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Claix

by dev.claix

Extract Excel/CSV to JSON

claix.extract.excel
Read-onlyIdempotent

Extract structured JSON from an Excel (.xlsx) or CSV file using a Claix excel-json schema. Processes the first sheet only. Returns typed JSON matching the schema. Use when the user attaches a spreadsheet or CSV.

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.
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

A4.3/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable behavior not in annotations: 'Processes the first sheet only' and 'Returns typed JSON matching the schema.' This goes beyond what annotations disclose without any contradiction.

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, front-loaded with the core action. Every sentence adds value: what it does, a key behavioral caveat, and when to use it. No fluff.

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

Completeness5/5

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

Given the rich input schema (100% coverage), comprehensive annotations, and output schema, the description covers essential context: file types, first-sheet behavior, return type, and usage scenario. Nothing critical is missing.

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

Parameters3/5

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

Schema description coverage is 100%, and each parameter (schema_id, filename, file_path, file_base64, api_key) has a detailed description. The tool description itself does not add parameter details, so baseline 3 is appropriate per the rubric.

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 extracts structured JSON from Excel/CSV files using a schema, with a specific verb and resource. It distinguishes from sibling tools like claix.extract.pdf and claix.extract.image by explicitly mentioning spreadsheet/CSV input.

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 usage context: 'Use when the user attaches a spreadsheet or CSV.' It does not explicitly name alternative tools or exclusions, but the sibling list implies the differentiation. This is clear enough but not as explicit as naming alternatives.

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.

Resources