Bristlecone Guard

Deterministic runtime guardrails and M2M (machine-to-machine) safety primitives for autonomous AI agent pipelines.

Exposes high-speed validation, AST evaluation, schema enforcement, and web/DNS auditing tools via standard HTTP and the Anthropic Model Context Protocol (MCP).

Tools

• audit_dns: Forward DNS resolution and network routing verification. Guards against Server-Side Request Forgery (SSRF).
• chunk_text: Sliding-window text segmentation with configurable overlap for RAG ingestion.
• eval_expression: Deterministic mathematical and boolean expression evaluation inside an isolated AST sandbox.
• extract_web: Sanitized server-side text extraction from public web pages.
• repair_json: Syntax repair for broken, malformed, or unclosed JSON strings produced by LLMs.
• validate_schema: Strict key-level schema validation for agent input/output payloads.

Public Endpoints

• Base URL: https://bristleconelogic.com
• MCP Transport (SSE): https://bristleconelogic.com/mcp
• Agentic Discovery Catalog: https://bristleconelogic.com/.well-known/ai-catalog.json
• Resource Manifest: https://bristleconelogic.com/.well-known/ai-resources.json
• API Documentation: https://bristleconelogic.com/docs

Connecting to Claude Desktop / MCP Clients

Add the following to your claude_desktop_config.json:

{ "mcpServers": { "bristlecone-guard": { "url": "https://bristleconelogic.com/mcp" } } }

For authenticated or metered tenant access:

{ "mcpServers": { "bristlecone-guard": { "url": "https://bristleconelogic.com/mcp", "headers": { "Authorization": "Bearer bl_live_YOUR_API_KEY" } } } }

LangChain & LangGraph

Install langchain-mcp-adapters to connect directly over HTTP:

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async def run():
    async with MultiServerMCPClient({
        "bristlecone": {
            "url": "https://bristleconelogic.com/mcp",
            "transport": "http"
        }
    }) as client:
        agent = create_react_agent(ChatOpenAI(model="gpt-4o"), client.get_tools())
        res = await agent.ainvoke({"messages": [("user", "Validate payload and inspect for SSRF")]})
        print(res["messages"][-1].content)

if __name__ == "__main__":
    asyncio.run(run())

LlamaIndex

Install llama-index-tools-mcp to load remote tools dynamically:

import asyncio
from llama_index.tools.mcp import aget_tools_from_mcp_url
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI

async def run():
    tools = await aget_tools_from_mcp_url("https://bristleconelogic.com/mcp")
    agent = ReActAgent.from_tools(tools, llm=OpenAI(model="gpt-4o"), verbose=True)
    res = agent.chat("Check destination URL security: https://example.com")
    print(res)

if __name__ == "__main__":
    asyncio.run(run())

CrewAI

Install crewai-tools and pass the MCP endpoint into your agents:

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerTool

bristlecone = MCPServerTool(url="https://bristleconelogic.com/mcp")

auditor = Agent(
    role="Runtime Guardrail Specialist",
    goal="SSRF defense and deterministic AST JSON repair",
    backstory="Deterministic validation layer protecting autonomous agents from unsafe execution.",
    tools=[bristlecone]
)

task = Task(description="Verify internal CIDR restrictions.", agent=auditor, expected_output="Audit status")
Crew(agents=[auditor], tasks=[task]).kickoff()

Autonomous M2M Settlement

• Protocol: x402 (HTTP 402 Payment Required)
• Network: Base L2 (eip155:8453)
• Asset: USDC
• Payee Contract / Treasury: 0xa17c8c3005698bc4ea6406a00387445e1d30c35f

License

Apache-2.0