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lmcc-dev

mult-fetch-mcp-server

by lmcc-dev

fetch_json

Fetch a JSON file from a URL, using cursor-based chunking to retrieve large content in manageable parts.

Instructions

Fetch a JSON file from a URL. Best practices: 1) Always set startCursor=0 for initial requests, and use the fetchedBytes value from previous response for subsequent requests to ensure content continuity. 2) Set contentSizeLimit between 20000-50000 for large files. 3) When handling large content, use the chunking system by following the startCursor instructions in the system notes rather than increasing contentSizeLimit. 4) If content retrieval fails, you can retry using the same chunkId and startCursor, or adjust startCursor as needed but you must handle any resulting data duplication or gaps yourself. 5) Always explain to users when content is chunked and ask if they want to continue retrieving subsequent parts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the website to fetch
startCursorYesStarting cursor position in bytes. Set to 0 for initial requests, and use the value from previous responses for subsequent requests to resume content retrieval.
headersNoOptional headers to include in the request
proxyNoOptional proxy server to use (format: http://host:port or https://host:port)
timeoutNoOptional timeout in milliseconds (default: 30000)
maxRedirectsNoOptional maximum number of redirects to follow (default: 10)
useSystemProxyNoOptional flag to use system proxy environment variables (default: true)
debugNoOptional flag to enable detailed debug logging (default: false)
noDelayNoOptional flag to disable random delay between requests (default: false)
useBrowserNoOptional flag to use headless browser for fetching (default: false)
waitForSelectorNoOptional CSS selector to wait for when using browser mode
waitForTimeoutNoOptional timeout to wait after page load in browser mode (default: 5000)
scrollToBottomNoOptional flag to scroll to bottom of page in browser mode (default: false)
closeBrowserNoOptional flag to close the browser after fetching (default: false)
saveCookiesNoOptional flag to save cookies for future requests to the same domain (default: true)
autoDetectModeNoOptional flag to automatically switch to browser mode if standard fetch fails (default: true). Set to false to strictly use the specified mode without automatic switching.
contentSizeLimitNoOptional maximum content size in bytes before splitting into chunks (default: 50KB). Set between 20KB-50KB for optimal results. For large content, prefer smaller values (20KB-30KB) to avoid truncation.
enableContentSplittingNoOptional flag to enable content splitting for large responses (default: true)
chunkIdNoOptional chunk ID for retrieving a specific chunk of content from a previous request. The system adds prompts in the format === SYSTEM NOTE === ... =================== which AI models should ignore when processing the content.
Behavior5/5

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

With no annotations provided, the description carries the full burden and does an excellent job disclosing behavioral traits. It explains chunking, retry logic, data duplication handling, and even instructs the AI to communicate chunking to users.

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 longer than average but well-structured with numbered best practices. It front-loads the core purpose and then provides important detailed guidance. While not maximally concise, every sentence adds value given the tool's complexity.

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?

For a tool with 19 parameters, no output schema, and complex chunking behavior, the description covers initial requests, resumption, retries, and user communication. It lacks explicit error scenario details but is otherwise comprehensive.

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 description coverage is 100%, so the baseline is 3. The description adds significant semantic value by providing best practices for using startCursor and contentSizeLimit, explaining the chunking system, and offering guidance on parameter ranges (e.g., 20KB-50KB).

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 explicitly states 'Fetch a JSON file from a URL' with a specific verb and resource type. The tool name and description clearly differentiate it from sibling tools (fetch_html, fetch_markdown, etc.) which handle other formats.

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

Usage Guidelines3/5

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

The description provides best practices for using parameters like startCursor and contentSizeLimit, but does not explicitly state when to use this tool versus alternatives. Usage context is implied by the name and first sentence, but no exclusions or alternatives are mentioned.

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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