page_metaExtracts OG tags, canonical URL, title, and meta description from any web page. Useful for link previews, SEO audits, and content enrichment. [price: $0.001/call USDC via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Target webpage URL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It adds pricing info ($0.001/call) and lists exactly what fields are extracted, which is useful. However, it does not disclose limitations like failure on non-HTML pages, redirect handling, or that 'any web page' may be over-claiming.
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?
Two sentences plus a pricing note. The core function is front-loaded, followed by use cases, then price—every part serves a purpose 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?
For a single-parameter tool with no output schema, the description covers purpose, expected return fields, use cases, and cost. It misses edge-case behavior (e.g., pages that block bots or non-HTML responses), but the essential facts for calling it correctly are present.
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 description coverage is 100%: the sole parameter 'url' already has a clear description 'Target webpage URL'. The tool description adds no new semantic detail beyond mentioning 'web page', so it neither helps nor hurts beyond the baseline.
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?
States a specific verb 'Extracts' with a clear resource list: OG tags, canonical URL, title, and meta description. This readily distinguishes it from siblings like scrape or link_graph, which handle broader or different content extraction.
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?
Provides explicit use cases: link previews, SEO audits, and content enrichment. It does not name alternative tools or state when not to use it, but the context is clear enough for an agent to select it for meta-data extraction.
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.