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CrawlForge MCP Server

by mysleekdesigns
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JavaScript
Remote

extract_with_llm

Read-only

Extract data from a URL or text using a natural-language prompt. Defaults to a local Ollama model (http://localhost:11434, no API key required) and picks an installed model itself; pass model to choose one, or provider: "openai" or…

Instructions

Extract data from a URL or text using a natural-language prompt. Defaults to a local Ollama model (http://localhost:11434, no API key required) and picks an installed model itself; pass model to choose one, or provider: "openai" or "anthropic" with the matching API key to use a cloud model instead. Not for known selectors (scrape_structured) or a schema-shaped result (extract_structured). Call list_ollama_models only when a model name is rejected. Asking again for the same page with different formats, paths or selectors reuses this session's copy for 10 minutes at 1 credit; pass maxAge: 0 to force a fresh fetch. Cost: 3 credits plus your LLM provider's own charge.

Input Schema

NameRequiredDescriptionDefault
urlNoURL to fetch and extract from (one of url/content required)
modelNoOverride the model. For ollama, pass a name returned by list_ollama_models (e.g. 'llama3.2', 'qwen2.5:7b'). Defaults: openai='gpt-4o-mini', anthropic='claude-haiku-4-5-20251001', ollama=$OLLAMA_DEFAULT_MODEL, else the best installed model for extraction (gemma3:12b, then gemma3:4b, gpt-oss:20b, mistral:7b, llama3.2, qwen2.5:3b).
maxAgeNoReuse this session's copy of the page if it was fetched within this many ms (default 600000 = 10 min); 0 always fetches fresh.
promptYesNatural-language extraction instruction
schemaNoOptional JSON-schema for output shape (used as Ollama structured-outputs format when provider is 'ollama')
contentNoPre-fetched text to extract from (one of url/content required)
providerNoLLM provider. Defaults to 'ollama' (local, no key, http://localhost:11434). Use 'openai' or 'anthropic' for cloud models (requires the matching API key).auto
maxTokensNoMaximum output tokens
user_agentNoOverride the outbound User-Agent. CrawlForge identifies itself honestly by default; use this only for targets you have your own agreement with.
respect_robotsNoRespect the target site's robots.txt (default: true). Setting this to false is honoured, returns a warning in the response, and is recorded against your API key — it is your decision, not a silent default.
verify_numbersNoNumeric provenance guard (default: true): every price or numeric value the LLM returns must appear literally in the page source, else it is returned as null with a reason in `provenance.unverified`. Set false to get the model's raw numbers back, including ones it derived (a count, a sum, a total) rather than read off the page.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv6.24.0
    • addedInput schema / properties / maxAge
      Added value: +{
+  "description": "Reuse this session's copy of the page if it was fetched within this many ms (default 600000 = 10 min); 0 always fetches fresh.",
+  "maximum": 9007199254740991,
+  "minimum": 0,
+  "type": "integer"
+}
  2. Changed1 schema field changedv6.19.2
    • changedInput schema / properties / model / description
      Previous value: -"Override the model. For ollama, pass a name returned by list_ollama_models (e.g. 'llama3.2', 'qwen2.5:7b'). Defaults: openai='gpt-4o-mini', anthropic='claude-haiku-4-5-20251001', ollama='llama3.2' or $OLLAMA_DEFAULT_MODEL."New value: +"Override the model. For ollama, pass a name returned by list_ollama_models (e.g. 'llama3.2', 'qwen2.5:7b'). Defaults: openai='gpt-4o-mini', anthropic='claude-haiku-4-5-20251001', ollama=$OLLAMA_DEFAULT_MODEL, else the best installed model for extraction (gemma3:12b, then gemma3:4b, gpt-oss:20b, mistral:7b, llama3.2, qwen2.5:3b)."
  3. Changed2 schema fields changedv6.0.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / schema / propertyNames
      Added value: +{
+  "type": "string"
+}
  4. Changed3 schema fields changedv5.4.0
    • addedInput schema / properties / respect_robots
      Added value: +{
+  "description": "Respect the target site's robots.txt (default: true). Setting this to false is honoured, returns a warning in the response, and is recorded against your API key — it is your decision, not a silent default.",
+  "type": "boolean"
+}
    • addedInput schema / properties / user_agent
      Added value: +{
+  "description": "Override the outbound User-Agent. CrawlForge identifies itself honestly by default; use this only for targets you have your own agreement with.",
+  "type": "string"
+}
    • addedInput schema / properties / verify_numbers
      Added value: +{
+  "default": true,
+  "description": "Numeric provenance guard (default: true): every price or numeric value the LLM returns must appear literally in the page source, else it is returned as null with a reason in `provenance.unverified`. Set false to get the model's raw numbers back, including ones it derived (a count, a sum, a total) rather than read off the page.",
+  "type": "boolean"
+}
  5. Changed1 schema field changedv5.0.4
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  6. First observedv4.10.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already establish read-only, open-world, non-destructive behavior, and the description adds useful context beyond them: local Ollama defaults require no API key, cloud providers need matching keys, callers pay 3 credits plus provider charges, and cached page reuse costs 1 credit for 10 minutes unless maxAge is 0. These details materially improve invocation planning without contradicting the annotations.

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?

The description is front-loaded with the core purpose, then layers in provider defaults, exclusions, model fallback, caching, and cost. Despite its length, each sentence carries operational information for a complex 11-parameter tool.

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?

Given the tool's complexity, rich schema, and annotations, the description covers the critical invocation context: alternatives, provider setup, model defaults, caching, and cost. It does not describe the returned extraction shape or provenance/error behavior, which leaves a modest gap because there is no output schema.

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%, so the schema already documents all 11 parameters in detail. The description still adds useful semantic context for model/provider selection and the cost/caching implications of maxAge, though it leaves several behavioral controls such as verify_numbers and respect_robots to 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 states a specific verb and resource: extract data from a URL or text using a natural-language prompt. It explicitly distinguishes this tool from scrape_structured and extract_structured, so an agent can identify its role among siblings.

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?

It names when not to use this tool and which alternatives to use instead: scrape_structured for known selectors and extract_structured for schema-shaped results. It also gives a clear conditional path for list_ollama_models and explains provider/model selection.

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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