Clay

@Clay find 20 Series A SaaS companies in Austin with 10-50 employees that raised funding in the last 12 months

Clay returned the first page of 20 companies matching your criteria. Example results include three HR-tech startups (Workforce Labs, PeopleSync, TalentGrid) and two dev-tool companies (CodeFlow, APIHub). Each record shows company name, employee count, last funding date, and LinkedIn URL. The search consumed 20 credits from your Clay quota and flagged has_more: true, meaning additional pages are available if you call Continue Search.

This example highlights Clay's advanced company filtering—combining firmographics, funding signals, and geography. Before running similar searches, confirm your workspace has sufficient search credits; each result consumes one credit, and broad queries can exhaust quota quickly. The MCP returns only the first page; you must explicitly request continuation.

@Clay run the 'Enrich with LinkedIn profile' routine on these five candidate email addresses and return job titles plus years of experience

Clay queued the enrichment routine for all five emails. After polling for 45 seconds, four profiles completed: two Senior Engineers (6 and 9 years experience), one Product Manager (4 years), and one Director of Sales (12 years). The fifth email returned no LinkedIn match. The run consumed 4 enrichment credits. Clay stored the full profile data in your workspace; this response includes only the requested title and tenure fields.

Routine execution is asynchronous and credit-intensive—each enriched record costs credits based on the data provider. The MCP polls for a limited time; long-running routines may return a run ID you must check later with Get Routine Run Results. Side-effect: enriched data persists in your Clay workspace and counts toward storage limits.

@Clay search for VP-level marketing contacts at companies we tagged 'Q1 outreach' in our CRM, then draft personalized intro emails referencing their recent LinkedIn posts

Clay found 14 VP Marketing contacts across your tagged account list. The MCP returned names, current companies, LinkedIn URLs, and recent post summaries (e.g., 'Jane Doe at Acme Corp posted about ABM strategy shifts last week'). Using that context, the AI drafted 14 personalized email openers—each references the contact's specific post topic and ties it to your product's value prop. Clay consumed 14 search credits; the AI synthesis required no additional Clay quota.

This example chains Clay's search precision with the AI's reasoning: Clay surfaces the structured contact data and social signals, then the LLM weaves them into outreach copy. The search respects your workspace's CRM tag filters, but verify tag accuracy before running—incorrect tags will yield irrelevant contacts and waste credits.

When Clay beats manual prospecting for a 2-person GTM team

A seed-stage B2B SaaS with one founder and one SDR needs 200 qualified leads per week but can't afford a full sales-ops hire. Clay's Search People or Companies tool filters by firmographics and job title in a single call, then Run Routine enriches each match with email and LinkedIn in batch—no CSV round-trips. The 6-tool surface is narrow enough that the SDR learns it in one afternoon, and API_KEY auth means no OAuth dance when the founder wants to prototype a new ICP filter in Switchy. If your list criteria change daily or you need more than 500 records per search, Clay's quota burns fast and you'll want a dedicated ops person managing credit spend. For early-stage teams running one or two stable ICPs, this MCP turns list-building from a half-day Sheets exercise into a 10-minute Switchy prompt.

Clay for mid-deal intelligence gathering at AE handoff

A 6-person sales team qualifies inbound leads in HubSpot, but AEs waste an hour per demo researching the prospect's org chart and recent funding. The AE opens Switchy during the SDR → AE handoff call, pastes the company domain, and asks Clay's Search People tool for all C-level contacts, then calls Run Routine to pull each exec's latest LinkedIn headline and company news mentions. Get Routine Run Results polls until enrichment finishes—usually under 90 seconds—so the AE walks into the demo with a live org map and three conversation hooks. This works when you're qualifying 10–30 accounts per week; above that volume you want Clay's native UI or a dedicated rev-ops workflow because the per-call quota cost adds up. For deal-by-deal research where speed beats scale, this MCP keeps AEs in one workspace instead of toggling between Switchy, LinkedIn, and a separate Clay tab.

When Clay's routine runner fits quarterly upsell planning

A 4-person customer-success team at a Series A company reviews expansion opportunities each quarter: which accounts hired new stakeholders, which grew headcount above 100, which raised a Series B. The CS lead drops 40 customer domains into Switchy, calls Search People or Companies with a headcount-change filter, then runs Get Routine Run Results to pull hiring velocity and funding events for each match. Clay's Get Search Query Reference tool surfaces the exact filter syntax so the CS lead doesn't guess field names, and Continue Search pages through results when one account returns 50+ new hires. This scenario works because it's low-frequency (quarterly) and the result set is small (under 100 accounts). If you're tracking expansion signals weekly or across 500+ customers, Clay's search quota becomes a bottleneck and you want a scheduled workflow in Clay's UI or a CDP integration. For periodic, high-touch CSM planning, this MCP turns a two-day spreadsheet project into a 20-minute Switchy session.