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Glama

add_webpage

Fetches a webpage, extracts its body, splits it into chunks, and stores them in a knowledge base for retrieval. Returns source URL and chunk count, or an error if fetch fails.

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:web chunk 落入该项目子库(空=全局库);client:仅用于审计归类。 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: chunks go to that project's sub-store (empty = global); client is audit-only.

Input Schema

NameRequiredDescriptionDefault
urlYes
clientNo
projectNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does reasonably well: it discloses both the success return shape ({source, chunks}) and the failure shape including the WEB_FETCH_FAILED error code and a reason message. It also explains the storage scoping rule (project sub-store vs global). It stops short of auth requirements, rate limits, or duplicate-URL behavior.

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 content is front-loaded and each piece of information is useful, but the entire description is duplicated verbatim in Chinese and English, which doubles the token cost without adding information for a reader who understands either language.

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?

There is no output schema, so the description's disclosure of the return and error shapes is exactly what is needed. For a simple 3-parameter ingestion tool with no annotations, the description covers purpose, parameters, and outcomes adequately; only operational details like deduplication or size limits are absent.

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 0%, so the description must compensate, and it does for the non-obvious parameters: 'project' is explained as the destination sub-store with empty meaning global, and 'client' is explicitly flagged as audit-only metadata. 'url' is self-evident. This adds real meaning beyond the echoed titles in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a concrete verb chain and resource: fetch a webpage body, split it, and ingest it into the store. That is far more specific than the bare name, but it never differentiates itself from the sibling add_document (file-based ingestion) or write_memory, so an agent must infer which ingestion tool to pick.

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?

Usage is only implied by the phrase 'fetch a webpage body and ingest it' — an agent can infer this is for pulling web content into the knowledge base. There is no statement of when to prefer add_document over this tool, no prerequisites, and no exclusions (e.g. unsupported schemes or already-ingested URLs).

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