Skip to main content
Glama

add_webpage

Fetches a webpage, extracts its body text, and splits it into indexed chunks stored as shared knowledge for all clients.

Instructions

抓取网页正文并切分入库;返回 {"source": url, "chunks": 块数}; 抓取/正文提取失败时返回 {"error": "WEB_FETCH_FAILED", "message": 原因}。 project/client:仅用于审计归类;web chunk 为共享知识,所有客户端可检索。 client:来源客户端(可选,缺省从 clientInfo 自动识别)。 English: Fetch a webpage body and ingest it after splitting; returns {"source": url, "chunks": count}; on fetch/body-extraction failure returns {"error": "WEB_FETCH_FAILED", "message": reason}. project/client: audit bucketing only; web chunks are shared knowledge, searchable by all clients.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
clientNo
projectNo
Install Server

TDQS

A3.8/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It clearly specifies success return shape, failure error code, project/client's audit-only role, and that web chunks are shared knowledge searchable by all clients, while also noting client auto-detection.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The main action and return behavior are front-loaded and the structure is logical, but the content is duplicated across Chinese and English sections, making it longer than strictly necessary. Each sentence does not fully earn its place because of the near-verbatim repetition.

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 no annotations and no output schema, the description provides crucial invocation details: success/error returns, parameter roles, and sharing semantics. Minor gaps remain around url parameter documentation and any size/format constraints, but the core usage context is sufficiently complete 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.

Parameters3/5

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

The schema has 0% description coverage, so the description must compensate. It explains that client is optional and defaults from clientInfo, and that project/client are audit-only, but it does not describe the url format or project individually beyond the grouped statement.

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 action ('抓取网页正文并切分入库' / 'Fetch a webpage body and ingest it after splitting') with a clear resource: a webpage. This distinguishes it from siblings like add_document and write_memory, which handle documents and memory respectively.

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 implies the tool is for ingesting web pages into shared knowledge, and clarifies that project/client are only for audit bucketing. However, it does not explicitly compare against alternatives like add_document, nor state when one should choose this tool over them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/fish827-08/rag-kb'

If you have feedback or need assistance with the MCP directory API, please join our Discord server