Bulk-enrich a list of company domains with emails, contact pages, social profiles, and (where available) phone numbers.
Wraps nexgendata/lead-list-enricher. Best used after find_b2b_leads
to add contact-tier data to a freshly built lead list. Up to ~50
domains per call recommended for response-time reasons.
Args: domains: List of company domains (e.g. ["stripe.com","airbnb.com"]).
| Name | Required | Description | Default |
|---|---|---|---|
| domains | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, so the description's mention of enriching is consistent. It adds value by naming the external service and batch size recommendation, but doesn't discuss data freshness, missing domains, or rate limits.
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?
Extremely concise: three sentences plus a single bullet for the argument. Front-loaded with purpose, no wasted words. Perfect structure for quick parsing.
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 description gives a high-level overview of returned data types but lacks detailed output structure (e.g., exact fields per domain, nesting). With no output schema, more specificity would help agents use the data effectively.
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?
With 0% schema description coverage, the description compensates by explaining the 'domains' parameter as a list of company domains with examples. However, it could add more detail on format constraints or error handling.
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 the tool bulk-enriches a list of company domains with specific data types (emails, contact pages, social profiles, phone numbers). It uses a strong verb and resource combination and distinguishes from siblings like enrich_company and find_b2b_leads.
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?
Explicitly recommends use after find_b2b_leads and advises a batch size of ~50 domains for response time. This provides clear context, though it lacks explicit when-not-to-use scenarios.
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.
Each tool targets a distinct sales intelligence function: from company profiling, tech detection, hiring signals, to lead finding and enrichment. Even the two enrichment tools (enrich_company vs. aggregate_company_profile) are clearly differentiated by depth and features.
All tools follow a consistent verb_noun snake_case pattern (e.g., aggregate_company_profile, detect_tech_stack, find_b2b_leads). No mixing of conventions or vague verbs.
10 tools is well-scoped for a sales intelligence server, covering the full pipeline from prospecting to enrichment and signal detection. It's neither too sparse nor overwhelming.
The tool set covers end-to-end sales research: lead generation, company enrichment, tech stack detection, hiring signals, email finding, YC directory, job search, and funding tracking. No obvious gaps for the intended domain.