Claix

Agent mode Excel/CSV to JSON

claix.agent.excel

Read-onlyIdempotent

Run Excel/CSV extraction plus Agent mode semantic reasoning. Requires is_agent_mode on the schema. Returns data[] and agent_data with typed answers from agent_definition.

Input Schema

TableJSON Schema

Name	Required	Description
`api_key`	No	API key secreta de Claix. Ejemplo: claix_sk_abc123.... Opcional si la conexión MCP envía x-api-key en cabecera HTTP (recomendado en Smithery/Cursor).
`filename`	No	Nombre original del archivo con extensión. Ejemplo: factura-2026-03.pdf. Ayuda a inferir el MIME cuando envías file_base64.
`file_path`	No	URL HTTPS pública del archivo a procesar. Ejemplo: https://cdn.example.com/factura.pdf. No uses rutas locales del PC del usuario.
`schema_id`	Yes	UUID 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_base64`	No	Archivo 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

Name	Required	Description
`data`	No	JSON payload from the Claix API (extracted records, schema list, or Excel export metadata).
`error`	No	Human-readable error message when success is false.
`success`	Yes	True when Claix returned a successful response. False when isError is set on the tool result.

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations by specifying a schema prerequisite ('Requires is_agent_mode on the schema') and outlining the return structure ('data[] and agent_data with typed answers'). 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?

The description is two concise sentences. The first sentence front-loads the main purpose, and the second adds a prerequisite and output summary. Every word earns its place with no redundancy or fluff.

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?

The description covers the core purpose, a key prerequisite, and the main return fields, while output schema and annotations fill in details. It does not elaborate on what 'semantic reasoning' entails or how file inputs are selected, but those are either covered in the schema or not essential for invoking the tool correctly.

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?

All 5 parameters have detailed descriptions in the input schema (100% coverage), so the schema carries the parameter documentation burden. The description does not add parameter-specific meaning; it only references a schema configuration flag ('is_agent_mode') that is not among the tool's own parameters. Baseline 3 is appropriate.

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 runs 'Excel/CSV extraction plus Agent mode semantic reasoning', naming the resource and adding a specific capability that distinguishes it from claix.extract.excel and sibling agent tools. The mention of returning 'agent_data with typed answers' further differentiates it from simple extraction.

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 implies use for semantic reasoning over Excel/CSV via 'Agent mode semantic reasoning' and names a prerequisite ('Requires is_agent_mode on the schema'). However, it does not explicitly state when not to use it or compare directly with alternative extract tools, so it stops short of full exclusion guidance.

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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