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RapierCraft

alterlab-mcp-server

by RapierCraft

alterlab_extract

Extract structured data from raw HTML, text, or markdown content using predefined extraction profiles or natural language prompts. Returns JSON with extracted fields like price, title, author, and more.

Instructions

Extract structured data from raw HTML, text, or markdown content WITHOUT scraping. Bring your own pre-fetched content. Use this when you already have the page content and want to run AlterLab's extraction pipeline on it. For scraping + extraction in one step, use alterlab_scrape with formats=['json'] instead. Profiles: 'product' (price, title, reviews), 'article' (title, author, body), 'job_posting', 'faq', 'recipe', 'event', 'ecommerce_homepage', 'directory_listing'. Returns JSON data. Use extraction_prompt for natural language extraction (LLM-powered). Use cache='only' to retrieve a previously cached result without calling the LLM.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesRaw content to extract from — HTML, text, or markdown. Bring your own pre-fetched content; this endpoint does NOT scrape a URL.
content_typeNoType of the provided contenthtml
extraction_profileNoPre-defined extraction profile. 'product' extracts price/title/reviews, 'article' extracts title/author/body, etc. 'auto' detects the page type. Mutually exclusive with extraction_template.
extraction_templateNoShorthand alias for extraction_profile — selects the same pre-built schema template. Mutually exclusive with extraction_profile.
extraction_schemaNoCustom JSON Schema for extraction. Fields are mapped from content. Overrides extraction_profile when provided
extraction_promptNoNatural language instructions for LLM extraction (e.g., 'Extract all product prices and ratings'). Charged at LLM extraction rate when provided.
extraction_modelNoPer-request LLM model override in provider-specific format (e.g. 'gpt-4o', 'claude-opus-4-5-20251101', 'llama3-70b-8192'). Overrides the model saved in your BYOK key settings for this request only.
extraction_providerNoLLM provider to use for extraction. Selects the matching BYOK key registered at /dashboard/settings/llm-keys. When omitted, the most recently used registered key is used.
formatsNoOutput formats for content transformation. 'json' is best for structured extraction. 'content' returns filtered/cleaned content. 'raw' returns the unprocessed response body.
source_urlNoOriginal URL of the content (for context only — not fetched). Helps the extractor understand the content's domain.
evidenceNoInclude field provenance/evidence for extracted fields (which part of the content each field came from)
cacheNoCache control for LLM extraction results. 'auto': return cached result if available (default). 'skip': bypass cache lookup, always call LLM (result is still stored). 'only': return cached result or 404 if not cached — never calls the LLM.auto
cache_ttlNoTTL for caching this extraction result, in seconds. Defaults to server setting (3600s). Max 86400s (24 hours).
Behavior5/5

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

No annotations provided, but description extensively discloses behaviors: no scraping, cache behavior, LLM extraction with extraction_prompt, model/provider override, source_url not fetched, evidence inclusion, cache TTL. Very transparent.

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?

Single paragraph, front-loaded with core purpose. Lists profiles and features efficiently. Slightly long but each sentence adds value.

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?

No output schema, so description should detail return format. Only says 'Returns JSON data' and mentions evidence flag. Lacks specifics on output structure (e.g., extraction fields, error formats). Adequate but not complete.

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 coverage is 100%, baseline 3. Description adds meaningful extra context like profile descriptions (e.g., product extracts price/title/reviews) and alias relationship between extraction_template and extraction_profile.

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?

Clearly states it extracts structured data from raw content without scraping. Distinguishes from alterlab_scrape and lists profiles. Verb+resource+scope specific.

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

Usage Guidelines5/5

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

Explicitly says when to use (already have content, want extraction pipeline) and when to use alterlab_scrape instead. Also explains cache='only' for cached results.

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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