ai-workers-mcp

ai_extract

Extract specified fields from plain text and return them as structured JSON, using a free language model.

Instructions

Extract named fields from plain text as JSON using a configured free LLM. Plain prose only — no code, secrets, or file paths.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>",
      "fields": "<fields>"
    },
    "name": "ai_extract"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Plain text to extract from
`fields`	Yes	Field names to extract, e.g. ['author', 'date', 'title']

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jun 14, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses it uses a free LLM and returns JSON, and restricts input to plain prose. It doesn't detail potential state changes, rate limits, or auth needs, but for a read-like extraction this is acceptable. Adds moderate behavioral context.

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?

Two sentences, no fluff. The action and constraints are front-loaded. Every word earns its place.

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 straightforward tool with no output schema, the description covers core functionality and constraints. It doesn't explain return value structure, but that's acceptable per rules. Could be slightly more complete with an explicit note about output format, but it's already implied.

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 the description adds an example for fields parameter ('e.g. ['author', 'date', 'title']'), which provides some additional clarity beyond the schema. Baseline 3 is appropriate as the description does not significantly extend 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 verb 'extract', resource 'named fields from plain text', and output format 'as JSON'. It distinguishes from sibling tools by specifying a very different operation (extracting fields) vs. summarizing, classifying, etc.

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?

Provides clear input constraints: 'Plain prose only — no code, secrets, or file paths.' This helps the agent decide when to use this tool and when to choose another. However, no explicit mention of alternatves among siblings.

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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