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coolaigit

site-crawler-mcp

by coolaigit

summarize_page

Summarize any webpage's content using configurable LLM or local methods, with automatic fallback for consistent results.

Instructions

概括页面内容。mode: auto(LLM 优先,失败回落本地)/ llm / local。

llm_provider 如 "deepseek/deepseek-chat" 或 "openai/gpt-4o-mini"(LiteLLM 格式); llm_api_key_env 指定 API key 的环境变量名(默认 DEEPSEEK_API_KEY)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
modeNoauto
llm_providerNo
llm_api_key_envNoDEEPSEEK_API_KEY
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the auto mode tries LLM first and falls back to local on failure, and it specifies the llm_provider format and llm_api_key_env default. This adds useful context beyond the schema, though it stops short of explaining error scenarios or output formats.

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 compact and well-structured: it opens with the main purpose, then details modes and provider parameters in a clear, scannable format. Every sentence adds value with no redundancy.

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 4 parameters, no output schema, and no annotations, the description covers the essential functional aspects: purpose, mode behaviors, and provider configuration. It doesn't mention return value or edge cases, but this is not critical for basic invocation and is adequate given the tool's simplicity.

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 has 0% description coverage, so the description's explanations are essential. It defines allowed mode values, provides concrete LiteLLM format examples for llm_provider, and states the default for llm_api_key_env. This compensates well for the schema's lack of 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 '概括页面内容' (summarize page content), identifying a specific verb (summarize) and resource (page). This purpose is distinct from sibling tools like scrape_page or crawl_site, making it easy for an agent to know what this tool does.

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 does not explicitly state when to use this tool instead of alternatives like scrape_page or crawl_site. It does provide mode-specific guidance (auto/llm/local) and parameter details, but lacks explicit when-to-use or when-not-to-use statements relative to sibling tools.

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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