Enrich a company domain. Returns company name, description, detected tech stack, social media links, DNS records, platform detection (WordPress/Shopify/etc.), and contact page links. Useful for lead scoring and prospect research.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Company domain (e.g. stripe.com or https://stripe.com) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description lists the output data but omits details on rate limits, authentication, or error handling. It is transparent about the read-only nature but lacks depth on behavioral constraints.
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, no fluff. The first sentence states action and outputs, the second provides context. Every word earns its place.
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 tool with one parameter and no output schema, the description covers expected outputs and use case. It could mention validation or failure behavior, but overall it is sufficient.
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 already has a description for the single parameter, but the description adds 'e.g. stripe.com or https://stripe.com' which clarifies acceptable formats. This extra example adds value 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 clearly states 'Enrich a company domain' and enumerates specific returned data (e.g., company name, tech stack). It effectively distinguishes this comprehensive tool from more focused siblings like detect_stack or dns_records_lookup.
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 mentions usefulness for lead scoring and prospect research, implying when to use it. However, it does not explicitly state when not to use or mention alternative tools for specific data points.
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.
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.