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Glama

fetch_wechat_article

Reads WeChat article content—main text, image list, publish time—from a URL. Halts on login or verification; pass candidate_id to check the account name, flagging mismatches.

Instructions

联网读取公众号文章正文、配图清单与发布时间。遇验证/登录停止。可提供candidate_id:按账号名核验候选,不一致标为conflicting。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
candidate_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv4.1.3
    • addedInput schema / properties / candidate_id
      Added value: +{
      +  "pattern": "^[a-f0-9]{64}$",
      +  "type": "string"
      +}
  2. First observedv4.1.1

TDQS

A3.5/5.0
Behavior4/5

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

Adds meaningful behavioral context beyond the annotations: it discloses network access, stops when verification/login is encountered, and describes candidate verification with conflicting marking. It does not contradict the annotations, and it gives useful caveats that annotations alone would not convey.

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?

Two concise sentences with the core function front-loaded, followed by the critical failure mode and optional-parameter behavior. No filler or redundant repetition of schema fields.

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?

Covers the main outputs (article body, image list, publish time), the failure mode (verification/login stops), and optional candidate_id behavior. However, it lacks detail on the return structure and how candidate_id is obtained or used in workflow, and there is no output schema to fill that gap.

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?

Schema description coverage is 0%, so the description must compensate. It explains candidate_id semantics reasonably (account-name verification and conflict marking), but the required url parameter has no explicit format or meaning beyond the general 'read article online' context.

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?

States a specific verb and resource: '联网读取公众号文章正文、配图清单与发布时间' (read article body, image list, publish time online). This is clear and distinguishes online fetching from saved/local variants, though it does not explicitly name a sibling alternative.

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

Usage Guidelines2/5

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

No explicit when-to-use guidance or alternative routing. It implies online use and mentions a candidate verification flow, but it never tells the agent when to prefer this tool over read_wechat_post, get_saved_article, or search_wechat_articles.

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