Un servidor MCP que proporciona herramientas para interactuar con la red Farcaster ( farcaster.xyz ), lo que permite que los modelos de IA obtengan transmisiones, busquen canales y analicen contenido.
Obtener transmisiones de usuario : recupera transmisiones de un usuario específico de Farcaster por FID
Obtener casts de nombre de usuario : recupera casts de un usuario específico de Farcaster por nombre de usuario
Obtener transmisiones de canal : recupera transmisiones de un canal Farcaster específico
Related MCP server: Lens Protocol MCP Server
# Clone the repository
git clone https://github.com/manimohans/farcaster-mcp.git
cd farcaster-mcp
# Install dependencies
npm install
# Build the project
npm run buildnpm startnpx @modelcontextprotocol/inspector node ./build/index.jsInstalar Claude para escritorio
Abra la configuración de la aplicación Claude para escritorio en:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Ventanas: %APPDATA%\Claude\claude_desktop_config.json
Agregue la siguiente configuración:
{
"mcpServers": {
"farcaster": {
"command": "node",
"args": ["/absolute/path/to/farcaster-mcp/build/index.js"]
}
}
}Reiniciar Claude para escritorio
Este proyecto incluye archivos de configuración de Smithery para una fácil implementación:
# Install Smithery CLI
npm install -g @smithery/cli
# Deploy to Smithery (specify the client, e.g., claude, cline, windsurf, etc.)
npx @smithery/cli install @manimohans/farcaster-mcp --client claudeOpciones de cliente disponibles: claude, cline, windsurf, roo-cline, witsy, enconvo
Recupera los lanzamientos de un usuario específico de Farcaster por su FID (ID de Farcaster).
Parámetros:
fid : ID de usuario de Farcaster (número)
limit (opcional): número máximo de conteos a devolver (predeterminado: 10)
Ejemplo de consulta: "Muéstrame los últimos lanzamientos de FID 6846".
Recupera los lanzamientos de un usuario específico de Farcaster por su nombre de usuario.
Parámetros:
username : nombre de usuario de Farcaster (cadena)
limit (opcional): número máximo de conteos a devolver (predeterminado: 10)
Ejemplo de consulta: "Muéstrame los últimos lanzamientos del nombre de usuario 'mani'".
Recupera transmisiones de un canal Farcaster específico.
Parámetros:
channel : Nombre del canal o URL (cadena)
limit (opcional): número máximo de conteos a devolver (predeterminado: 10)
Ejemplo de consulta: "Muéstrame los últimos lanzamientos del canal 'aichannel'".
Este repositorio incluye los archivos de configuración necesarios para Smithery:
smithery.yaml : configuración de YAML para la implementación de Smithery
smithery.json : configuración JSON para las capacidades de Smithery
Dockerfile : configuración del contenedor para la implementación de Smithery
Esta implementación utiliza la API Farcaster Hubble para obtener datos.
# Run in development mode
npm run devInstituto Tecnológico de Massachusetts (MIT)
Get casts from a specific Farcaster channel
| Name | Required | Description | Default |
|---|---|---|---|
| channel | Yes | Channel name (e.g., 'aichannel') or URL | |
| limit | No | Maximum number of casts to return (default: 10) |
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 states what the tool does but doesn't describe behavioral traits such as whether it's read-only, has rate limits, authentication needs, pagination behavior, or what the return format looks like. This leaves significant gaps for a tool that presumably fetches data.
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 that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every part contributing to understanding the core function.
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 complexity of a data-fetching tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects like safety, response format, or error handling, which are crucial for an agent to use the tool effectively in 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 description doesn't add any parameter-specific information beyond what's already in the input schema, which has 100% schema description coverage. It mentions 'specific Farcaster channel' but doesn't elaborate on the 'channel' parameter's semantics or the 'limit' parameter's implications. Baseline 3 is appropriate since the schema does the heavy lifting.
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 casts') and target resource ('from a specific Farcaster channel'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get-user-casts' or 'get-username-casts', which would require mentioning channel-specific versus user-specific retrieval.
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 like the sibling tools 'get-user-casts' or 'get-username-casts'. It lacks any context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get casts from a specific Farcaster user by FID
| Name | Required | Description | Default |
|---|---|---|---|
| fid | Yes | Farcaster user ID (FID) | |
| limit | No | Maximum number of casts to return (default: 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on permissions, rate limits, error handling, or return format. For a read operation with no annotations, this leaves significant gaps in understanding how the tool behaves.
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, clear sentence with zero waste. It is front-loaded with the core purpose and efficiently conveys the essential information without unnecessary elaboration.
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 low complexity (2 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It lacks details on behavioral aspects like pagination, error cases, or output structure, which are important for a read operation without annotations to guide the agent.
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 100%, so the schema already documents both parameters (fid and limit) adequately. The description adds no additional meaning beyond what the schema provides, such as parameter interactions or usage examples, which aligns with the baseline score when schema coverage is high.
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 casts') and specifies the resource ('from a specific Farcaster user by FID'), making the purpose explicit. It distinguishes from sibling tools like 'get-channel-casts' and 'get-username-casts' by focusing on user ID rather than channel or username.
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 like 'get-username-casts' or 'get-channel-casts'. It does not mention prerequisites, exclusions, or contextual factors that would help an agent choose between these sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get casts from a specific Farcaster username
| Name | Required | Description | Default |
|---|---|---|---|
| username | Yes | Farcaster username | |
| limit | No | Maximum number of casts to return (default: 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on traits like whether it's read-only, requires authentication, has rate limits, returns paginated results, or handles errors. For a retrieval tool with zero annotation coverage, this is a significant gap in transparency.
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 that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, achieving optimal conciseness.
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 complexity (data retrieval with parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'casts' entail (e.g., content, metadata), return format, error handling, or behavioral constraints. For a tool with these contextual gaps, the description should provide more completeness to aid the agent.
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 100%, with clear descriptions for both parameters ('username' as Farcaster username and 'limit' with default). The description adds no additional parameter semantics beyond what the schema provides, such as format examples for usernames or constraints on limit values. Baseline 3 is appropriate since the schema does the heavy lifting.
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 'casts from a specific Farcaster username', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get-channel-casts' and 'get-user-casts', which likely retrieve casts by different criteria. The description is specific but lacks sibling distinction.
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. With sibling tools like 'get-channel-casts' and 'get-user-casts' available, there's no indication of when this username-based retrieval is preferred, such as for public profiles or specific user identification. No exclusions or prerequisites are mentioned.
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.
get-channel-castsget-user-castsget-username-castsScored across 3 tools
The three tools all retrieve casts but from different sources (channel, user by FID, username), which provides some distinction. However, the overlap in purpose (getting casts) and the potential confusion between user-based tools (FID vs. username) create moderate ambiguity, as an agent might struggle to choose the right one without clear context about the available identifier.
All tool names follow a consistent verb-noun pattern with hyphens (get-channel-casts, get-user-casts, get-username-casts). This uniformity makes the set predictable and easy to understand, with no deviations in naming style or structure.
With only 3 tools, the server feels thin for a Farcaster domain, which likely involves more operations like posting casts, interacting with content, or accessing user profiles. This limited set may hinder agents from performing comprehensive tasks, indicating an under-scoped tool surface.
The tool set is severely incomplete for a Farcaster server, covering only read operations for casts from specific sources. It lacks essential CRUD operations such as creating or deleting casts, user management, channel interactions, or broader API features, leaving significant gaps that will cause agent failures in typical workflows.
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