What a website is built and run on, from a single fetch: framework, CMS, ecommerce platform, CDN, analytics, payments, support widgets and web server — each reported with the response header or HTML marker that proves it. Use for competitive research, sales prospecting and vendor due diligence. Price: $0.005
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Site URL to fingerprint |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that the tool makes a single fetch, reports multiple technology types, and provides evidence for each detection. It also discloses pricing. This is meaningful behavioral context beyond the tool name and schema.
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 a single, information-dense sentence listing the detected technologies and evidence, followed by use cases and price. Every element earns its place; 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 simple one-parameter tool with no output schema, the description covers what it does, what it detects, how results are evidenced, when to use it, and pricing. This is complete enough for an agent to invoke 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 input schema already describes the only parameter ('url' as 'Site URL to fingerprint') with 100% coverage. The description adds context about what the URL is used for and the output categories, but does not add parameter-level details beyond 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 clearly identifies the tool's purpose: detecting the technology stack of a website (framework, CMS, CDN, etc.) from a single fetch. It differentiates from sibling tools by listing specific categories and noting the evidence (response header/HTML marker) that proves each detection.
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?
Explicit use cases are provided: competitive research, sales prospecting, and vendor due diligence. It does not explicitly mention when not to use it or name alternatives, but the use-case framing gives clear context for selection.
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.
Several tool clusters have near-overlapping purposes: fetch_webpage/fetch_webpage_pro/fetch_resilient and batch_fetch/get_contents are hard to distinguish, and answer_question/research/deep_research differ mainly in price and depth. The search_* and intel_* families are clearer, but the core fetching and research overlap creates ambiguity.
Most tools follow a verb_noun snake_case pattern (fetch_webpage, search_web, extract_data), but there are notable exceptions like domain_intel, package_intel, youtube_transcript, memory_set, and intel_company, where the prefix/suffix convention is inconsistent. Still, the naming is broadly readable.
35 tools is a large surface, far beyond the typical 3-15 range. The server covers many research verticals, but the number feels bloated, especially with multiple fetch and research variants that could be consolidated.
The tool set covers a wide range of web research needs: searching, fetching, crawling, extracting, screenshots, domain/tech/package intelligence, and market/competitive analysis. It lacks obvious lifecycle operations for monitors (list/delete/update) and memory (get/delete), but core workflows are well covered.