Claix

Agent mode text/HTML/XML to JSON

claix.agent.text

Read-onlyIdempotent

Run text/HTML/XML extraction plus Agent mode reasoning. Requires is_agent_mode. Send content (no file). Returns data[] and agent_data. POST /agent/txt-json.

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).
`content`	Yes	Plain text, HTML, or XML already processed. Do not send a file. Example: <html><body>Invoice F-1</body></html>
`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.

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

B3.1/5.0

Behavior4/5

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

While annotations already indicate read-only and idempotent behavior, the description adds information about required is_agent_mode and the output type (data[] and agent_data), offering additional transparency beyond the annotations without contradiction.

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

Conciseness4/5

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

The description is concise, containing multiple short sentences that convey the key points: what it does, requirements, output, and endpoint. It is well-structured and to the point, though slightly repetitive in mentioning 'agent mode' multiple times.

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

Completeness3/5

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

The description provides a minimal overview of inputs and outputs but lacks details on the exact structure of the returned data or specific use cases. Given the simplicity of the tool and presence of annotations, it is adequate but not fully complete.

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?

The input schema descriptions cover all three parameters comprehensively, so the baseline is 3. The description does not add extra meaning beyond the schema, but the schema is sufficient.

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

Purpose3/5

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

The description states it performs text/HTML/XML extraction and agent mode reasoning, which is somewhat specific but does not clearly distinguish from sibling extraction tools like extract.text or convert.json_to_excel. The purpose is understandable but could be more precise.

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

Usage Guidelines2/5

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

No explicit guidance is provided on when to use this tool versus alternatives. It simply describes the action without mentioning conditions or preferences for selecting it over similar 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.

Resources

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