这是一个不用 MCP Python SDK 手写的最小 MCP demo。它包含完整链路：
nanomcp.server 作为 MCP server，通过 stdio 收发 JSON-RPC。
nanomcp.cli 作为 MCP client/host，启动 server，做 initialize、tools/list、tools/call。
chat 命令调用 OpenAI Chat Completions。模型返回 function/tool call 后，CLI 把它转成 MCP tools/call，再把工具结果发回模型生成最终回答。
一句话：function call 是“模型告诉你的应用它想调用什么函数”的模型 API 能力；MCP 是“你的应用如何用统一协议发现和调用外部工具/上下文服务”的连接协议。
更具体地说：
Function/tool calling 发生在 LLM API <-> 你的应用 之间。模型不会真的执行函数，它只返回类似 {"name":"get_weather","arguments":{...}} 的调用意图。
MCP 发生在 你的应用 <-> MCP server 之间。MCP server 暴露工具清单和执行入口，比如 tools/list 和 tools/call。
Host/client 是中间人。它先从 MCP server 拿工具 schema，把这些 schema 转成模型 API 的 tools；模型选择工具后，host/client 再调用 MCP server。
本项目里的链路是：
用户问题
-> nanomcp.cli
-> OpenAI Chat Completions tools=function schemas
<- 模型返回 tool_calls
-> nanomcp.cli 把 tool_call 映射为 MCP tools/call
-> nanomcp.server 执行 get_weather 或 find_files
<- MCP tool result
-> nanomcp.cli 把结果发回模型
<- 模型最终回答所以它们不是同一个层级：
Function call: 模型 API 的工具选择/参数生成机制
MCP: 应用连接工具服务器的标准协议Related MCP server: MCP Server Demo
nanomcp/
nanomcp/
cli.py # MCP client + model caller
server.py # hand-written MCP server over stdio
tests/
test_protocol.py
pyproject.toml
README.md在项目目录运行：
cd ~/Desktop/nanomcp
python3 -m nanomcp.cli list-tools直接调用天气工具：
python3 -m nanomcp.cli call get_weather '{"location":"Shanghai","unit":"celsius"}'直接调用当前日期时间工具：
python3 -m nanomcp.cli call get_current_datetime '{"timezone":"Asia/Shanghai"}'直接调用文件查找工具：
python3 -m nanomcp.cli call find_files '{"query":"*.pdf","max_results":5}'默认只搜索 ~/Desktop。可以临时扩大或缩小搜索根目录：
NANOMCP_FILE_ROOT=~/Desktop/nanomcp python3 -m nanomcp.cli call find_files '{"query":"*.py"}'需要 OpenAI API key。这里没有使用 OpenAI Python SDK，而是用标准库 urllib 直接发 HTTP。
推荐把本地配置写进 .env：
cd ~/Desktop/nanomcp
cp .envtemplate .env然后编辑 .env：
OPENAI_API_KEY=你的 key
OPENAI_BASE_URL=https://api.openai.com/v1
NANOMCP_MODEL=gpt-4.1-mini
NANOMCP_TIMEZONE=Asia/Shanghai.env 会被 CLI 自动读取，并且已经被 .gitignore 忽略。
cd ~/Desktop/nanomcp
python3 -m nanomcp.cli chat "上海今天天气怎么样？顺便帮我找桌面上的 PDF 文件"默认模型是 gpt-4.1-mini。你可以改：
NANOMCP_MODEL=gpt-5-mini python3 -m nanomcp.cli chat "找一下这个项目里的 py 文件"如果你使用 OpenAI-compatible gateway：
OPENAI_BASE_URL=http://localhost:8000/v1 python3 -m nanomcp.cli chat "上海天气怎么样？"轻量检查本地配置和 MCP server：
python3 -m nanomcp.cli doctor如果 chat 输出 OpenAI API quota is exhausted (429 insufficient_quota)，意思是模型 API 拒绝了请求：当前 OPENAI_API_KEY 所属项目没有可用额度或 billing 没开通。这不是 MCP server 失败，因为请求在模型返回 tool call 之前就被拒绝了。
排查顺序：
python3 -m nanomcp.cli doctor
echo "$OPENAI_API_KEY"
cat .env
python3 -m nanomcp.cli call get_weather '{"location":"Shanghai"}'
OPENAI_BASE_URL=http://localhost:8000/v1 python3 -m nanomcp.cli chat "上海天气怎么样？"第一个命令脱敏显示有效配置、shell 是否覆盖 .env、MCP server 是否能列出工具。
第二个命令确认 shell 里是否已经设置了 key。
第三个命令确认 .env 里的本地配置。
第四个命令验证本地 MCP 链路是否正常，不依赖模型 API。
第五个命令演示如何切到 OpenAI-compatible gateway。
如果仍然使用 OpenAI 官方 API，需要更换有额度的 key/project，或检查 billing 和模型权限。
默认天气是 deterministic demo data，方便无网络、无第三方 key 时学习协议链路。要尝试真实查询：
NANOMCP_LIVE_WEATHER=1 python3 -m nanomcp.cli call get_weather '{"location":"Shanghai"}'真实天气使用 https://wttr.in，失败时会自动回退到 demo data。
cd ~/Desktop/nanomcp
python3 -m unittest discover -s tests测试覆盖：
MCP initialize
MCP tools/list
MCP tools/call get_weather
MCP tools/call find_files
MCP tools/call get_current_datetime
看 nanomcp/cli.py 的 openai_tools_from_mcp()：它把 MCP tool schema 转成 OpenAI function tool schema。
看 run_chat()：它收到模型 tool_calls 后调用 mcp.call_tool()。这就是 MCP 和 function call 的衔接点。
看 nanomcp/server.py 的 main()：它只读 stdin、写 stdout，每一行都是 JSON-RPC。server 不知道 OpenAI，也不直接接触模型。
Find local files by name under the allowed root. The default root is ~/Desktop. Set NANOMCP_FILE_ROOT to change it.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Filename substring or glob pattern, such as *.pdf. | |
| root | No | Optional subdirectory under NANOMCP_FILE_ROOT. | |
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the root directory and default, but it does not disclose important behaviors such as case sensitivity, recursion depth, glob pattern handling, permissions, or the structure of returned results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences. The first sentence states purpose and scope, the second provides configuration info. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there is no output schema, the description should hint at return format or behavior. It does not mention what is returned (file paths, metadata), sorting, recursion, or error handling. The tool is simple but the agent may need more context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes two of three parameters (query and root). The description adds context about the root default and environment variable configuration, but does not enhance understanding of max_results or clarify glob pattern syntax beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Find local files by name under the allowed root.' It specifies the scope (local files) and the constraint (under a root). The siblings are unrelated (datetime and weather), so there is no ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or any prerequisites. Given that siblings are unrelated, implicit guidance is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the current date, time, and weekday. Use this for questions about today, current time, current date, or weekday.
| Name | Required | Description | Default |
|---|---|---|---|
| timezone | No | IANA timezone name, such as Asia/Shanghai or America/New_York. Defaults to NANOMCP_TIMEZONE or Asia/Shanghai. | Asia/Shanghai |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It adequately describes the output (date, time, weekday) and timezone parameter. However, it does not mention that the operation is read-only, instantaneous, or any potential dependencies, leaving some behavioral details implicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise and front-loaded with the core function. Every sentence serves a purpose without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema), the description fully covers what the tool does, its possible output, and appropriate use cases. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With full schema coverage (100%), the description adds no new parameter details beyond the schema. The schema already describes the timezone parameter well, so the description provides minimal added value, meeting the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves current date, time, and weekday. It explicitly lists use cases like 'today, current time, current date, or weekday', and siblings are unrelated, making purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description directly states when to use the tool ('for questions about today, current time, current date, or weekday'). It does not provide exclusions or alternatives, but given the simplicity and distinct siblings, this is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current weather for a city. By default this returns deterministic demo data. Set NANOMCP_LIVE_WEATHER=1 to try wttr.in.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | City or place name, for example Shanghai. | |
| unit | No | Temperature unit. | celsius |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the burden. It discloses the demo/live behavior and environment variable, but lacks details on return format, error handling, or external API dependencies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences efficiently define purpose and critical behavioral context. No superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple weather tool, the description covers core purpose and key behavioral nuance. However, it omits return value structure or typical properties, which would help the agent understand the output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers 100% of parameters with descriptions. The description adds no additional parameter meaning beyond what the schema provides, meeting baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get current weather for a city,' using a specific verb and resource, and distinguishes from siblings like find_files and get_current_datetime.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains the default demo mode and how to switch to live data, providing context for when to expect real or synthetic data. No explicit alternatives or exclusions but sufficient for this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0find_filesget_current_datetimeget_weatherScored across 3 tools
Each tool has a clear, distinct purpose: file search, datetime, and weather. No overlap or ambiguity.
All tool names follow the verb_noun snake_case pattern consistently: find_files, get_current_datetime, get_weather.
Three tools is small but appropriate for a 'nano' server intended as a minimal utility collection. Not too few given its scope.
The tools cover only three disparate areas with no clear domain. As a general utility set, common operations like calculations or text processing are missing, but it may be intentionally limited.
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