Provides integration with OpenAI's Chat Completions API, enabling AI models to generate function/tool calls that can be executed through the MCP server's tools for weather, file search, and datetime operations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@nanomcp what's the weather in Tokyo today?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This is a minimal MCP demo written from scratch without the MCP Python SDK. It contains the complete link:
nanomcp.server acts as the MCP server, sending and receiving JSON-RPC via stdio.
nanomcp.cli acts as the MCP client/host, starting the server and performing initialize, tools/list, and tools/call.
The chat command calls OpenAI Chat Completions. After the model returns a function/tool call, the CLI converts it into an MCP tools/call, and then sends the tool result back to the model to generate the final answer.
In a nutshell: function calling is the model API capability where "the model tells your application which function it wants to call"; MCP is the connection protocol for "how your application uses a unified protocol to discover and call external tools/context services."
More specifically:
Function/tool calling happens between LLM API <-> your application. The model does not actually execute the function; it only returns an intent to call, such as {"name":"get_weather","arguments":{...}}.
MCP happens between your application <-> MCP server. The MCP server exposes a tool list and execution entry point, such as tools/list and tools/call.
The host/client is the middleman. It first gets the tool schema from the MCP server and converts these schemas into model API tools; after the model selects a tool, the host/client then calls the MCP server.
The link in this project is:
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So they are not at the same level:
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.mdRun in the project directory:
cd ~/Desktop/nanomcp
python3 -m nanomcp.cli list-toolsCall the weather tool directly:
python3 -m nanomcp.cli call get_weather '{"location":"Shanghai","unit":"celsius"}'Call the current date and time tool directly:
python3 -m nanomcp.cli call get_current_datetime '{"timezone":"Asia/Shanghai"}'Call the file search tool directly:
python3 -m nanomcp.cli call find_files '{"query":"*.pdf","max_results":5}'By default, it only searches ~/Desktop. You can temporarily expand or shrink the search root directory:
NANOMCP_FILE_ROOT=~/Desktop/nanomcp python3 -m nanomcp.cli call find_files '{"query":"*.py"}'An OpenAI API key is required. This project does not use the OpenAI Python SDK, but uses the standard library urllib to send HTTP requests directly.
It is recommended to write local configuration into .env:
cd ~/Desktop/nanomcp
cp .envtemplate .envThen edit .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 will be automatically read by the CLI and has already been ignored by .gitignore.
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"The default model is gpt-4.1-mini. You can change it:
NANOMCP_MODEL=gpt-5-mini python3 -m nanomcp.cli chat "找一下这个项目里的 py 文件"If you use an OpenAI-compatible gateway:
OPENAI_BASE_URL=http://localhost:8000/v1 python3 -m nanomcp.cli chat "上海天气怎么样？"Lightweight check of local configuration and MCP server:
python3 -m nanomcp.cli doctorIf chat outputs OpenAI API quota is exhausted (429 insufficient_quota), it means the model API rejected the request: the project associated with the current OPENAI_API_KEY has no available quota or billing is not enabled. This is not an MCP server failure, as the request was rejected before the model returned a tool call.
Troubleshooting order:
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？"The first command displays valid configuration with sensitive info masked, whether the shell overrides .env, and whether the MCP server can list tools.
The second command confirms whether the key is already set in the shell.
The third command confirms the local configuration in .env.
The fourth command verifies whether the local MCP link is normal, without relying on the model API.
The fifth command demonstrates how to switch to an OpenAI-compatible gateway.
If you still use the official OpenAI API, you need to replace it with a key/project that has quota, or check billing and model permissions.
The default weather is deterministic demo data, which is convenient for learning the protocol link without a network or third-party keys. To try a real query:
NANOMCP_LIVE_WEATHER=1 python3 -m nanomcp.cli call get_weather '{"location":"Shanghai"}'Real weather uses https://wttr.in, and will automatically fall back to demo data if it fails.
cd ~/Desktop/nanomcp
python3 -m unittest discover -s testsTest coverage:
MCP initialize
MCP tools/list
MCP tools/call get_weather
MCP tools/call find_files
MCP tools/call get_current_datetime
See openai_tools_from_mcp() in nanomcp/cli.py: it converts the MCP tool schema into the OpenAI function tool schema.
See run_chat(): it calls mcp.call_tool() after receiving tool_calls from the model. This is the connection point between MCP and function calling.
See main() in nanomcp/server.py: it only reads from stdin and writes to stdout, with each line being JSON-RPC. The server does not know about OpenAI, nor does it directly touch the model.
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. Dates show when Glama detected each change.
v0.1.0find_filesget_current_datetimeget_weatherEach 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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