Claix

Extract text/HTML/XML to JSON

claix.extract.text

Read-onlyIdempotent

Extract structured JSON from already processed plain text, HTML, or XML using a Claix txt-json schema. Send the payload in the content field (no file). Max 300,000 characters. POST /api/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

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral constraints beyond those: max 300,000 characters, no file upload, and the requirement that input must already be processed. 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.

Conciseness4/5

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

The description is short, front-loaded with purpose, and includes only a few supporting details. The phrase 'POST /api/txt-json' is arguably redundant for MCP invocation, but it is minor and does not significantly hurt clarity.

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, output schema, and annotations, the description is sufficiently complete. It covers input type, payload mechanism, size limit, and schema usage. Return values are already documented by the output schema, so no additional explanation is needed.

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 descriptions cover all parameters (100%), so the baseline is 3. The description adds value by specifying the content field as the payload location, emphasizing 'no file', and introducing the 300,000-character limit, which is not present in the schema. This supplements the parameter meaning beyond the schema alone.

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 states a specific action ('Extract structured JSON') and resource ('already processed plain text, HTML, or XML'), and clarifies the output format. It also distinguishes itself from sibling extract tools by explicitly noting 'no file' and 'already processed', making its scope clear.

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 it for already processed text/HTML/XML, send payload in content, and respect the 300,000-character limit. It does not explicitly name alternative tools or state when not to use it, but the 'no file' and 'already processed' cues strongly imply the boundary versus file-based extract 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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