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by dev.claix

Convert JSON to Excel

claix.convert.json_to_excel
Read-onlyIdempotent

Convert JSON to an Excel .xlsx file using a Claix json-excel schema. Provide json_data inline or file_base64 with a .json file. Returns base64-encoded xlsx on success.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyNoAPI key secreta de Claix. Ejemplo: claix_sk_abc123.... Opcional si la conexión MCP envía x-api-key en cabecera HTTP (recomendado en Smithery/Cursor).
filenameNoNombre original del archivo con extensión. Ejemplo: factura-2026-03.pdf. Ayuda a inferir el MIME cuando envías file_base64.
file_pathNoURL HTTPS pública del archivo a procesar. Ejemplo: https://cdn.example.com/factura.pdf. No uses rutas locales del PC del usuario.
json_dataNoJSON object or array to export. Example: [{"sku":"A1","qty":2}]. Use instead of file_base64 when data is already in context.
schema_idYesUUID del schema creado en el dashboard de Claix. Ejemplo: 550e8400-e29b-41d4-a716-446655440000. Llama a claix.schemas.list primero si no lo conoces.
file_base64NoArchivo codificado en Base64. Acepta data URLs (data:application/pdf;base64,...) o Base64 puro. Ejemplo de uso: adjunta el PDF/imagen del chat como Base64 antes de llamar a extract_*.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoJSON payload from the Claix API (extracted records, schema list, or Excel export metadata).
errorNoHuman-readable error message when success is false.
successYesTrue when Claix returned a successful response. False when isError is set on the tool result.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover safety (readOnly, idempotent, non-destructive). The description adds behavioral context: it requires a schema_id (dependency) and returns base64-encoded xlsx. It also clarifies the file_base64 input should be a .json file, which the schema description ambiguously refers to as PDF/image. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences: purpose, input method, and return format. Front-loaded with the primary action, no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-param tool with an output schema, the description covers the core workflow (convert JSON to Excel), input options, and output. It doesn't explain the schema concept in depth, but that is referenced and the schema_id is documented in the schema. Given output schema exists, return values are sufficiently covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, giving a baseline of 3. The description adds meaning by explicitly stating json_data and file_base64 are alternative inputs ('or'), and clarifies that file_base64 should be a .json file, which is not clear from the schema description. This goes beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool converts JSON to an Excel .xlsx file using a Claix schema, with a specific verb and resource. It distinguishes from sibling extract/agent tools by focusing on JSON-to-Excel conversion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on how to use the tool (provide json_data inline or file_base64 with a .json file) and the return format. It doesn't explicitly name alternatives or when-not-to-use, but the use case is unambiguous given sibling tools are extract/agent tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation5/5

Every tool has a clearly distinct role: extract.* produces raw structured JSON, agent.* adds agent-mode reasoning, schemas.* manages schemas, and window_context.* handles persisted documents. Even the parallel extract/agent pairs for each format are disambiguated by the agent/group prefix and explicit descriptions about is_agent_mode.

Naming Consistency4/5

All tools follow a claix.<group>.<target> convention with lowercase snake_case, which is predictable and readable. The main inconsistency is action placement: extract.doc is verb-first while schemas.create is object-first, and convert.json_to_excel uses a noun phrase instead of a verb.

Tool Count4/5

At 17 tools, the set is slightly above the ideal 3-15 range, but each tool maps to a distinct endpoint or format variant. The parallel extract and agent families are justified by different processing modes, though they do make the surface feel a bit heavier than necessary.

Completeness4/5

The set covers schema lifecycle (create, list, delete), extraction for five major formats, agent-mode variants, document deletion, and window-context query/retrieval. Minor gaps include no schema update endpoint and no generic document listing/retrieval outside window_context.

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