gitlab-mcp
A Model Context Protocol (MCP) server for GitLab
Crawler Summary
Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. --- name: real-estate-lead-gen description: Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. --- Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Freshness
Last checked 4/15/2026
Best For
real-estate-lead-gen is best for listing, multiple workflows where MCP compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack
Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. --- name: real-estate-lead-gen description: Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. ---
Public facts
4
Change events
1
Artifacts
0
Freshness
Apr 15, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Trust score
Unknown
Compatibility
MCP
Freshness
Apr 15, 2026
Vendor
Estherperdana7
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.
Setup snapshot
git clone https://github.com/estherperdana7/real-estate-lead-gen.gitSetup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Estherperdana7
Protocol compatibility
MCP
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
5
Snippets
0
Languages
typescript
Parameters
bash
mkdir -p ~/lead_gen_pipeline
text
1. create_product: name="Real Estate Lead Intelligence — {METRO}"
2. create_price: Starter $149/mo recurring monthly
3. create_price: Starter $1,490/yr recurring yearly
4. create_price: Professional $299/mo recurring monthly
5. create_price: Professional $2,990/yr recurring yearly
6. create_price: Enterprise $599/mo recurring monthly
7. create_price: Enterprise $5,990/yr recurring yearly
8. create_payment_link: for each monthly price
9. create_coupon: name="FIRSTMONTH50", percent_off=50, duration="once"text
You are a real estate lead qualification specialist for the {METRO} market.
Lead to Qualify: {{input}}
Score using this weighted rubric (1-10 each):
- Motivation (30%): 10=must sell, 5=considering, 1=curious
- Timeline (25%): 10=30 days, 7=90 days, 4=6 months, 1=none
- Property Value (20%): 10=$2M+, 8=$1M-2M, 5=$500K-1M, 3=under $500K
- Engagement (15%): 10=responded, 7=open house, 5=active, 3=none
- Competition (10%): 10=no agent (FSBO), 7=expired, 3=has agent
Tasks:
1. Search online for the property to verify details
2. Research comparable recent sales in the neighborhood
3. Assess seller motivation from available signals
4. Calculate weighted final score (1-10)
5. Write a personalized 3-4 sentence outreach script
6. Classify: HOT (8-10), WARM (5-7.9), NURTURE (1-4.9)text
Research this real estate agent as a potential customer for an automated
lead generation service that delivers AI-qualified seller leads daily.
Agent: {{input}}
Find: website, email, market focus, estimated annual volume, buying signals
(active marketing, growing team, high volume, awards, thought leadership).
Score purchase likelihood (1-10).
Write a 3-4 sentence outreach email referencing their specific achievements
and offering a free sample report. Service pricing: Starter $149/mo,
Professional $299/mo, Enterprise $599/mo.text
cron: "0 0 7 * * 1-5"
name: "Daily {METRO} Lead Discovery"
prompt: [Run stages 3-4 using files in ~/lead_gen_pipeline/]Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB OPENCLEW
Editorial quality
ready
Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. --- name: real-estate-lead-gen description: Automated real estate lead generation pipeline that discovers, qualifies, and delivers seller leads to agents. Use when users want to find motivated sellers (FSBO, expired listings, price reductions, pre-foreclosures), score leads with AI, prospect for agent customers, set up Stripe monetization, or schedule recurring lead delivery via Gmail. Supports any US metro area. ---
Automated pipeline that discovers motivated seller leads, qualifies them with AI scoring, prospects for agent customers, monetizes via Stripe, and delivers results via Gmail on a recurring schedule.
The pipeline has 6 stages executed in order:
Determine which stages to run based on user request. For first-time setup, run all 6. For recurring execution, run stages 3-4 only.
Create a working directory and configuration files.
mkdir -p ~/lead_gen_pipeline
Read the ICP template: references/icp_template.md. Customize for the user's target metro area by replacing placeholders ({METRO}, {BOROUGH_LIST}, {NEIGHBORHOOD_LIST}).
Save customized ICP to ~/lead_gen_pipeline/icp.md.
Read the email templates: references/email_templates.md. Customize company name and metro area. Save to ~/lead_gen_pipeline/email_templates.md.
Scoring Rubric (always use these weights):
| Factor | Weight | 10 = Best | 1 = Worst | |--------|--------|-----------|----------| | Motivation | 30% | Must sell (foreclosure, estate, divorce) | Just curious | | Timeline | 25% | Within 30 days | No timeline | | Property Value | 20% | $2M+ | Under $200K | | Engagement | 15% | Responded / open house | No signals | | Competition | 10% | No agent (FSBO) | Exclusive listing |
Final Score = (Motivation × 0.3) + (Timeline × 0.25) + (Value × 0.2) + (Engagement × 0.15) + (Competition × 0.1)
Tiers: HOT (8-10), WARM (5-7.9), NURTURE (1-4.9)
Use the Stripe MCP server to create products and pricing. Run these commands in order:
1. create_product: name="Real Estate Lead Intelligence — {METRO}"
2. create_price: Starter $149/mo recurring monthly
3. create_price: Starter $1,490/yr recurring yearly
4. create_price: Professional $299/mo recurring monthly
5. create_price: Professional $2,990/yr recurring yearly
6. create_price: Enterprise $599/mo recurring monthly
7. create_price: Enterprise $5,990/yr recurring yearly
8. create_payment_link: for each monthly price
9. create_coupon: name="FIRSTMONTH50", percent_off=50, duration="once"
Save all IDs and links to ~/lead_gen_pipeline/stripe_config.md.
Tier features:
| Tier | Boroughs | Frequency | Leads/week | |------|----------|-----------|------------| | Starter $149/mo | 1 | Weekly digest | ~20 | | Professional $299/mo | 3 | Daily + weekly | ~50 | | Enterprise $599/mo | All | Real-time + daily + weekly | Unlimited |
Scan multiple sources for each lead category. Adapt source URLs to the target metro.
Category 1 — Price Reductions: Browse the metro's primary listing site (StreetEasy for NYC, Redfin/Zillow for others) filtered by price drops > 5% in the last 14 days. Collect: address, neighborhood, type, price, drop amount, beds/baths/sqft, brokerage.
Category 2 — FSBO: Browse Zillow FSBO section filtered to the target metro. Collect: address, borough, price, beds/baths/sqft, notes.
Category 3 — Expired/Long DOM: Search for listings with 90+ days on market or recently expired. Collect: address, neighborhood, type, price, DOM, brokerage.
Category 4 — Pre-Foreclosure: Search for lis pendens, NOD filings, or pre-foreclosure listings in the metro. Collect: address, borough, estimated value, filing type.
Save all raw leads to ~/lead_gen_pipeline/daily_leads_raw.md with date header and tables per category.
Target: 20-50 raw leads per scan.
Use the map tool to qualify all discovered leads in parallel.
Prompt template for each lead:
You are a real estate lead qualification specialist for the {METRO} market.
Lead to Qualify: {{input}}
Score using this weighted rubric (1-10 each):
- Motivation (30%): 10=must sell, 5=considering, 1=curious
- Timeline (25%): 10=30 days, 7=90 days, 4=6 months, 1=none
- Property Value (20%): 10=$2M+, 8=$1M-2M, 5=$500K-1M, 3=under $500K
- Engagement (15%): 10=responded, 7=open house, 5=active, 3=none
- Competition (10%): 10=no agent (FSBO), 7=expired, 3=has agent
Tasks:
1. Search online for the property to verify details
2. Research comparable recent sales in the neighborhood
3. Assess seller motivation from available signals
4. Calculate weighted final score (1-10)
5. Write a personalized 3-4 sentence outreach script
6. Classify: HOT (8-10), WARM (5-7.9), NURTURE (1-4.9)
Output schema for map tool:
| Field | Type | Description | |-------|------|-------------| | address | string | Full property address | | lead_type | string | Price Reduction / FSBO / Expired / Pre-Foreclosure | | estimated_value | string | Dollar amount | | motivation_score | number | 1-10 | | timeline_score | number | 1-10 | | value_score | number | 1-10 | | engagement_score | number | 1-10 | | competition_score | number | 1-10 | | final_score | number | 1.0-10.0 | | tier | string | HOT / WARM / NURTURE | | outreach_script | string | 3-4 sentence personalized script | | key_signals | string | Comma-separated signals |
Save qualified leads sorted by score to ~/lead_gen_pipeline/daily_leads_qualified.md.
Use the map tool to research target agents in parallel.
Identify 10 top-producing agents in the metro by searching for "top real estate agents {METRO} {YEAR}" and collecting names, brokerages, and specialties.
Prompt template for each agent:
Research this real estate agent as a potential customer for an automated
lead generation service that delivers AI-qualified seller leads daily.
Agent: {{input}}
Find: website, email, market focus, estimated annual volume, buying signals
(active marketing, growing team, high volume, awards, thought leadership).
Score purchase likelihood (1-10).
Write a 3-4 sentence outreach email referencing their specific achievements
and offering a free sample report. Service pricing: Starter $149/mo,
Professional $299/mo, Enterprise $599/mo.
Output schema for map tool:
| Field | Type | Description | |-------|------|-------------| | agent_name | string | Full name | | brokerage | string | Company name | | market_focus | string | Borough/neighborhoods | | annual_volume | string | Dollar amount or Unknown | | website | string | URL or Not found | | email | string | Email or Not found | | buying_signals | string | Comma-separated signals | | purchase_likelihood | number | 1-10 | | outreach_draft | string | Personalized email body |
Save to ~/lead_gen_pipeline/agent_prospects.md.
Schedule recurring tasks using the schedule tool.
Daily lead scan (weekdays 7 AM):
cron: "0 0 7 * * 1-5"
name: "Daily {METRO} Lead Discovery"
prompt: [Run stages 3-4 using files in ~/lead_gen_pipeline/]
The scheduled prompt should instruct Manus to:
~/lead_gen_pipeline/icp.md~/lead_gen_pipeline/email_templates.md~/lead_gen_pipeline/delivery_log.mdUse the Gmail MCP server (manus-mcp-cli tool call send_email --server gmail) to deliver leads.
HOT lead alert: Send immediately when a lead scores 8+. Use Template 1 from email templates.
Weekly digest: Compile all qualified leads from the week into a summary table. Use Template 2 from email templates.
Agent outreach: Send personalized prospecting emails to agent prospects. Use Template 3 from email templates.
Always ask user for confirmation before sending emails (confirm_browser_operation).
After full execution, the working directory contains:
| File | Purpose |
|------|--------|
| icp.md | Ideal Customer Profile and scoring rubric |
| email_templates.md | Email templates for alerts, digests, outreach |
| stripe_config.md | Stripe product IDs, prices, payment links |
| daily_leads_raw.md | Raw discovered leads (updated daily) |
| daily_leads_qualified.md | Scored and tiered leads (updated daily) |
| agent_prospects.md | Researched agent customers with outreach drafts |
| delivery_log.md | Audit log of all deliveries |
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract"
curl -s "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
Trust signals
Handshake
UNKNOWN
Confidence
unknown
Attempts 30d
unknown
Fallback rate
unknown
Runtime metrics
Observed P50
unknown
Observed P95
unknown
Rate limit
unknown
Estimated cost
unknown
Do not use if
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
A Model Context Protocol (MCP) server for GitLab
A Model Context Protocol (MCP) server for GitLab
This agent researches trends, scripts videos, sets up engagement automation, and compiles everything into a shareable document.
This agent analyzes Reddit data to generate trending content concepts tailored to your audience.
Contract JSON
{
"contractStatus": "missing",
"authModes": [],
"requires": [],
"forbidden": [],
"supportsMcp": false,
"supportsA2a": false,
"supportsStreaming": false,
"inputSchemaRef": null,
"outputSchemaRef": null,
"dataRegion": null,
"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
"snapshotUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/snapshot",
"contractUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract",
"trustUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust"
},
"curlExamples": [
"curl -s \"https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/snapshot\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"MCP"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_OPENCLEW",
"generatedAt": "2026-10-09T02:29:24.710Z"
}
},
"retryPolicy": {
"maxAttempts": 3,
"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
"HTTP_429",
"HTTP_503",
"NETWORK_TIMEOUT"
]
}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
"fallbackRate": null,
"attempts30d": null,
"trustUpdatedAt": null,
"trustConfidence": "unknown",
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Capability Matrix
{
"rows": [
{
"key": "MCP",
"type": "protocol",
"support": "unknown",
"confidenceSource": "profile",
"notes": "Listed on profile"
},
{
"key": "listing",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "multiple",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
}
],
"flattenedTokens": "protocol:MCP|unknown|profile capability:listing|supported|profile capability:multiple|supported|profile"
}Facts JSON
[
{
"factKey": "docs_crawl",
"label": "Crawlable docs",
"value": "6 indexed pages on the official domain",
"category": "integration",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "vendor",
"label": "Vendor",
"value": "Estherperdana7",
"category": "vendor",
"href": "https://github.com/estherperdana7/real-estate-lead-gen",
"sourceUrl": "https://github.com/estherperdana7/real-estate-lead-gen",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-15T01:15:14.761Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "protocols",
"label": "Protocol compatibility",
"value": "MCP",
"category": "compatibility",
"href": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-04-15T01:15:14.761Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "handshake_status",
"label": "Handshake status",
"value": "UNKNOWN",
"category": "security",
"href": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/estherperdana7-real-estate-lead-gen/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true,
"metadata": {}
}
]Change Events JSON
[
{
"eventType": "docs_update",
"title": "Docs refreshed: Sign in to GitHub · GitHub",
"description": "Fresh crawlable documentation was indexed for the official domain.",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true,
"metadata": {}
}
]Sponsored
Ads related to real-estate-lead-gen and adjacent AI workflows.