structured-extract-mcp
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Alternatives to structured-extract-mcp
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Related Servers
- FlicenseNot gradedqualityBmaintenanceEnables agents to deterministically extract structured entities from input payloads and validate or coerce them against JSON schemas, returning a standardized response dossier with execution telemetry. It runs as a zero-dependency MCP server that plugs into Claude Desktop, Cursor, and other MCP clients.8-
- FlicenseNot gradedqualityBmaintenanceEnables deterministic extraction and validation of structured JSON data from inputs using a defined schema. It plugs into MCP-compatible clients like Claude Desktop, Cursor, and Windsurf to return standardized, schema-validated results.7-
- AlicenseNot gradedqualityBmaintenanceHosted MCP server for structured knowledge extraction and entity enrichment, allowing users to author JSON schemas, enrich entities using multiple LLM providers, and benchmark model performance.MIT
- FlicenseNot gradedqualityBmaintenanceEnables agents and MCP clients to deterministically extract and repair JSON/schema issues such as truncated braces, markdown wrappers, and invalid types without LLM round-trips.7-
- FlicenseNot gradedqualityBmaintenanceEnables MCP-compatible agents and IDEs to deterministically extract JSON and auto-repair malformed schemas, including truncated braces, markdown wrappers, and invalid types, without LLM round-trips.7-
- AlicenseNot gradedqualityBmaintenanceEnables agents to ask typed questions about text or JSON and receive structured answers, with tools for single requests, batch processing, model listing, and offline validation.11 npmMIT
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: list_schemas discovers available schemas and extract performs the extraction. There is no overlap or ambiguity between them.
Both names are lowercase snake_case, but one follows verb_noun (list_schemas) while the other is a bare verb (extract). This is a minor deviation within an otherwise consistent convention.
Only 2 tools makes the surface quite thin, though each one earns its place for a focused extraction service. It sits at the borderline of being under-scoped.
The core workflow (discover schemas, extract data) is covered, but there is no way to inspect a schema's fields or validate text without extracting, leaving users partly blind to the expected structure.