Una implementación de servidor del Protocolo de Contexto de Modelo (MCP) que proporciona capacidades de control y gestión de red a través del controlador SDN de POX. Este servidor permite la programación de red basada en Python, la gestión de dispositivos OpenFlow y el análisis automatizado de red mediante la arquitectura modular de POX. Ideal para entornos educativos, prototipado de redes e investigación de SDN.
Related MCP server: F5 MCP Server
El servidor expone dos recursos dinámicos:
pox://network-config : Un memorando completo de configuración del controlador POX
Realiza un seguimiento de los componentes POX activos y sus configuraciones
Registra la topología de la red y las reglas de flujo
Mantiene información descubierta sobre la red
pox://topology : Vista de topología de red en tiempo real
Muestra rutas de datos OpenFlow activas (conmutadores)
Los mapas alojan ubicaciones y conexiones
Muestra el estado del enlace y las asignaciones de puertos
El servidor proporciona tres indicaciones especializadas:
pox-network-manager : Indicador interactivo para la gestión del controlador POX
Argumento requerido: topic : El aspecto de control de red en el que se debe centrar la atención
Ayuda a configurar componentes y módulos POX
Guía a través de la implementación de políticas de red
Se integra con la nota de configuración de red
simple-hub : Implementación básica de concentrador L2 usando POX
Argumento obligatorio: dpid - El identificador de la ruta de datos
Demuestra la programación basada en eventos de POX
Muestra el manejo básico de paquetes y las inundaciones.
Explica los mecanismos centrales de la POX
learning-switch : implementación del interruptor de aprendizaje L2
Argumento obligatorio: dpid - El identificador de la ruta de datos
Muestra la gestión de mesas de POX
Implementa el aprendizaje y reenvío de MAC
Demuestra las capacidades de manejo de paquetes de POX
El servidor ofrece cinco herramientas principales:
get_switches
Enumere todas las rutas de datos de OpenFlow conectadas
No se requiere entrada
Devoluciones: Matriz de conmutadores activos controlados por POX
Incluye estado de conexión y capacidades.
get_switch_desc
Obtenga información detallada sobre la ruta de datos
Aporte:
dpid (cadena): identificador de ruta de datos
Devoluciones: detalles y características del conmutador administrado por POX
get_flow_stats
Recuperar estadísticas de flujo de POX
Aporte:
dpid (cadena): identificador de ruta de datos
match (objeto, opcional): Estructura de coincidencia POX
table_id (cadena, opcional): ID de tabla de OpenFlow
out_port (cadena, opcional): filtro de puerto de salida
Devoluciones: estadísticas de flujo POX, incluidos los recuentos de paquetes
set_table
Configurar tablas de flujo POX
Aporte:
dpid (cadena): identificador de ruta de datos
flows (matriz): especificaciones de flujo POX
Devoluciones: Confirmación de actualización de la tabla de flujo
append_insight
Agregar información de red al memorando de configuración de POX
Aporte:
insight (cadena): observación o análisis de red
Devoluciones: Confirmación de adición de Insight
Actualiza el recurso pox://network-config
{
"mcpServers": {
"pox": {
"command": "uv",
"args": [
"--directory",
"parent_of_servers_repo/servers/src/mcp_server_pox",
"run",
"server.py"
],
"env": {
"POX_SERVER_URL": "http://localhost:8000"
}
}
}
}Este servidor MCP cuenta con la licencia MIT. Esto significa que puede usar, modificar y distribuir el software libremente, sujeto a los términos y condiciones de la licencia MIT. Para más detalles, consulte el archivo de LICENCIA en el repositorio del proyecto.
¡Agradecemos sus contribuciones! No dude en enviar solicitudes de incorporación de cambios, informar errores o sugerir nuevas funciones.
Add a network insight to the configuration memo
| Name | Required | Description | Default |
|---|---|---|---|
| insight | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behaviors. It only says 'Add' but omits details like idempotency, side effects, or whether insights are appended or overwritten.
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, efficient and front-loaded. However, it could include more context without becoming verbose, so it's not perfect.
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 no output schema and no annotations, the description should cover behavior and parameter details. It only covers purpose, leaving gaps in understanding post-conditions and usage.
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%. The parameter name 'insight' is vague; the description says 'network insight' but does not clarify format, constraints, or examples, failing to add value beyond the schema.
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 'Add a network insight' and the target 'configuration memo'. It distinguishes from sibling tools like get_flow_stats (retrieval) and set_table (setting table), as this is the only tool for appending insights.
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 vs alternatives, no prerequisites, and no context about scenarios like whether the memo must exist beforehand.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get flow statistics from a switch
| Name | Required | Description | Default |
|---|---|---|---|
| dpid | Yes | ||
| match | No | ||
| table_id | No | ||
| out_port | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It only implies a read operation ('Get') but does not mention if it is expensive, requires authentication, or any side effects. This is insufficient for safe selection.
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 very short (one sentence), which might seem concise, but it fails to convey necessary information. It does not earn its place because it adds little value beyond the tool name.
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 has 4 parameters, no output schema, and no annotations, the description is severely incomplete. It does not explain return values, parameter constraints, or how to use the tool 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 description coverage is 0%, and the description does not add meaning to any of the 4 parameters (dpid, match, table_id, out_port). The agent receives no help understanding parameter roles beyond their names.
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 'Get flow statistics from a switch' clearly states the action (get) and resource (flow statistics from a switch), distinguishing it from siblings like get_switch_desc or get_switches. However, it could be more specific about what statistics are included.
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 given on when to use this tool vs alternatives, such as prerequisites, conditions, or when not to use it. The description lacks any contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a specific switch
| Name | Required | Description | Default |
|---|---|---|---|
| dpid | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description does not disclose behavioral traits like side effects, required permissions, or data sensitivity. It implies a read operation but does not confirm safety or discuss response structure.
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, which is concise but lacks structure. It does not waste words but also does not provide rich detail. Acceptable for a simple tool, but could be improved.
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 parameter, no output schema), the description fails to specify what 'detailed information' encompasses or any return format. Sibling tools are not referenced to complete the 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?
With 0% schema description coverage, the description adds no meaning to the 'dpid' parameter. It does not explain what 'dpid' represents or how to obtain it, leaving the agent to infer from the tool name.
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 verb 'Get' and resource 'detailed information about a specific switch', which distinguishes it from the sibling tool 'get_switches' (likely for listing all switches). However, it could be more specific about what 'detailed information' includes.
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 guidance on when to use this tool versus alternatives, such as comparing with 'get_switches' or 'get_flow_stats'. No preconditions or caveats are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a list of all connected OpenFlow switches
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description implies a safe read operation, but lacks details on potential failures (e.g., no connected switches) or rate limits. The transparency is adequate for a simple list tool but not thorough.
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, efficient sentence with no superfluous information, perfectly sized for the simplicity of the tool.
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?
The description lacks return value details (e.g., structure of each switch), which is notable since there is no output schema. However, the tool's simplicity and zero parameters mitigate the gap somewhat.
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 zero parameters and schema coverage at 100%, the description adds no parameter details but none are needed. The baseline of 4 applies per guidelines.
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 a list') and the resource ('all connected OpenFlow switches'), differentiating it from sibling tools like get_flow_stats or get_switch_desc.
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, such as when detailed switch descriptions are needed via get_switch_desc.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Set the flow table on a switch
| Name | Required | Description | Default |
|---|---|---|---|
| dpid | Yes | ||
| flows | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description should disclose behavioral traits. It only states 'set' but doesn't clarify side effects (e.g., whether existing flows are overwritten), idempotency, authentication needs, or what happens to unmatched flows. This lack of transparency could lead to unintended consequences.
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, front-loaded with the key action. It is concise, but the brevity sacrifices critical detail. While it avoids verbosity, it could include more essential information without becoming overly long.
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 has two parameters (one complex), no output schema, and no annotations, the description is too sparse. It does not address input formats, behavioral effects, or return values, leaving significant gaps for the agent to interpret 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 description coverage is 0%, so the description must compensate. It mentions 'dpid' as a switch identifier and 'flows' as flow entries, but provides no format details (e.g., dpid syntax, flow object structure). The flows parameter is an array of objects with no defined properties, leaving the agent unable to construct valid input.
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 verb 'set' and the resource 'flow table on a switch', which distinguishes it from sibling tools that perform read or append operations. However, it lacks detail on the scope (e.g., whether it replaces all flows or merges), so it falls short of a perfect 5.
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 usage context or guidance is provided. The description does not indicate when to use this tool versus alternatives (e.g., for initial configuration vs. incremental updates), nor does it mention prerequisites or limitations.
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.
v0.1.0append_insightget_flow_statsget_switch_descget_switchesset_tableEach tool targets a distinct operation: listing switches, getting switch details, getting flow stats, setting the flow table, and appending an insight. No overlapping purposes.
All tool names follow a consistent verb_noun pattern in snake_case (append_insight, get_*, set_table), making them predictable and easy to understand.
With 5 tools, the server is well-scoped for its purpose of OpenFlow switch management. The number falls within the ideal range and each tool provides necessary functionality.
The set covers basic operations like querying and setting but lacks tools for deleting or modifying flow rules, or managing switch connections, leaving notable gaps for full lifecycle management.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.
The Telnyx MCP server is an official implementation of the Model Context Protocol that enables AI clients (like Claude Desktop, Cursor, and OpenAI Agents) to interact with Telnyx's telephony, messaging, and AI assistant APIs. It provides comprehensive capabilities including making and managing phone calls, sending SMS/MMS messages, purchasing and configuring phone numbers, creating AI assistants with custom instructions, managing cloud storage buckets, scraping and embedding website content, and handling integration secrets. The server exists as both a local implementation and a remotely hosted version, allowing developers to integrate real-world communication infrastructure directly into AI applications.
The OpenMesh MCP Server provides decentralized, permissionless cloud infrastructure that integrates AI assistants with Web2 and Web3 applications without middlemen. It offers intelligent service discovery to identify MCP servers, acts as a universal proxy for routing requests to discovered services, implements performance optimization through usage feedback, and provides automatic fault tolerance with alternative services when servers become unavailable.
The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/davidlin2k/pox-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
