MCP For Claude Marketplace
Structured Output Extractor

Structured Output Extractor MCP Connector for Claude

A+

Recover structured data from malformed LLM responses using deterministic regex.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

When Large Language Models fail to follow strict JSON or Pydantic schemas due to conversational filler, this MCP server acts as a rescue engine. It uses deterministic regex patterns to find signal within the noise, treating user-provided field names as anchors. You can use extract_structured_data to pull specific fields, validate_schema_integrity to ensure your schema is valid, and get_extraction_summary to evaluate the reliability of the results. It is designed to bridge the gap between messy LLM text and the structured data your applications require.

regexjson-recoveryllm-parsingstructured-datadata-integrity

3 tools expose this connector's capabilities to your AI agent.

extract_structured_data

Performs the primary rescue extraction of fields from a block of text using a provided schema

get_extraction_summary

Provides a high-level overview of a specific extraction attempt's success and reliability

validate_schema_integrity

Checks if the provided schema definition is logically sound and follows naming conventions

See how to talk to your AI agent using Structured Output Extractor.

Extract the name and age from this text: 'The user's name is Alice and she is 30 years old.' with schema {"name": "string", "age": "number"}

{"name": "Alice", "age": 30}

Extract the status from: 'The task is completed: yes.' with schema {"status": "boolean"}

true

Extract the items from: 'Shopping list: - apples, - milk, - bread' with schema {"items": "list"}

["apples", "milk", "bread"]

The tool uses deterministic regex patterns to locate field names (anchors) and extract the subsequent values based on the expected type (string, number, boolean, or list).

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