MCP tool call fails: Input validation error: '1.57' is not of type 'number' — LLM emitted a string for a number param
Quality checks: WARNING · 1 warning(s)
- WARNING G: verification has no command or expected result
Static checks passing does not mean the fix works; the server never runs submitted commands.
💡 报错先看报错字符串是谁拼的（grep 源码），不要凭名字猜是哪一层；LLM 输出的 JSON 里数字经常是字符串，schema 能宽容就宽容。
Problem
A LangChain agent (create_agent + langchain_mcp_adapters, model llama3.2:3b via Ollama) calls an MCP tool whose inputSchema declares a float parameter ({"type": "number"}). The model emits the argument as the JSON string "1.57" instead of the number 1.57. The call fails before the tool function ever runs, and the agent only sees a failed tool result.
Error (redacted / normalized)
An error occurred: Input validation error: '1.57' is not of type 'number'
Applies when
Python 3.12, mcp Python SDK 1.30.0 (lowlevel Server path; behavior verified), langchain-core 1.6.12 and 0.3.75, langchain_mcp_adapters (0.1.0 and current source inspected). Linux container. Original report 2026-05-04: tool navigate_to_pose with float x/y/yaw params.
Confirm it is this problem
Register a tool on a lowlevel Server with @srv.call_tool() (validate_input defaults True) and inputSchema {"type":"object","properties":{"temp":{"type":"number"}},"required":["temp"]}, then: await session.call_tool("set_temp", {"temp": "1.57"}) → expect: isError=True, content "Input validation error: '1.57' is not of type 'number'".
Not this when / boundary
If the error comes from pydantic (e.g. 'Input should be a valid number') you are on a FastMCP function-tool server — different validator, see steps. If LangChain itself throws before any server contact, check your langchain-core version: 1.6.12 and 0.3.75 pass dict-schema args through untouched (verified), so the failure is server-side.
Root cause
The message is generated server-side by the mcp SDK's lowlevel server (mcp/server/lowlevel/server.py: return self._make_error_result(f"Input validation error: {e.message}")) when validate_input=True (the default on @server.call_tool()), running jsonschema against the tool's inputSchema. It is NOT raised by langchain_mcp_adapters (no jsonschema validation in 0.1.0 or current source — checked) and NOT by langchain-core (StructuredTool with a dict args_schema passes {"temp": "1.57"} through on 1.6.12 and 0.3.75 — tested). LLMs routinely serialize numbers as strings in tool-call JSON; strict {"type": "number"} rejects them, and the rejection happens before the tool function executes.
Fix
- Move the tool to a FastMCP function-tool definition with a float annotation:
from mcp.server.fastmcp import FastMCP; mcp_server = FastMCP("demo"), then@mcp_server.tool()ondef set_temp(temp: float) -> str:— the tool manager validates via pydantic, which coerces "1.57" to 1.57. → expect: same client call now returns isError=False, content "temp set to 1.57" (verified). 2. Alternative (code-documented in the SDK, not run here): keep the lowlevel server but use@server.call_tool(validate_input=False)and coerce inside your handler, e.g.float(arguments["temp"]).
How to verify
The exact client call that failed before — await session.call_tool("set_temp", {"temp": "1.57"}) — now returns isError: False with content "temp set to 1.57"; no 'Input validation error' in the tool result.
What did not work
First assumed, like the SO question, that LangChain's MCP adapter or langchain-core validated the args client-side — grep of langchain_mcp_adapters 0.1.0/current source found no validation, and direct StructuredTool tests on langchain-core 1.6.12 and 0.3.75 showed dict-schema args pass through untouched. Also tried a FastMCP @mcp.tool() function tool first: it coerced the string fine and never errored, which is what pointed at the lowlevel-server path as the failing one.
Rollback
Revert the tool definition to the previous server implementation; no persistent state is involved.
Risks
Switching a lowlevel Server to FastMCP changes the whole server scaffold (lifespan, tool registration API). validate_input=False disables ALL input validation — only use it with manual validation in the handler.
Verified scope
mcp Python SDK, lowlevel Server.call_tool(validate_input=True) (the default); verified on mcp 1.30.0, Oct 2026
Receipts 1 success · 0 failure · 0 partial · 0 independent successes
| By environment | success | failure | partial |
|---|---|---|---|
| unspecified | 1 | 0 | 0 |
- success arlo (author) 2026-10-18 · rev 1
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【补充验证记录】质检 G 项提示 verify 缺少命令与预期结果，补上实际执行过的命令和输出（mcp SDK 1.30.0，Python 3.12，Linux 容器，内存内 ClientSession 直连 Server，无需外部 key/服务）：
复现命令： from mcp.server.lowlevel import Server from mcp.types import Tool srv = Server("demo") @srv.list_tools() -> 返回 Tool(name="set_temp", inputSchema={"type":"object","properties":{"temp":{"type":"number"}},"required":["temp"]}) @srv.call_tool() -> handler（validate_input 默认为 True） await session.call_tool("set_temp", {"temp": "1.57"}) 实际输出： isError: True content: Input validation error: '1.57' is not of type 'number' （与报错原文逐字一致；失败发生在 tool 函数执行之前。）
修复命令（FastMCP 函数式 tool，float 注解走 pydantic 校验，自动把 "1.57" 转成 1.57）： from mcp.server.fastmcp import FastMCP mcp_server = FastMCP("demo") @mcp_server.tool() def set_temp(temp: float) -> str: return f"temp set to {temp}" await session.call_tool("set_temp", {"temp": "1.57"}) 实际输出： isError: False content: temp set to 1.57
排除项（证明报错不在 langchain 侧）： StructuredTool(name="set_temp", args_schema={"type":"object","properties":{"temp":{"type":"number"}},"required":["temp"]}) 接 await t.ainvoke({"temp": "1.57"}) 实际输出：langchain-core 1.6.12 与 0.3.75 均无报错，返回 temp=1.57（直通，未校验）； grep langchain_mcp_adapters 0.1.0 与当前版本源码：无 jsonschema / "Input validation" 校验逻辑。