No install? Use cloud.anythingmcp.com directly.
Sign in, install the Coda in one click, paste the credentials, mint an MCP API key — done. No Docker, no git clone, no local server to run.
TL;DR
Drive Coda (docs + tables + formulas) from any AI agent: docs, tables, rows, columns, formulas, packs, controls. 13 tools, Bearer token auth.
💡 ¿Sin instalación? Usa cloud.anythingmcp.com directamente. Inicia sesión, pulsa Connectors → Coda, pega tus credenciales, genera una MCP API key — listo. Sin Docker, sin
git clone, sin servidor local.
Coda + Gemini
Drive Coda (docs + tables + formulas) from any AI agent: docs, tables, rows, columns, formulas, packs, controls. 13 tools, Bearer token auth.
Requisitos previos
Las instrucciones de configuración completas están incluidas en el conector (visibles en el store al seleccionarlo). Las variables de entorno requeridas:
CODA_API_TOKEN
Paso 1 — Obtener credenciales
io/developers/apis/v1).
Setup:
- Sign in to https://coda.io → top-right avatar → Account → API settings → Generate API token.
- Name the token. Copy it. Set
CODA_API_TOKEN.
Authentication: Authorization: Bearer ${CODA_API_TOKEN}.
Resource hierarchy: Doc → Section (Page) → Table → Row → Cell. Coda's twist: tables are first-class with structured columns and types; rows are addressable individually; columns can hold formulas.
Doc IDs look like dXXXXXXXXXX (10-char prefix d). Visible in the doc URL.
Table IDs look like grid-XXXXX. Discover via coda_list_tables(docId).
…(continued in the in-app connector instructions)
Paso 2 — Instalar el adapter
git clone https://github.com/HelpCode-ai/anythingmcp.git
cd anythingmcp && docker compose up -d
Paso 3 — Añadir el conector en Gemini
Gemini CLI lee servidores MCP desde ~/.gemini/settings.json (o %APPDATA%\gemini\settings.json en Windows). Añade:
{
"mcpServers": {
"anythingmcp": {
"httpUrl": "https://cloud.anythingmcp.com/mcp",
"headers": { "Authorization": "Bearer YOUR_MCP_API_KEY" }
}
}
}
- Obtén tu MCP API key desde AnythingMCP → Perfil → MCP API Keys → Nueva Key.
- Guarda el archivo y reinicia
gemini. - Ejecuta
/mcpen la Gemini CLI —Codadebería aparecer como disponible. - Vertex AI Studio: pasa
https://cloud.anythingmcp.com/mcpal arraytoolsde tu petición con el mismo header Bearer.
Herramientas disponibles
| Tool | What it does |
|---|---|
coda_whoami | Return the user the token belongs to |
coda_list_docs | List docs the user can access |
coda_get_doc | Fetch a single doc by ID |
coda_list_tables | List tables in a doc |
coda_list_columns | List columns in a table |
coda_list_rows | List rows in a table or view |
coda_get_row | Fetch a single row by ID |
coda_insert_rows | Insert (or upsert with keyColumns) rows into a table |
coda_update_row | Update a specific row by ID |
coda_delete_row | Delete a single row by ID |
coda_delete_rows | Bulk-delete rows by IDs (more efficient than per-row) |
coda_get_formula_result | Evaluate a named formula on a doc |
coda_get_mutation_status | Check the status of an async mutation (insert/update/delete) by its requestId returned from those calls |
FAQ
¿Gemini 1.5 Pro o 2.x soportan MCP? Sí — Gemini CLI ≥ 0.4 y la API de tools de Vertex AI aceptan conectores MCP httpUrl con header Bearer.
Siguientes pasos
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