解一个验证码或人机挑战,返回可直接使用的解。¥0.001–0.033/次,按成功计费,解不出来一律退款。遇到目标站被 Turnstile / reCAPTCHA / hCaptcha / Cloudflare 挑战页 / GeeTest 等挡住时用它。参数逐类型不同,先用 everyinfra_list_captcha_types 确认。
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | 验证码类型,如 turnstile / recaptcha_v2 / image | |
| params | No | 该类型的参数,如 {"website_url":"https://…","website_key":"0x…"}。⚠ 有几类要你自带代理(proxy):它们交付的是 Cookie 而 Cookie 绑定求解时的出口 IP —— 用我们的出口解出来,你拿去用是无效的 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a non-read, potentially destructive/side-effect operation. The description adds meaningful behavioral context beyond that: it is paid per success, refunds on failure, and warns that some captcha types require the user to supply a proxy because the returned cookies bind to the solving IP. This is exactly the kind of practical behavioral disclosure an agent needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core purpose and pricing, then gives usage context and a pointer to the sibling tool. Every sentence earns its place with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the large enum of captcha types, no output schema, and nested params, the description covers the key operational facts: what it returns, cost model, when to use it, and how to discover type-specific parameters. It could mention the return format or latency in more detail, but the pointer to list_captcha_types reasonably fills the main gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds value by clarifying that parameters vary by type and advising the agent to check everyinfra_list_captcha_types before selecting parameters. This helps an agent understand that the generic 'params' object is intentionally type-specific.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action (solve a captcha or human challenge) and the resource (captcha/challenge), and explains it returns a directly usable solution. It differentiates itself from sibling tools like everyinfra_list_captcha_types by being the actual solving tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use it: when a target site is blocked by Turnstile, reCAPTCHA, hCaptcha, Cloudflare, GeeTest, etc. It also instructs to first use everyinfra_list_captcha_types to confirm parameters. It does not spell out negative cases or alternatives, but the use context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.