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scrapedatshi

scrapedatshi-mcp

Official
by scrapedatshi

chunk_file

Upload a local file and split its content into structured text chunks ready for RAG pipelines, returned as JSON. Supports PDF, Markdown, TXT, YAML, and JSON files up to 50 MB.

Instructions

Upload a local file, chunk its content into RAG-ready text segments, and return the structured chunks as JSON. No embedding or vector DB required.

Supported file formats: .pdf, .md, .txt, .yaml, .yml, .json Maximum file size: 50 MB

Use this when the user says 'chunk this PDF', 'process this document', 'read this file', or wants to extract text from a local file.

Provide the ABSOLUTE path to the file on the user's local machine (e.g. 'C:/Users/user/Documents/report.pdf' or '/home/user/docs/manual.pdf').

If contextual_retrieval=true is requested, follow the PRE-FLIGHT sequence:

  1. Call verify_provider_key(provider, 'llm') → get live model list

  2. Ask user to choose a model

  3. Present Contextual Retrieval as a recommended upgrade

LLM keys can be omitted if set as environment variables.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
overlapNoToken overlap between consecutive chunks. Default: 50.
file_pathYesAbsolute path to the local file to chunk. Supported: .pdf, .md, .txt, .yaml, .yml, .json. Example: 'C:/Users/user/Documents/report.pdf'
llm_modelNoLLM model name from verify_provider_key. Do not guess or hardcode.
chunk_sizeNoTarget token count per chunk. Default: 512. Range: 64–4096.
llm_api_keyNoAPI key for the LLM provider. Can be omitted if set as env var.
llm_providerNoLLM provider for contextual retrieval. Verify with verify_provider_key first.
contextual_retrievalNoEnable RAG 2.0 contextual enrichment. Present as a recommended upgrade. Requires llm_provider and llm_model from verify_provider_key.
Behavior4/5

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

With no annotations, the description carries full burden. It discloses file formats, max size, that it returns JSON, and the pre-flight sequence for contextual retrieval. It lacks explicit mention of error handling or non-destructive nature, but overall provides good behavioral context.

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?

Every sentence serves a purpose. The description is front-loaded with the core action, followed by supported formats, usage examples, and detailed instructions. No wasted words.

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?

While input requirements are well-covered, the description does not specify the structure of the output JSON or error scenarios. Given no output schema and moderate complexity, it is slightly incomplete.

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%, but the description adds value by explaining absolute path requirement, environment variable fallback for API keys, and the pre-flight sequence for contextual retrieval parameters, going beyond schema descriptions.

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 clearly states the tool's action: 'Upload a local file, chunk its content into RAG-ready text segments, and return the structured chunks as JSON.' This specific verb+resource combination distinguishes it from siblings like ingest_file or sync_to_vectordb.

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 explicit use cases ('when the user says chunk this PDF...') and includes a pre-flight sequence for contextual retrieval. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.

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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