sprout-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@sprout-mcprecommend model for biography synthesis"
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.
Sprout MCP
Drop your Opus bill 80%. Model-tiered content pipeline for MCP — cheap models seed work, expensive models verify it.
Sprout routes tasks to the right model tier automatically. Haiku drafts, Sonnet fact-checks, Opus verifies. Every chunk tracks provenance, confidence, and cost.
Install
uvx sprout-mcpOr add to Claude Code's MCP config (~/.claude/settings.json):
{
"mcpServers": {
"sprout": {
"command": "uvx",
"args": ["sprout-mcp"]
}
}
}Related MCP server: ypollak2/llm-router
How It Works
Haiku (seed) → Sonnet (watered) → Opus (sprouted)
Draft Fact-check Verify
$0.005/M $0.015/M $0.075/MSeed — Haiku drafts content cheaply (summarization, extraction, first passes)
Water — Sonnet reviews and fact-checks the seeds
Sprout — Opus deep-verifies only what passed Sonnet's review
Instead of running everything through Opus at $75/M output tokens, most work stays at Haiku's $5/M. Only the final verification — typically 10-20% of total work — touches Opus.
Tools (13)
Tool | Description |
| Store content with provenance (model, task type, sources) |
| List chunks needing review, filtered by confidence/project |
| Promote (seed→watered→sprouted) or reject chunks |
| Get model recommendation for a task type |
| Dashboard of chunk counts, confidence levels, token usage |
| Export verified chunks as JSON |
| Generate structured review summary for batch verification |
| Schedule tasks to run at a specific time or delay |
| View pending scheduled tasks |
| Cancel a pending scheduled task |
| Add/update routing rules at runtime |
| Estimated spend per model with real pricing |
| Track failed attempts with backoff guidance |
Configuration
Environment Variables
Variable | Default | Description |
|
| SQLite database location |
| (none) | Path to JSON config file for custom routes and pricing |
|
| Max retry attempts before giving up |
|
| Exponential backoff base (seconds) |
Custom Config File
Create a JSON file and point SPROUT_CONFIG at it:
{
"routes": {
"code_review": { "tier": "sonnet", "reason": "Code analysis needs reasoning" },
"translation": { "tier": "haiku", "reason": "Straightforward language task" }
},
"pricing": {
"custom-model": 10.00
}
}Default Routing Table
Task Type | Tier | Why |
| haiku | Factual summarization |
| haiku | Historical summarization |
| haiku | Content summarization |
| haiku | Structural verification |
| haiku | General summarization |
| haiku | Structured extraction |
| sonnet | Cross-reference claims |
| sonnet | Code analysis |
| opus | Deep factual verification |
| opus | Domain expertise required |
| opus | Deep reasoning required |
Unknown task types default to haiku — start cheap, escalate if needed.
Example Workflow
You: Use recommend_model for "biography_synthesis"
Sprout: biography_synthesis → haiku-4.5 (Factual summarization)
You: Use submit_chunk to store the Haiku output
Sprout: Stored chunk abc12345 [seed] for person-001.biography
You: Use get_review_queue to see what needs fact-checking
Sprout: 1 chunk pending review
You: Use mark_reviewed to promote after Sonnet fact-checks it
Sprout: Chunk abc12345 → watered (verified by sonnet-4.6)
You: Use get_cost_report
Sprout: haiku-4.5: ~1,300 tokens (1 chunk) — $0.0065
Total: $0.0065Development
git clone https://github.com/mepsopti/sprout-mcp.git
cd sprout-mcp
uv sync --extra dev
uv run pytestSupport
If Sprout saves you money on your AI bill, consider buying me a coffee:
License
MIT
mcp-name: io.github.mepsopti/sprout-mcp
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- AlicenseNot gradedqualityNot gradedmaintenanceIntelligent multi-model orchestrator with dynamic routing that optimizes AI costs by prioritizing free models and escalating to paid tiers only when needed, with stateless architecture using Redis and PostgreSQL.34
- AlicenseAqualityAmaintenanceRoutes your AI tasks to the best available model across 20+ providers — automatically selecting based on task type, budget, and subscription pressure. Supports text, image, video, and audio with built-in cost optimization and fallback chains.6071MIT
- AlicenseNot gradedqualityBmaintenanceEnables step-level routing of AI workflows by decomposing tasks, selecting the best model per step within constraints, executing steps, and providing full execution traces.1Apache 2.0
- FlicenseNot gradedqualityDmaintenanceAutomatically routes queries to the most suitable AI model based on task type, cost constraints, and performance needs, supporting multiple providers and customizable priorities.
Related MCP Connectors
Enterprise AI Control Plane: governance, guardrails, spend tracking, compliance & smart routing.
Runtime AI governance: decision gates, human approval, hash-chained audit, compliance mapping.
Execution graph of AWS: verified contracts, least-privilege IAM policies, pre-flight authorization.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/mepsopti/sprout-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server