Model Context Protocol (MCP) Server
Cliente MCP que utiliza LangChain/Python 
Este sencillo cliente de Protocolo de contexto de modelo (MCP) demuestra el uso de las herramientas del servidor MCP por parte de LangChain ReAct Agent.
Aprovecha una función de utilidad convert_mcp_to_langchain_tools() de langchain_mcp_tools .
Esta función maneja la inicialización paralela de múltiples servidores MCP especificados y convierte sus herramientas disponibles en una lista de herramientas compatibles con LangChain ( List[BaseTool] ).
Actualmente se admiten LLM de Anthropic, OpenAI y Groq.
Una versión mecanografiada de este cliente MCP está disponible aquí
Prerrequisitos
Python 3.11+
[opcional]
uv(uvx) instalado para ejecutar servidores MCP basados en paquetes Python[opcional] npm 7+ (
npx) para ejecutar servidores MCP basados en paquetes Node.jsClaves API de Anthropic , OpenAI y/o Groq según sea necesario
Related MCP server: Just Prompt
Configuración
Instalar dependencias:
make installConfigurar claves API:
cp .env.template .envActualice
.envsegún sea necesario..gitignoreestá configurado para ignorar.envpara evitar confirmaciones accidentales de las credenciales.
Configure los ajustes de los servidores LLM y MCP
llm_mcp_config.json5según sea necesario.El formato del archivo de configuración de los servidores MCP sigue la misma estructura que Claude for Desktop , con una diferencia: el nombre de clave
mcpServersse ha cambiado amcp_serverspara seguir la convención snake_case comúnmente utilizada en los archivos de configuración JSON.El formato del archivo es JSON5 , donde se permiten comentarios y comas finales.
El formato se amplía aún más para reemplazar las notaciones
${...}con los valores de las variables de entorno correspondientes.Mantenga todas las credenciales y la información privada en el archivo
.envy haga referencia a ellas con la notación${...}según sea necesario.
Uso
Ejecute la aplicación:
make startLa primera vez tarda un poco.
Ejecutar en modo detallado:
make start-vVer opciones de la línea de comandos:
make start-hCuando se le solicite, puede simplemente presionar Entrar para usar consultas de ejemplo que realizan invocaciones de herramientas de servidor MCP.
Las consultas de ejemplo se pueden configurar en llm_mcp_config.json5
Available Tools
2 toolsget-alertsC
Get weather alerts for a US state
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter US state code (e.g. CA, NY) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry full behavioral transparency. It only states the basic action without disclosing traits like read-only nature, authentication requirements, rate limits, or what type of alerts are returned. This is insufficient for an agent to understand the tool's behavior.
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 a single sentence, achieving conciseness. It is appropriately front-loaded with the verb and resource. However, it lacks structure such as prerequisites or return format, but it remains efficient for its length.
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 simplicity (one required parameter, no output schema, no annotations), the description should provide more context, such as what the alerts contain or that it is a read operation. The minimal description leaves gaps for an agent to understand the tool's full context.
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 has 100% description coverage for the 'state' parameter, so the baseline is 3. The description does not add any additional meaning beyond the schema; it simply restates the parameter's role without further context.
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 action ('Get') and resource ('weather alerts') with a specific scope ('for a US state'). It distinguishes itself from the sibling 'get-forecast' by focusing on alerts, not forecasts, though it does not explicitly mention the sibling.
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?
No guidance is provided on when to use this tool versus alternatives like 'get-forecast'. The description does not include any prerequisites, context, or exclusions, leaving the agent to infer usage from tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-forecastB
Get weather forecast for a location in the US
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude of the location | |
| longitude | Yes | Longitude of the location |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as data source, update frequency, or limitations of the forecast. The description only states the basic purpose.
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 a single concise sentence that immediately communicates the tool's purpose. No unnecessary words.
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 tool with two parameters and no output schema, the description is minimal but covers the core purpose. However, it lacks usage guidance and behavioral details that would help an AI agent use it correctly.
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?
Schema coverage is 100% with both parameters described. The description adds no additional meaning beyond the schema; the mention of 'in the US' is a location constraint but not parameter-specific. Baseline of 3 is appropriate.
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 action 'Get', the resource 'weather forecast', and the scope 'for a location in the US'. It distinguishes from the sibling tool 'get-alerts' which likely deals with alerts.
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?
No guidance on when to use this tool versus alternatives. The description does not indicate when to prefer this over 'get-alerts' or any other context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.3.2- First observed
get-alerts - First observed
get-forecast
TDQS
Scored across 2 tools
The tools get-alerts and get-forecast have clearly distinct purposes: one provides weather alerts, the other gives forecasts. No overlap.
Both tool names follow the consistent pattern 'get-<resource>', using lowercase and hyphens, which is predictable.
With only 2 tools, the server feels too sparse for a weather domain. Typically, more operations like current conditions or radar would be expected.
The tool set only covers alerts and forecasts, missing common weather operations like current conditions, location search, or severe weather warnings, resulting in significant gaps.
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