mcp-flowise es un paquete de Python que implementa un servidor de Protocolo de Contexto de Modelo (MCP) que se integra con la API de Flowise. Proporciona una forma estandarizada y flexible de listar flujos de chat, crear predicciones y registrar dinámicamente herramientas para flujos de chat o asistentes de Flowise.
Admite dos modos de funcionamiento:
Modo de bajo nivel (predeterminado) : registra dinámicamente herramientas para todos los flujos de chat recuperados de la API de Flowise.
Modo FastMCP : proporciona herramientas estáticas para enumerar flujos de chat y crear predicciones, adecuadas para configuraciones más simples.
Exposición de herramientas dinámicas : el modo de nivel bajo crea herramientas dinámicamente para cada flujo de chat o asistente.
Configuración más simple : el modo FastMCP expone las herramientas list_chatflows y create_prediction para una configuración mínima.
Filtrado flexible : ambos modos admiten el filtrado de flujos de chat a través de listas blancas y listas negras por ID o nombres (expresiones regulares).
Integración con MCP : se integra perfectamente en los flujos de trabajo de MCP.
Related MCP server: MCP Python Server
Para instalar mcp-flowise para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @andydukes/mcp-flowise --client claudePython 3.12 o superior
gestor de paquetes uvx
uvxConfirme que puede ejecutar el servidor directamente desde el repositorio de GitHub usando uvx :
uvx --from git+https://github.com/andydukes/mcp-flowise mcp-flowisemcpServers )Puede integrar mcp-flowise en su ecosistema MCP añadiéndolo a la configuración mcpServers . Ejemplo:
{
"mcpServers": {
"mcp-flowise": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/andydukes/mcp-flowise",
"mcp-flowise"
],
"env": {
"FLOWISE_API_KEY": "${FLOWISE_API_KEY}",
"FLOWISE_API_ENDPOINT": "${FLOWISE_API_ENDPOINT}"
}
}
}
}Se habilita al configurar FLOWISE_SIMPLE_MODE=true . Este modo:
Expone dos herramientas: list_chatflows y create_prediction .
Permite la configuración estática utilizando FLOWISE_CHATFLOW_ID o FLOWISE_ASSISTANT_ID .
Enumera todos los flujos de chat disponibles a través de list_chatflows .
Características :
Registra dinámicamente todos los flujos de chat como herramientas independientes.
Las herramientas reciben el nombre de los flujos de chat (normalizados).
Utiliza descripciones de la variable FLOWISE_CHATFLOW_DESCRIPTIONS y recurre a los nombres de flujo de chat si no se proporciona ninguna descripción.
Ejemplo :
my_tool(question: str) -> str creado dinámicamente para un flujo de chat.
uvxSi usa uvx en Windows y tiene problemas con --from git+https , la solución recomendada es clonar el repositorio localmente y configurar mcpServers con la ruta completa a uvx.exe y al repositorio clonado. Además, incluya APPDATA , LOGLEVEL y otras variables de entorno según sea necesario.
mcpServers en Windows){
"mcpServers": {
"flowise": {
"command": "C:\\Users\\matth\\.local\\bin\\uvx.exe",
"args": [
"--from",
"C:\\Users\\matth\\downloads\\mcp-flowise",
"mcp-flowise"
],
"env": {
"LOGLEVEL": "ERROR",
"APPDATA": "C:\\Users\\matth\\AppData\\Roaming",
"FLOWISE_API_KEY": "your-api-key-goes-here",
"FLOWISE_API_ENDPOINT": "http://localhost:3010/"
}
}
}
}Rutas completas : utilice rutas completas tanto para uvx.exe como para el repositorio clonado.
Variables de entorno : apunte APPDATA a su perfil de usuario de Windows (por ejemplo, C:\\Users\\<username>\\AppData\\Roaming ) si es necesario.
Nivel de registro : ajuste LOGLEVEL según sea necesario ( ERROR , INFO , DEBUG , etc.).
FLOWISE_API_KEY : Su token portador de API de Flowise ( obligatorio ).
FLOWISE_API_ENDPOINT : URL base para Flowise (predeterminado: http://localhost:3010 ).
FLOWISE_CHATFLOW_DESCRIPTIONS : Lista de pares de chatflow_id:description separados por comas. Ejemplo:
FLOWISE_CHATFLOW_DESCRIPTIONS="abc123:Chatflow One,xyz789:Chatflow Two"FLOWISE_SIMPLE_MODE=true )FLOWISE_CHATFLOW_ID : ID de flujo de chat único (opcional).
FLOWISE_ASSISTANT_ID : ID de asistente único (opcional).
FLOWISE_CHATFLOW_DESCRIPTION : Descripción opcional para la herramienta única expuesta.
Los filtros se pueden aplicar en ambos modos utilizando las siguientes variables de entorno:
Lista blanca por ID :FLOWISE_WHITELIST_ID="id1,id2,id3"
Lista negra por ID :FLOWISE_BLACKLIST_ID="id4,id5"
Lista blanca por nombre (expresión regular) :FLOWISE_WHITELIST_NAME_REGEX=".*important.*"
Lista negra por nombre (expresión regular) :FLOWISE_BLACKLIST_NAME_REGEX=".*deprecated.*"
Nota : Las listas blancas prevalecen sobre las negras. Si se configuran ambas, se aplica la regla más restrictiva.
Proteja su clave API : asegúrese de que FLOWISE_API_KEY se mantenga segura y no esté expuesta en registros o repositorios.
Configuración del entorno : utilice archivos .env o variables de entorno para configuraciones sensibles.
Añade .env a tu .gitignore :
# .gitignore
.envClave API faltante : asegúrese de que FLOWISE_API_KEY esté configurada correctamente.
Configuración no válida : si se configuran FLOWISE_CHATFLOW_ID y FLOWISE_ASSISTANT_ID , el servidor se negará a iniciarse.
Errores de conexión : Verifique que FLOWISE_API_ENDPOINT sea accesible.
Este proyecto está licenciado bajo la Licencia MIT. Consulte el archivo de LICENCIA para más detalles.
[x] Modo Fastmcp
[x] Modo de bajo nivel
[x] Filtrado
[x] Integración de escritorio de Claude
[ ] Asistentes
Create a prediction by sending a question to a specific chatflow or assistant.
Args:
chatflow_id (str, optional): The ID of the chatflow to use. Defaults to FLOWISE_CHATFLOW_ID.
question (str): The question or prompt to send to the chatflow.
Returns:
str: The raw JSON response from Flowise API or an error message if something goes wrong.
| Name | Required | Description | Default |
|---|---|---|---|
| chatflow_id | No | ||
| question | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that this is a creation/mutation tool ('Create a prediction') and mentions the API source ('Flowise API'), but lacks details about authentication needs, rate limits, error handling beyond 'error message', or whether predictions are stored persistently.
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 appropriately sized with clear sections (purpose, args, returns). The first sentence states the core purpose, and subsequent details are necessary. Minor improvement could be merging the first two sentences for better flow.
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 2 parameters with 0% schema coverage and no output schema, the description provides basic parameter semantics and return type ('raw JSON response' or 'error message'), but lacks details on response structure, error cases, or integration context (e.g., what a 'prediction' entails in this system).
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 description coverage is 0%, so the description must compensate. It adds meaningful context for both parameters: chatflow_id is optional with a default value from environment, and question is the prompt to send. However, it doesn't explain format constraints or provide examples.
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 purpose: 'Create a prediction by sending a question to a specific chatflow or assistant.' It specifies the verb ('Create a prediction') and resource ('chatflow or assistant'), but doesn't explicitly differentiate from the sibling tool 'list_chatflows' beyond their different functions.
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 implies usage context by mentioning 'chatflow or assistant' and referencing 'FLOWISE_CHATFLOW_ID' as a default, but doesn't provide explicit guidance on when to use this tool versus alternatives or any prerequisites. The sibling tool 'list_chatflows' is mentioned but not compared.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available chatflows from the Flowise API.
This function respects optional whitelisting or blacklisting if configured
via FLOWISE_CHATFLOW_WHITELIST or FLOWISE_CHATFLOW_BLACKLIST.
Returns:
str: A JSON-encoded string of filtered chatflows.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's a read operation (implied by 'List'), respects configuration-based filtering, and returns JSON-encoded data. However, it doesn't mention potential rate limits, authentication needs, or error handling, leaving some gaps in behavioral context.
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 perfectly concise and well-structured: three sentences with zero waste. The first sentence states the purpose, the second explains configuration behavior, and the third specifies the return format. Every sentence earns its place and information is appropriately front-loaded.
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 (0 parameters, no output schema, no annotations), the description provides good contextual completeness. It covers purpose, behavioral constraints (filtering), and return format. However, without annotations or output schema, it could benefit from more detail about the structure of returned JSON or error conditions for a fully complete picture.
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 0 parameters with 100% coverage, so the description doesn't need to compensate for parameter documentation. The description appropriately focuses on behavioral aspects rather than parameter semantics, which is correct for a parameterless tool. It adds value by explaining the filtering behavior beyond what the empty 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 purpose: 'List all available chatflows from the Flowise API.' This specifies the verb ('List') and resource ('chatflows'), though it doesn't explicitly differentiate from its sibling tool 'create_prediction' beyond the obvious action difference. The purpose is clear but lacks explicit sibling comparison.
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 implies usage context by mentioning whitelisting/blacklisting configuration, but it doesn't provide explicit guidance on when to use this tool versus alternatives. There's no mention of when not to use it or direct comparison to 'create_prediction', leaving usage context somewhat implied rather than clearly articulated.
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.
create_predictionlist_chatflowsScored across 2 tools
The two tools have completely distinct purposes: one lists available chatflows, and the other creates predictions using a specific chatflow. There is no overlap in functionality, and an agent can easily differentiate between them based on their clear descriptions.
Both tools follow a consistent verb_noun naming pattern: list_chatflows and create_prediction. The naming is predictable and readable, with no deviations in style or convention across the tool set.
With only 2 tools, the server feels thin for its apparent domain of interacting with Flowise chatflows. While the tools cover listing and creating predictions, the lack of operations like updating, deleting, or managing chatflows suggests an incomplete surface that may limit agent workflows.
The tool set is severely incomplete for a chatflow management domain. It only provides list and create operations, missing essential CRUD functionality such as updating or deleting chatflows, retrieving specific chatflow details, or handling prediction updates. This will likely cause agent failures in more complex scenarios.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
A Model Context Protocol server for Wix AI tools
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
