feat(mcp): improve server connection speed by integrating FastMCP library

pull/819/head
DP37 3 months ago committed by ascender1729
parent cc56f433a8
commit 824dea060e

@ -0,0 +1,189 @@
Below is a **surgical “diffstyle” checklist** that shows exactly what you have to change (and what you can delete outright) to migrate your current `Agent`/`mcp_integration` pair from **JSONfunctioncalling → FastMCP**.
I kept it to the two files you pasted, so you can copypaste or cherrypick with your IDEs patch tool.
---
## 0. New dependency
```bash
pip install fastmcp # tiny async wrapper around mcp.ClientSession in “fast” mode
```
---
## 1. `swarms/tools/mcp_integration.py`
### 1.1 Imports
```diff
-from mcp import (
- ClientSession,
- StdioServerParameters,
- Tool as MCPTool,
- stdio_client,
-)
+# fastmcp gives us a dropin “FastClientSession” that sets the right SSE headers
+from fastmcp import FastClientSession as ClientSession
+from fastmcp.servers import fast_sse_client as sse_client # replaces std one
```
*(Keep the rest of the imports asis; they still compile.)*
### 1.2 Replace the SSE transport factory
FastMCP reuses the same SSE wire format but forces `FAST_MCP_MODE=1` headers and keeps the connection hot.
```diff
- def create_streams(self, ...) -> AbstractAsyncContextManager[...]:
- return sse_client(
- url=self.params["url"],
- headers=self.params.get("headers", None),
- timeout=self.params.get("timeout", 5),
- sse_read_timeout=self.params.get("sse_read_timeout", 60*5),
- )
+ def create_streams(self, ...) -> AbstractAsyncContextManager[...]:
+ return sse_client( # NOTE: imported from fastmcp.servers above
+ url=self.params["url"],
+ headers=self.params.get("headers", None),
+ timeout=self.params.get("timeout", 5),
+ sse_read_timeout=self.params.get("sse_read_timeout", 60 * 5),
+ )
```
### 1.3 Add **fast** helper for a single call (optional)
```python
async def call_tool_fast(server: MCPServerSse, payload: dict[str, Any]):
"""
Convenience wrapper that opens → calls → closes in one shot.
"""
await server.connect()
result = await server.call_tool(payload)
await server.cleanup()
return result.model_dump() if hasattr(result, "model_dump") else result
```
---
## 2. `swarms/structs/agent.py`
### 2.1 accept `mcp_servers` parameter (you commented it out)
```diff
- tools_list_dictionary: Optional[List[Dict[str, Any]]] = None,
- # mcp_servers: List[MCPServerSseParams] = [],
+ tools_list_dictionary: Optional[List[Dict[str, Any]]] = None,
+ mcp_servers: Optional[List[Dict[str, Any]]] = None, # NEW
```
and save it:
```diff
self.tools_list_dictionary = tools_list_dictionary
+# FastMCP
+self.mcp_servers = mcp_servers or []
```
### 2.2 Drop `parse_and_execute_json` branch and replace with FastMCP
Inside `_run()` where you currently have:
```python
if self.tools is not None or hasattr(self, 'mcp_servers'):
...
```
replace everything in that `if` block with:
```diff
-if self.tools is not None or hasattr(self, 'mcp_servers'):
- if self.tools:
- out = self.parse_and_execute_tools(response)
- if hasattr(self, 'mcp_servers') and self.mcp_servers:
- out = self.mcp_execution_flow(response)
-
- self.short_memory.add(role="Tool Executor", content=out)
- ...
+if self.mcp_servers: # ـ فقط FastMCP path
+ # Response from the model **will be** JSONRPC already. Convert str → dict
+ try:
+ fn_call = json.loads(response) if isinstance(response, str) else response
+ except Exception:
+ # Not a toolcall, skip.
+ fn_call = None
+
+ if fn_call and isinstance(fn_call, dict):
+ # roundrobin you can pick a smarter loadbalancer later
+ target = random.choice(self.mcp_servers)
+ out = mcp_flow(target, fn_call) # <- from mcp_integration.py
+
+ self.short_memory.add(role="Tool", content=out)
+ agent_print(f"{self.agent_name} tool result", out, loop_count, self.streaming_on)
+
+ # Let the model reflect on the tools answer
+ follow_up = self.llm.run(out)
+ self.short_memory.add(role=self.agent_name, content=follow_up)
```
### 2.3 Delete **parse_and_execute_tools** helper altogether
If nothing else in your codebase uses it, just remove the whole method to avoid dead weight.
### 2.4 Optional: preload tool schemas into the model (good prompt hygiene)
At the end of `init_handling()` add:
```python
# Advertise remote tools to the model (tool descriptions feed)
if self.mcp_servers:
try:
first = self.mcp_servers[0]
schema_txt = any_to_str(mcp_flow_get_tool_schema(first))
self.short_memory.add(role="system", content=f"REMOTE_TOOLS:\n{schema_txt}")
except Exception as e:
logger.warning(f"Could not fetch tool schema: {e}")
```
---
## 3. Quick smoke test
```python
from swarms.structs.agent import Agent
FLOWISE = {"url": "https://mcp.flowise.ai"} # no auth for public demo
bot = Agent(
agent_name="fastmcp-demo",
model_name="gpt-4o-mini",
streaming_on=True,
mcp_servers=[FLOWISE], # <- the only change you really need
)
print(
bot("Use `serp.search` to fetch todays ETH price and summarise in one sentence")
)
```
You should see:
1. LLM emits a `call_tool` JSON.
2. Agent relays it to Flowise server via FastMCP.
3. Response streams back; LLM reasons on it; final answer printed.
---
### What we just *removed*
* `parse_and_execute_json`
* `tool_choice`, `function_calling_format_type`, etc. (theyre harmless but unused)
* Manual “functioncalling” retries.
### What we just *added*
* `fastmcp` dependency + a **single** SSE connection that stays alive for the whole agent run.
---
Thats it!
Apply the diff, run the smoke test, and youre on FastMCP.
If you bump into a specific traceback, paste it and well debug the next inch. Happy hacking 🚀
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