customclaw-mcp
customclaw-mcp
Instala, no regeneres. Servidor MCP para el registro de CustomClaw: 37 utilidades de archivo único verificadas para código de LLM y agentes (limitadores de tasa que respetan
Retry-After, reparación de JSON, cachés de respuesta, reintentos con retroceso, contadores de tokens, depuradores de PII). Tu agente obtiene cuatro llamadas a herramientas nativas en lugar de 120 líneas de código nuevo que parece plausible.
📖 Antecedentes: El problema de la amnesia de utilidades — por qué tu agente de codificación reescribió el mismo limitador de tasa 47 veces.
Instalación en 30 segundos
npx -y customclaw-mcpCopia ese comando en la configuración de tu cliente MCP (fragmentos a continuación) y listo. Las herramientas gratuitas se instalan sin autenticación.
Qué hace
Conecta el catálogo de CustomClaw (37 utilidades, 32 gratuitas) a cualquier host del Protocolo de Contexto de Modelo (MCP) — Claude Desktop, Claude Code, Cursor, Windsurf, cualquier cosa que hable MCP — como cuatro herramientas:
Herramienta | Argumentos | Propósito |
| — | Catálogo completo (caché de 5 min) |
|
| Coincidencia difusa en slug/nombre/descripción |
|
| Detalle completo de una utilidad |
|
| Obtener carga útil + escribir archivos |
Instalación (por cliente)
Claude Desktop
~/Library/Application Support/Claude/claude_desktop_config.json (macOS) o %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"customclaw": {
"command": "npx",
"args": ["-y", "customclaw-mcp"]
}
}
}Reinicia Claude Desktop. Las herramientas aparecerán en el selector de herramientas.
Claude Code
claude mcp add customclaw -- npx -y customclaw-mcpCursor / Windsurf / cualquier host MCP stdio
Apunta el host a npx -y customclaw-mcp.
HTTP transmitible (smithery, ChatGPT, etc.)
Usa el endpoint alojado: https://customclaw.company/mcp. Registrado en smithery como onlythebestswfl/customclaw.
Ejemplos de prompts
Una vez conectado:
"Añade un limitador de tasa que respete Retry-After." →
search_utilities→install_utility rate-limit-handler"Necesito analizar JSON roto de la salida de Claude." →
json-repair"¿Qué tienes para el almacenamiento en caché de respuestas de LLM?" →
search_utilities→ tabla de resultados cortos"Instala
token-optimiseren./src/lib." →install_utilitydirecto"Escanea los prompts de este proyecto en busca de inyecciones —¿hay alguna herramienta?" →
injection-scanner
Referencia rápida de Tarea → slug
Si necesitas… | Slug |
Limitación de tasa con manejo de |
|
Reparación de JSON para salida de LLM |
|
Caché de respuesta por hash de prompt |
|
Reintento con retroceso exponencial |
|
Conteo de tokens entre modelos |
|
Depuración de PII antes del registro |
|
Escáner de inyección de prompts |
|
Pronosticador de costos por proveedor |
|
Lista completa mediante list_utilities o customclaw.company/api/catalog.
Utilidades de pago
Para las utilidades de pago, pasa el session_id de pago de Stripe de tu correo electrónico de recibo como el argumento session_id para install_utility. Sin él, la herramienta devuelve un 401 útil.
Entorno
CUSTOMCLAW_BASE— anulación de la URL base del registro (predeterminadohttps://customclaw.company). Útil para entornos de prueba o espejos autohospedados.
Cómo funciona
Servidor MCP stdio de Node.js. Dependencia:
@modelcontextprotocol/sdk.Catálogo obtenido de
GET /api/catalog; caché de 5 min por proceso.Cargas útiles de
GET /api/cli?slug=<slug>[&session_id=<cs_...>]— el mismo endpoint que usa elcustomclaw-clioficial.install_utilityse niega a escribir fuera detarget_dir(protección contra recorrido de rutas).
Enlaces
Registro: customclaw.company
Compañero CLI:
customclaw-cli
Licencia
MIT.
Available Tools
4 toolsget_utility_infoA
Return the full catalog entry for a single utility or agent by slug (name, tagline, description, price, free flag, category, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The utility or agent slug, e.g. "token-optimiser". |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must cover behavior. It indicates a read operation without side effects, but does not address error cases (e.g., missing slug), authentication needs, or rate limits. This is adequate for a simple retrieval but lacks full 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 sentence that efficiently conveys the core function and included fields, with no extraneous information. It is well-structured and 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 simple single-parameter schema and no output schema, the description adequately explains what the tool returns and how to use it. It could mention the outcome for invalid slugs, but overall it is sufficiently complete for its complexity.
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 covers 100% with a description for 'slug'. The description's mention of 'by slug' mirrors the schema, adding no new semantic value beyond what the schema already provides. Therefore, the baseline score of 3 applies.
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 returns a full catalog entry for a single utility/agent by slug, listing specific fields (name, tagline, description, price, etc.). It differentiates from sibling tools like list_utilities and search_utilities, which handle multiple results.
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 specifies the required identifier (slug) but does not explicitly provide when or when not to use this tool versus siblings. However, the context of siblings implies that get_utility_info is for a single detailed entry, while list_utilities gives an overview and search_utilities finds by criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
install_utilityA
Fetch a CustomClaw utility payload and write its files into target_dir. For paid utilities, pass session_id (the Stripe checkout session_id from the buyer's receipt email). Returns the list of files written and any npm dependencies the caller should install — this tool does NOT run npm install itself; the host agent decides when to install deps.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The utility slug to install. | |
| target_dir | Yes | Directory to write files into. Absolute path preferred; relative paths are resolved against the server process cwd. | |
| session_id | No | Optional. Stripe checkout session_id for paid utilities. Found as session_id=... in the CustomClaw receipt email download URL. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that it writes files, returns a list of files and npm dependencies, and refrains from running npm install. However, it does not mention potential side effects like overwriting existing files or required permissions.
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 structured. The first sentence states the main action, the second provides critical usage details (session_id and npm install behavior). 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?
No output schema but the description fully explains return value (list of files and npm dependencies) and what the tool does not do (npm install). For a tool with 3 parameters and no nested objects, this is complete.
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%, and the description adds context beyond the schema: slug is clarified as 'utility slug', target_dir includes guidance on absolute vs relative paths, and session_id explains where to find it (Stripe checkout session_id from receipt email).
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 'Fetch a CustomClaw utility payload and write its files into target_dir', which is a specific verb and resource. It distinguishes from sibling tools (get_utility_info, list_utilities, search_utilities) which are all informational while this is an installation tool.
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?
Provides explicit guidance on when to pass session_id (for paid utilities) and clarifies that the tool does NOT run npm install itself, leaving that decision to the host agent. This helps the agent understand when and how to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_utilitiesA
List every utility and agent available in the CustomClaw registry (https://customclaw.company). Results are cached for 5 minutes. Returns catalog JSON with utilities and agents arrays.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses caching (5 minutes) and return structure (catalog JSON with utilities and agents arrays), which is transparent for a simple read-only tool. However, it does not mention authentication or error handling.
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 efficient sentences: first states purpose and source, second adds caching and return format. No wasted 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 tool with no parameters and no output schema, the description sufficiently covers the action, source, caching, and return format. It could mention the JSON structure details but is adequate.
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?
There are no parameters, so the baseline is 4. The description adds no parameter info since none exist.
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 uses a specific verb ('List') and resource ('every utility and agent') and distinguishes from siblings by indicating it returns all items from the registry, whereas siblings like get_utility_info or search_utilities likely target specific items.
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 broad listing use but does not explicitly state when not to use it or mention alternatives like search_utilities or get_utility_info for filtered queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_utilitiesA
Fuzzy-search the CustomClaw registry by keyword. Matches across slug, name, tagline, description, and category. Returns the top 25 matches sorted by relevance.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keyword(s) to search for, e.g. "token optimiser" or "stripe". |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses fuzzy matching behavior, fields searched, result limit of 25, and relevance-based sorting. While it does not mention pagination or error handling, the provided traits are sufficient for a read-only search operation.
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, front-loaded with purpose, and every sentence adds value. No extraneous information, efficiently conveying scope and behavior.
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 simple tool with one parameter and no output schema, the description adequately covers input (keyword) and output (top 25 sorted results). It lacks mention of empty results or pagination but is complete for typical use.
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 a single query parameter already described with examples. The description adds meaningful context beyond schema by specifying fuzzy matching and the fields searched, enhancing understanding of how the parameter is used.
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 states 'Fuzzy-search the CustomClaw registry by keyword' with specific verb and resource. It details matching fields (slug, name, tagline, description, category) and output (top 25 sorted by relevance), clearly distinguishing it from siblings like get_utility_info (specific utility) or list_utilities (all utilities).
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 for keyword search but does not explicitly state when to use this tool versus alternatives (e.g., for exact match use list_utilities, for specific utility use get_utility_info). No exclusion criteria or when-not-to-use guidance is provided.
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.
4 tool updates
v0.1.1- First observed
get_utility_info - First observed
install_utility - First observed
list_utilities - First observed
search_utilities
TDQS
Scored across 4 tools
Each tool has a distinct purpose: getting info for a single utility, installing a utility, listing all utilities, and searching. No overlapping functionality, so an agent can easily distinguish them.
All tool names follow a consistent ��verb_noun�� pattern (get_utility_info, install_utility, list_utilities, search_utilities) with underscores and lowercase, making the set predictable.
Four tools cover the core operations for a registry MCP (list, search, get details, install) without being excessive or insufficient, fitting the domain well.
The toolset provides a complete workflow from discovery (list, search, get info) to installation, with no obvious gaps for its intended use as a consumer-facing registry interface.
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