | FAST_LOCAL         | google/gemini-2.5-flash-lite [CACHE] |    0 |   0.000000
step-03-extract-entities   |  0.03 | FAST_LOCAL         | google/gemini-2.5-flash-lite |    1 |   0.000030
step-04-tool-lookup        |  0.45 | BALANCED           | google/gemini-2.5-flash      |    1 |   0.000232
step-05-cascade-json       |  0.05 | FAST_LOCAL         | google/gemini-2.5-flash      |    2 |   0.000121
step-06-rate-limit-429     |  0.43 | BALANCED           | anthropic/claude-3-5-haiku   |    2 |   0.001001
step-07-rate-limit-failover |  0.45 | BALANCED           | anthropic/claude-3-5-haiku   |    2 |   0.001001
step-08-frontier-arch      |  0.91 | FRONTIER_REASONING | anthropic/claude-3-7-sonnet  |    1 |   0.009240
step-09-tag-severity       |  0.02 | FAST_LOCAL         | google/gemini-2.5-flash-lite |    1 |   0.000011
step-10-tag-severity-cache |  0.02 | FAST_LOCAL         | google/gemini-2.5-flash-lite [CACHE] |    0 |   0.000000
step-11-code-audit         |  0.80 | FRONTIER_REASONING | anthropic/claude-3-7-sonnet  |    1 |   0.005367
step-12-summarize-slack    |  0.03 | FAST_LOCAL         | google/gemini-2.5-flash-lite |    1 |   0.000034
step-13-summarize-slack-dup |  0.03 | FAST_LOCAL         | google/gemini-2.5-flash-lite [CACHE] |    0 |   0.000000
step-14-sql-query-gen      |  0.49 | BALANCED           | anthropic/claude-3-5-haiku   |    1 |   0.000844
step-15-low-conf-cascade   |  0.03 | FAST_LOCAL         | anthropic/claude-3-5-haiku   |    2 |   0.000510
step-16-frontier-proof     |  0.68 | FRONTIER_REASONING | anthropic/claude-3-7-sonnet  |    1 |   0.008355
step-17-translate-note     |  0.03 | FAST_LOCAL         | google/gemini-2.5-flash-lite |    1 |   0.000028
step-18-extract-owners     |  0.04 | FAST_LOCAL         | google/gemini-2.5-flash-lite |    1 |   0.000020
step-19-postmortem-draft   |  0.70 | FRONTIER_REASONING | anthropic/claude-3-7-sonnet  |    1 |   0.004356
step-20-final-check        |  0.02 | FAST_LOCAL         | google/gemini-2.5-flash-lite |    1 |   0.000015
--------------------------------------------------------------------------------------------------
Frontier-Only Baseline Cost : $0.148660
Synapse-Router Actual Cost  : $0.031187
Total USD Cost Saved        : $0.117473 (79.02% reduction)
Semantic Cache Hits         : 3/20
Cascade / Failover Recoveries: 4 (100.0% workflow completion)
==================================================================================================

Quickstart

Installation

git clone https://github.com/codesphere-u/synapse-router.git
cd synapse-router
pip install -e ".[dev]"

Python SDK Usage

from synapse_router import AgentStepRequest, SynapseRouter

router = SynapseRouter()

# 1. Route a structured JSON extraction step (automatically selects Tier.FAST_LOCAL)
request = AgentStepRequest(
    prompt="Classify customer ticket intent into billing, outage, or security_audit.",
    require_json_schema={
        "type": "object",
        "required": ["intent", "priority"],
        "properties": {
            "intent": {"type": "string"},
            "priority": {"type": "string"},
        },
    },
    latency_sla_ms=300.0,
    min_confidence=0.80,
    tenant_id="acme-corp",
    workflow_id="ticket-triage-42",
    step_id="step-1",
)

receipt = router.execute_cascade(request)
print(f"Selected Endpoint : {receipt.selected_endpoint_id} ({receipt.final_tier})")
print(f"Actual Cost (USD) : ${receipt.total_cost_usd:.6f} (Saved ${receipt.cost_saved_usd:.6f})")
print(f"HMAC Receipt Sig  : {receipt.hmac_sha256_signature}")

MCP Server Integration (Claude Desktop / Cursor / LangGraph MCP)

Start the stdio JSON-RPC 2.0 MCP server:

python -m synapse_router.cli mcp

Or configure in mcp.json:

{
  "mcpServers": {
    "synapse-router": {
      "command": "python",
      "args": ["-m", "synapse_router.cli", "mcp"]
    }
  }
}

Exposed MCP Tools (`tools/list`) & Detailed Usage Guide

Tool Name	What is its use? (Purpose)	Key Arguments	Typical Output
`route_agent_step`	Route an AI agent workflow step across `FAST_LOCAL`, `BALANCED`, and `FRONTIER_REASONING` model tiers using Pareto cost/latency optimization, SimHash semantic caching, circuit-breaker failover, and confidence cascade escalation.	`prompt` (str), `system_prompt` (str), `latency_sla_ms` (float), `max_cost_usd` (float), `min_confidence` (float)	`{"selected_model": "...", "tier": "FAST_LOCAL", "cached": false, "estimated_cost_usd": 0.0001}`
`classify_task_complexity`	Compute deterministic task complexity score (`0.0–1.0`) and recommended model tier without executing model calls.	`prompt` (str), `system_prompt` (str), `tools_schema` (list)	`{"complexity_score": 0.35, "recommended_tier": "FAST_LOCAL", "feature_scores": {...}}`
`inspect_endpoint_health`	Inspect 3-state circuit breaker status (`CLOSED`, `OPEN`, `HALF_OPEN`), rolling error rates, HTTP 429 rate-limit counters, and EMA latency for model endpoints.	`endpoint_id` (optional str)	`{"endpoints": {"fast-llama": {"state": "CLOSED", "error_rate": 0.0, "p95_latency_ms": 42.1}}}`
`check_budget_status`	Query tenant and workflow token/USD budget utilization, remaining allowance, and 80% soft-cap tier downgrade state.	`tenant_id` (str), `workflow_id` (str)	`{"tenant_id": "...", "budget_usd": 100.0, "spent_usd": 14.20, "downgrade_active": false}`
`synapse_rate_limit_status`	Query sliding-window RPM and TPM consumption, ceiling limits, and saturation status for a model endpoint.	`endpoint_id` (str)	`{"endpoint_id": "...", "rpm_utilization": 0.12, "tpm_utilization": 0.08, "saturated": false}`
`synapse_export_aegis_g2`	Export canonical RFC 6962 G2_BUDGET_ROUTING attestation leaf for a routing receipt or simulation parameters.	`cost_usd` (float), `budget_cap_usd` (float), `model_id` (str), `task_prompt` (str)	`{"attestation_leaf": "...", "sha256": "...", "valid": true}`

Tool Usage Examples

1. `route_agent_step` — Intelligent Multi-Model Routing with Cost SLA

Route an agent's sub-task with strict latency and cost bounds:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "route_agent_step",
    "arguments": {
      "prompt": "Extract JSON fields from invoice: invoice_id, amount, due_date.",
      "latency_sla_ms": 250.0,
      "max_cost_usd": 0.002,
      "min_confidence": 0.85
    }
  }
}

2. `classify_task_complexity` — Pre-Execution Task Triage

Determine whether a task needs an expensive frontier model (Claude 3.5 Sonnet / o1) or can be safely delegated to an ultra-fast local model (Llama-3.1-8B):

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "classify_task_complexity",
    "arguments": {
      "prompt": "Design a formal TLA+ distributed consensus spec with proof of non-blocking atomicity.",
      "tools_schema": []
    }
  }
}

Exposed MCP Resources (`resources/list`, `resources/read`)

Resource URI	MIME Type	Purpose / Content Description
`synapse://endpoints/catalog`	`application/json`	Live catalog of configured model endpoints across tiers (`FAST_LOCAL`, `BALANCED`, `FRONTIER_REASONING`) with latency SLAs and token pricing.
`synapse://health/summary`	`application/json`	Real-time circuit breaker states, sliding-window rate limit counters, and budget spend utilization across all endpoints.

Exposed MCP Prompts (`prompts/list`, `prompts/get`)

Prompt Name	Purpose / Use Case	Required Arguments	Workflow Output
`route-task-advisor`	Guides an LLM agent to analyze task complexity, token requirements, and recommend the optimal model tier and fallback cascade.	`prompt` (string)	Pre-formatted advisory prompt instructing the model to estimate complexity, recommend tiers, and outline latency SLAs.
`budget-allocation-planner`	Guides an LLM agent to design budget limits with 80% soft-cap degradation rules for multi-tenant swarms.	`monthly_budget_usd` (string)	Pre-formatted allocation prompt breaking down spend tiers, cache targets, and rate limiting thresholds.

Architecture Decision Records (ADRs)

Testing

Run the full 93-test suite:

pytest -v

All 93 tests validate Pareto utility routing, SimHash semantic caching, confidence cascade escalation, 3-state circuit breakers, token/USD budget enforcement, rate limiters, RFC 6962 G2 attestation export, and Vercel serverless gateways.

Hosted endpoint (Vercel)

A free, public, stateless deployment of Synapse Router runs over MCP Streamable HTTP:


MCP endpoint	`https://synapse-router.vercel.app/mcp` (JSON-RPC 2.0 over `POST`)
Health probe	`https://synapse-router.vercel.app/health`

curl -s -X POST https://synapse-router.vercel.app/mcp -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Client Configuration

1. Claude Code CLI (Recommended)

# Add to current project:
claude mcp add --transport http synapse-router https://synapse-router.vercel.app/mcp

# Or add globally across all projects:
claude mcp add --scope user --transport http synapse-router https://synapse-router.vercel.app/mcp

2. Claude Desktop / Cursor / Windsurf (`mcp.json`)

{
  "mcpServers": {
    "synapse-router": {
      "type": "http",
      "url": "https://synapse-router.vercel.app/mcp"
    }
  }
}

3. Smithery Registry (1-Click Install)

npx -y @smithery/cli install @codesphere-u/synapse-router --client claude

4. Local Stdio via `uvx`

{
  "mcpServers": {
    "synapse-router": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/codesphere-u/synapse-router.git",
        "synapse-router",
        "mcp"
      ]
    }
  }
}

Hosted limitations: All 6 tools are available. Router/cache/budget state is in-memory and ephemeral per serverless instance.

The hosted gateway is api/index.py; it enforces a 1 MB body limit, strict JSON-RPC envelopes and the tool restrictions above. Deploy your own copy with npx vercel deploy --prod (entrypoint is configured in pyproject.toml under [tool.vercel]).

License

Available Tools

6 tools

check_budget_statusB

Query tenant and workflow token/USD budget utilization, remaining allowance, and 80% soft-cap tier downgrade state.

Inputs: tenant_id, workflow_id.

classify_task_complexityC

Compute deterministic complexity score [0.0, 1.0] and recommended model Tier (FAST_LOCAL, BALANCED, FRONTIER_REASONING) for an agent step.

Inputs: prompt (required), tools_schema, system_prompt, latency_sla_ms, +2 more.

inspect_endpoint_healthB

Inspect 3-state circuit breaker status (CLOSED, OPEN, HALF_OPEN), rolling error rates, HTTP 429 rate-limit counters, and EMA latency for registered model endpoints.

Inputs: endpoint_id.

route_agent_stepC

Route an AI agent workflow step across FAST_LOCAL, BALANCED, and FRONTIER_REASONING models using Pareto cost/latency optimization, SimHash semantic caching, circuit-breaker failover, and confidence-gated cascade escalation.

Inputs: prompt (required), step_id, tenant_id, workflow_id, +8 more.

synapse_export_aegis_g2C

Export canonical RFC 6962 G2_BUDGET_ROUTING attestation leaf for a routing receipt or simulation parameters.

Inputs: task_id, cost_usd, model_id, task_prompt, +2 more.

synapse_rate_limit_statusA

Query sliding-window RPM and TPM consumption, ceiling limits, and saturation status for a model endpoint.

Inputs: endpoint_id (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored 2026-11-12 18:16 across 6 tools

Disambiguation4/5

Each tool has a distinct object: step routing, complexity scoring, rate-limit status, budget status, endpoint health, and attestation export. The three status/query tools (rate_limit, budget, endpoint_health) sit close together and could be briefly confused, but their descriptions clearly separate RPM/TPM, token/USD budget, and breaker/error metrics.

Naming Consistency3/5

Most tools follow a verb_noun pattern (route_agent_step, classify_task_complexity, inspect_endpoint_health, check_budget_status), but two carry an unrelated 'synapse_' prefix (synapse_export_aegis_g2, synapse_rate_limit_status), and one name uses a cryptic domain acronym (AEGIS G2). The convention is readable but not uniform across the set.

Tool Count5/5

Six tools is well-scoped for a model-routing service: one router, one classifier, three observability queries, and one attestation export. Each tool maps to a distinct operational need with no filler.

Completeness3/5

The routing/observability loop is covered, but there are no tools to register or list model endpoints, reset an OPEN circuit breaker, or configure budgets/limits — only to read them. These gaps would force an agent to fall back to external configuration channels.

Maintenance

ActivityMaintained

ResponsivenessNo recent community issues

Resources

Related MCP Connectors

TokenOracle
com.guffeyholdings
Hosted MCP server for LLM cost estimation, model comparison, and budget-aware routing.
Finance Monitoring
2
https://mcp.guffeyholdings.com/TokenOracle
BrainstormRouter MCP Server
io.github.justinjilg
AI routing, memory, guardrails, and governance. Routes across Claude, GPT, Gemini.
RAG Systems Knowledge & Memory Agent Orchestration
2
https://api.brainstormrouter.com/v1/mcp/connect
ThinkNEO Control PlaneC
ai.thinkneo
Enterprise AI Control Plane: governance, guardrails, spend tracking, compliance & smart routing.
Monitoring Observability Security
23
https://mcp.thinkneo.ai/mcp
EuvraA
dev.euvra
Discover, compare, route, and execute machine-accessible capabilities for AI agents.
Agent Orchestration AI & Machine Learning Autonomous Agents
31
https://euvra.dev/mcp

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synapse-router

Why synapse-router? (The 2026 Multi-Agent Infrastructure Bottleneck)

System Architecture

Key Capabilities

Reproducible Benchmark Results (500 Multi-Agent Requests across 6 Endpoints)

20-Step Multi-Agent Workflow Simulation (demo)

Quickstart

Installation

Python SDK Usage

MCP Server Integration (Claude Desktop / Cursor / LangGraph MCP)

Exposed MCP Tools (tools/list) & Detailed Usage Guide

Tool Usage Examples

1. route_agent_step — Intelligent Multi-Model Routing with Cost SLA

2. classify_task_complexity — Pre-Execution Task Triage

Exposed MCP Resources (resources/list, resources/read)

Exposed MCP Prompts (prompts/list, prompts/get)

Architecture Decision Records (ADRs)

Testing

Hosted endpoint (Vercel)

Client Configuration

1. Claude Code CLI (Recommended)

2. Claude Desktop / Cursor / Windsurf (mcp.json)

3. Smithery Registry (1-Click Install)

4. Local Stdio via uvx

License

Available Tools

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

mcp-router

llm-router-mcpofficial

genpark-multi-model-costofficial

jev-model-router

Why `synapse-router`? (The 2026 Multi-Agent Infrastructure Bottleneck)

20-Step Multi-Agent Workflow Simulation (`demo`)

Exposed MCP Tools (`tools/list`) & Detailed Usage Guide

1. `route_agent_step` — Intelligent Multi-Model Routing with Cost SLA

2. `classify_task_complexity` — Pre-Execution Task Triage

Exposed MCP Resources (`resources/list`, `resources/read`)

Exposed MCP Prompts (`prompts/list`, `prompts/get`)

2. Claude Desktop / Cursor / Windsurf (`mcp.json`)

4. Local Stdio via `uvx`