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The log tells us two separate things are still wrong:
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| Symptom | Root cause | 1‑line fix |
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|---------|------------|------------|
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| `RuntimeWarning: coroutine 'batch_mcp_flow' was never awaited` | `batch_mcp_flow()` is **async**, but you call it like a plain function. | `out = await batch_mcp_flow(...)` *or* wrap the call in `asyncio.run(…)` if you want to stay sync. |
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| `[MCP‑error] Expecting value: line 1 column 1 (char 0)` | The Fast‑MCP mock server replied, but your client tried to `json.loads()` an **empty body** (usually means the HTTP endpoint or headers are wrong). | Point the URL at the server **root** (`http://0.0.0.0:8000`) and send plain JSON‑RPC. |
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Below is a minimal patch that removes both errors without touching the rest of your code‑base.
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---
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## 1 ️⃣ — make `mcp_execution_flow()` await the coroutine
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```python
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# ── swarms/structs/agent.py ─────────────────────────────────────────
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import asyncio # add this at the top if it’s not there
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# …
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def mcp_execution_flow(self, payload: dict):
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"""
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Forward the tool‑call dict to every MCP server in self.mcp_servers.
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Returns the first non‑error response as a string.
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"""
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try:
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# batch_mcp_flow is async ➜ run it synchronously
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result = asyncio.run(
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batch_mcp_flow(self.mcp_servers, payload)
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)
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return any_to_str(result)
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except Exception as err:
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logger.error(f"MCP flow failed: {err}")
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return f"[MCP-error] {err}"
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```
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*(If you prefer your whole agent to stay async, just make
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`mcp_execution_flow` itself `async def` and `await batch_mcp_flow` —
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then call it with `await` from the `_run` loop.)*
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---
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## 2 ️⃣ — use the correct Fast‑MCP endpoint
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In the client that spins up the **math agent**:
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```python
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math_server = MCPServerSseParams(
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url="http://0.0.0.0:8000", # ← root! no “/mcp”
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headers={"Content-Type": "application/json"},
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timeout=5.0,
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sse_read_timeout=30.0,
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)
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```
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⚠️ Fast‑MCP’s `run(transport="sse", port=8000)` already exposes both
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the SSE stream and the JSON‑RPC POST endpoint on that root URL.
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Adding `/mcp` makes the request hit a 404, so the body is empty — that’s
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exactly what the JSON decoder complained about.
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---
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## 3 ️⃣ — (optional) turn streaming off until everything works
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```python
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math_agent = Agent(
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# …
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streaming_on=False # ← easier to debug; turn back on later
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)
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```
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With streaming disabled, `LiteLLM` returns plain strings, so your
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`parse_llm_output()` method won’t be handed a
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`CustomStreamWrapper` object any more.
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---
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### Quick test matrix
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| Action | Expected result |
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|--------|-----------------|
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| `curl -X POST http://0.0.0.0:8000 -d '{"tool_name":"add","a":2,"b":3}' -H 'Content-Type: application/json'` | `{"result":5}` |
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| Run `mock_math_server.py` | “Starting Mock Math Server on port 8000…” |
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| Run `mcp_client.py`, type `add 2 and 3` | Agent replies something like “2 + 3 = 5”. No coroutine warning. |
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As soon as the round‑trip works once, you can:
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* re‑enable `streaming_on=True` and teach `parse_llm_output()` to turn a
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`CustomStreamWrapper` into text (`"".join(token.choices[0].delta.content for token in wrapper)`);
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* point the agent at your real MCP servers instead of the mock one.
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Happy hacking! |
