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andreperez

AnythingLLM MCP Server

by andreperez

anythingllm_upload_link

Scrape a web page from a given URL and upload it as a document to AnythingLLM. Turn online content into searchable documents.

Instructions

Scrape a web page and upload it as a document to AnythingLLM.

Args: link: URL to scrape (must start with http:// or https://)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linkYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations already declare this as a write operation (readOnlyHint=false), non-idempotent, and with external side effects (openWorldHint=true). The description adds that it 'scrapes a web page,' which gives a bit more specific mechanism beyond the annotations, but does not disclose any additional behavioral traits such as workspace targeting, failure behavior, or rate limits.

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 minimal and front-loaded with the core action. The Args section is neatly separated and adds necessary parameter detail without redundancy. Every sentence earns its place.

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?

The description does not specify where the uploaded document is placed (e.g., which workspace or folder), which is a significant ambiguity given sibling tools like upload_file_to_folder. While an output schema exists and annotations cover safety, the missing destination context could confuse an agent about expected effects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides a single 'link' parameter with no description (0% coverage). The description's 'Args' section compensates fully by defining the parameter as 'URL to scrape' and specifying the required protocol prefix. This is exactly what the agent needs to properly invoke the tool.

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 action: 'Scrape a web page and upload it as a document to AnythingLLM.' This is a specific verb+resource combination that immediately differentiates it from sibling tools like upload_file or upload_raw_text by specifying the source (web page).

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 implies a clear use case: when you have a URL to scrape and upload. It also provides a necessary constraint for the link parameter ('must start with http:// or https://'). However, it does not explicitly compare to alternatives like upload_file or upload_raw_text, so it falls short of full guidance.

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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