outreach-prospector-mcp
Related Servers
Alternatives to outreach-prospector-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceEnables targeted B2B lead discovery, DNS MX deliverability verification, and extraction of web content into clean Markdown for AI agents.MIT
- AlicenseNot gradedqualityBmaintenanceEnables autonomous B2B lead generation by discovering companies, scraping websites for contacts, decoding obfuscated emails, generating email permutations, and verifying email deliverability via MX and SMTP checks.2MIT
- AlicenseNot gradedqualityDmaintenanceEnables B2B prospecting from natural language: detect buying signals, score leads against ICP, enrich decision-makers, and draft personalized outreach messages via Claude.2MIT
- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to automate recruiter workflows by batch-searching candidates, capturing and OCR-ing resume screenshots, scoring and filtering applicants, extracting share links, greeting candidates, and exporting Markdown reports.-
- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to automate recruiter-side hiring workflows by batch-searching candidates, capturing resume screenshots, extracting share links, filtering and scoring profiles, sending greetings, and exporting Markdown reports.-
- AlicenseBqualityCmaintenanceEnables LinkedIn profile research, post analysis, business signal detection, ICP prospect matching, and sales opportunity discovery through natural language.20109 npmMIT
TDQS
Scored across 5 tools
Two overlaps are evident: scrape_clean_markdown explicitly handles ATS/career boards that scan_live_ats_career_ops also targets, and waterfall_find_decision_maker overlaps with prepare_linkedin_outreach_for_approval, which also finds LinkedIn decision-makers. Descriptions clarify intent, but an agent can still misselect when the task is discovery or scraping.
All names use snake_case, but the verb pattern is inconsistent: scrape_clean_markdown and waterfall_find_decision_maker are multi-part or noun-led rather than simple verb_noun. The names remain readable and domain-specific overall.
Five tools is well within the ideal 3-15 range and each maps to a recognizable pipeline stage: scrape, enrich, scan, validate, and prepare. No tool feels redundant enough to remove, and the set is not thin for the apparent scope.
The set covers research, decision-maker discovery, ATS scanning, validation, and LinkedIn outreach preparation, but it lacks email outreach drafting despite validating email payloads, and has no lead/contact storage or status tracking. These are notable gaps for an end-to-end prospecting workflow.