Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NicolaiLassen/orxhestra --skill mcp-integrationgit clone --depth 1 https://github.com/NicolaiLassen/orxhestraWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nicolailassen/orxhestra/mcp-integration)<a href="https://agentmods.dev/skills/nicolailassen/orxhestra/mcp-integration"><img src="https://agentmods.dev/badge/skills/nicolailassen/orxhestra/mcp-integration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nicolailassen/orxhestra/mcp-integration"><img src="https://agentmods.dev/badge/skills/nicolailassen/orxhestra/mcp-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.00333 |
| Opus 5 | $0.00012 | $0.00167 |
| Sonnet 5 | $0.00005 | $0.00067 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
mcp-integration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
MCP Integration
Connect to any MCP-compatible tool server.
pip install orxhestra[mcp]
Usage
from orxhestra.integrations.mcp import MCPClient, MCPToolAdapter
client = MCPClient("http://localhost:8001/mcp")
adapter = MCPToolAdapter(client)
mcp_tools = await adapter.load_tools()
agent = LlmAgent(
name="MCPAgent",
model=model,
tools=mcp_tools,
instructions="Use the available tools to answer questions.",
)
Testing with in-memory server
Pass a FastMCP server object directly (no HTTP needed):
from fastmcp import FastMCP
server = FastMCP("TestServer")
@server.tool
def add(a: int, b: int) -> int:
"""Add two numbers."""
return a + b
client = MCPClient(server) # in-memory, no network
adapter = MCPToolAdapter(client)
tools = await adapter.load_tools()
MCPToolAdapter.load_tools() fetches the tool list from the MCP server and wraps each as a LangChain BaseTool.
In YAML Composer
tools:
weather:
mcp:
url: "http://localhost:8001/mcp"
local:
mcp:
server: "myapp.mcp_server.server" # dotted import to FastMCP instance
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 63 lines · 23 tokens per session scan A 2d9aaa0a3536
mcp-integration is a skill published in the GitHub repository NicolaiLassen/orxhestra (21 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 333 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
workflow-ai-coding
Edit, validate, debug, publish, and inspect ReachAI Workflow drafts through the Workflow AI Coding REST API. Use when asked to create or modify a workflow graph, add/update/delete nodes or edges, validate GraphSpec, dry-run or debug-run a workflow, inspect trace/run output, check release readiness, publish a validated…
reachai-onboarding
Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff. Use when asked to connect a Spring Boot service to ReachAI, add reachai-capability-sdk or reachai-spring-boot2-starter, configure…
ak-add-integration
Add a messaging platform integration to an existing Agent Kernel project. This skill guides you through adding Slack, WhatsApp, Messenger, Instagram, Telegram, Teams, or Gmail integration by generating configuration, updating dependencies, and setting up webhook handlers. Designed for users extending their agents.
tool-design
Design and verify tools that AI agents can actually use — for any framework or language (MCP servers, LangChain/LangGraph, function-calling, raw JSON schema; TypeScript, Python, or otherwise). Use when writing a new tool for an agent, reviewing or fixing an existing tool definition, deciding how to split capabilities…
astra-vector-backend
Design Astra DB Data API and vector-search backends for retrieval, metadata filtering, and LangChain-compatible stores.
ampersend
Give an agent a way to pay for things on the internet. Use when the user wants the agent to be able to pay for things online, when an HTTP call returns 402 Payment Required, when calling an endpoint that charges per request, when the user names a capability they want without a specific URL in mind, or when the user is…