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CrawlForge MCP Server

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JavaScript
Remote

process_document

Read-onlyIdempotent

Extract text from PDF or DOCX files or URLs, returning structured sections, metadata, and word count. Parse contracts, reports, and research papers without manual copying.

Instructions

Use this to extract text from a PDF or DOCX URL or file - research papers, contracts, reports. The body decides how it is read: a PDF or Word document served under sourceType "url" still reaches its parser, and a body this tool cannot read (an image, an archive) is refused by name. Returns structured sections, metadata, and word count. Not for ordinary web pages (scrape), though an HTML URL is accepted. Cost: 2 credits. Example: process_document({source: "https://example.com/report.pdf", sourceType: "pdf_url"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesDocument source - URL or file path
optionsNoAdditional processing options (maxPages, pageRange:{start,end}, extractText, extractMetadata, outputFormat, ...)
redact_piiNoRedact personal data from the text this call returns, before it reaches your context window. true means the free regex pass over EMAIL, PHONE, FINANCIAL and SECRET. The result carries redaction:{entities,count}. Default: off
sourceTypeNoType of document source
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.
max_inline_charsNoLargest result to return inline, in characters of its JSON. Over it, the call returns a preview plus a result_handle for read_result instead of the whole result (default 40,000; env CRAWLFORGE_MAX_INLINE_CHARS)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv6.0.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / max_inline_chars
      Added value: +{
+  "description": "Largest result to return inline, in characters of its JSON. Over it, the call returns a preview plus a result_handle for read_result instead of the whole result (default 40,000; env CRAWLFORGE_MAX_INLINE_CHARS)",
+  "maximum": 10000000,
+  "minimum": 1000,
+  "type": "integer"
+}
    • changedInput schema / properties / options / additionalProperties
      Previous value: -trueNew value: +{}
    • addedInput schema / properties / redact_pii
      Added value: +{
+  "anyOf": [
+    {
+      "type": "boolean"
+    },
+    {
+      "properties": {
+        "entities": {
+          "description": "Which classes to redact, case-insensitive: EMAIL, PHONE, FINANCIAL, SECRET, plus PERSON and LOCATION when mode is \"model\". Omitted or empty means all four regex classes (and both model classes in \"model\" mode). An unknown name, or a model-only name without mode:\"model\", is rejected",
+          "items": {
+            "type": "string"
+          },
+          "type": "array"
+        },
+        "mode": {
+          "description": "\"fast\" (default) is regex only and free; \"model\" adds an Ollama NER pass for PERSON and LOCATION (+3 credits once per call)",
+          "enum": [
+            "fast",
+            "model"
+          ],
+          "type": "string"
+        },
+        "replace_style": {
+          "description": "\"tag\" (default) writes <EMAIL>, \"mask\" writes [REDACTED], \"remove\" deletes the value",
+          "enum": [
+            "tag",
+            "mask",
+            "remove"
+          ],
+          "type": "string"
+        }
+      },
+      "type": "object"
+    }
+  ],
+  "description": "Redact personal data from the text this call returns, before it reaches your context window. true means the free regex pass over EMAIL, PHONE, FINANCIAL and SECRET. The result carries redaction:{entities,count}. Default: off"
+}
  2. Changed2 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"
+}
  3. Changed2 schema fields 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"
    • changedInput schema / properties / options / description
      Previous value: -"Additional processing options (maxPages, pageRange:{start,end}, extractText, extractMetadata, password, outputFormat, ...)"New value: +"Additional processing options (maxPages, pageRange:{start,end}, extractText, extractMetadata, outputFormat, ...)"
  4. First observedv4.10.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description does not contradict them. Beyond that, it discloses cost (2 credits), the refusal of unsupported body types ('refused by name'), the fact that a PDF under sourceType 'url' still reaches its parser, and the return contents (structured sections, metadata, word count). This is substantial additional behavioral context that aids the agent in understanding side effects and edge cases.

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?

The description is front-loaded with the primary use case in the first sentence, then adds supporting details (edge cases, alternative, cost, example). It is slightly verbose but every sentence carries unique information. The structure is logical and it remains readable without being padded.

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 moderate complexity (7 params, nested object, enum, cost, redaction behavior), the description covers the key decision points: what it does, what it doesn't accept, an example, and the output shape. The output schema is not provided, but the description mentions return fields. It lacks explicit details on all option combinations, but the schema descriptions fill that gap. It is complete enough for an agent to call it correctly.

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% for all 7 parameters, so the structured descriptions are comprehensive. The description adds value by providing an illustrative example (source + sourceType), clarifying the nuance that sourceType 'url' can still process PDFs, and implying the meaning of source and sourceType through the example. While it doesn't detail options like maxPages or redact_pii (handled in schema), this is above the baseline for high-coverage schemas.

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 ('extract text') and resource ('PDF or DOCX URL or file'), names the target document types, and gives an example call. It explicitly distinguishes itself from web scraping ('Not for ordinary web pages (scrape)') and clarifies an edge case (HTML URL accepted but handled differently). This is precise and actionable.

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?

The description provides clear context for when to use this tool ('research papers, contracts, reports') and explicitly excludes ordinary web pages ('Not for ordinary web pages (scrape)'). It also gives a concrete usage example. It does not mention alternative tools by name (e.g., extract_text) but the exclusion and example are sufficient for an agent to select it correctly in most cases.

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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