captcha_evalClassifies captcha type (reCAPTCHA, hCaptcha, Turnstile) and difficulty. Eval only — we do NOT solve captchas. [price: $0.01/call USDC via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Captcha type: recaptcha, hcaptcha, turnstile, image, slider, puzzle |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. It does add useful context: this is an 'Eval only' operation and explicitly states it does not solve captchas, plus a price note. However, it does not disclose the output shape, how the optional 'type' parameter affects the call, or any relevant failure/edge-case behavior, so transparency is incomplete.
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 two compact sentences, front-loaded with the core purpose and followed by a clear non-solving and price note. Every sentence earns its place and there is no redundant or fluff content.
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?
The tool has no output schema and no annotations, so the description is the only behavioral contract. It never explains what a successful call returns, how to actually supply a captcha for eval, or what the optional 'type' parameter does in the invocation flow. This leaves significant ambiguity for an agent trying to call it correctly.
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?
The schema covers the single 'type' parameter with a description listing allowed values, so schema coverage is 100%. The tool description adds the concept of difficulty but does not clarify whether 'type' is an input to classify, a filter, or an output hint. Thus the description provides only marginal meaning beyond the schema.
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 states a specific verb and resource: 'Classifies captcha type (reCAPTCHA, hCaptcha, Turnstile) and difficulty.' It also adds a clear boundary with 'Eval only — we do NOT solve captchas,' distinguishing it from any solving-related alternatives. No sibling tool has overlap, so purpose ambiguity is low.
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 implies when to use the tool — when captcha type/difficulty classification is needed — and explicitly says not to use it for solving. However, it does not name alternative tools or give explicit 'when to use vs not use' guidance beyond the non-solving exclusion. This is adequate but not strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but several clusters overlap: domain_facts, page_meta, and scrape all return page title information, and search_verify, hallucination_check, and sweep all target claim validation. The descriptions usually clarify the use case, but the boundaries are not always obvious.
All names use lowercase snake_case, so there is a baseline consistency, but the pattern is mixed: bare verbs like scrape, summarize, and sweep sit alongside noun+noun forms like domain_facts and noun+verb forms like entity_find. The names are readable but do not form a predictable verb_noun API convention.
At 26 tools, this is heavy and above the typical well-scoped 3-15 range, though the server is explicitly positioned as a broad shelf of paid utilities. Many tools are small one-purpose endpoints, so the count feels more like a catalog than a focused suite, but it is not an extreme mismatch.
The shelf covers the major advertised areas: web page analysis, research verification, text guards and NLP, blockchain reads, and image generation. There are some gaps such as no web search and no transaction sending, but agents can typically work around them or pair this with another server.