Skip to main content
Glama
saurabhshuklagrowisto

Lead Scraping Plan MCP Server

by saurabhshuklagrowisto
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Remote

Lead Scraping Plan — signal-scored outbound pipeline for the staffing industry

An agentic outbound pipeline + mobile command center for a healthcare-staffing agency: scrape US healthcare-admin job postings, score them against the agency's actual buyer ICP, gate on company size for free before spending a credit, enrich, write cluster-routed sequences, and hand booked replies to the sales team — with a feedback → learning → eval-gated loop that actually closes.

Built as a demonstrably better rework of an existing open-source outbound dashboard (the incumbent tool), aligned to a finalized scraping plan (v3).

Why this beats the reference dashboard (measured, not claimed)

On 61 real human-rated postings exported from the original dashboard:

Original AI

This scorer v1

Agreement with human gold

67.2%

77.0%

Leads thrown away (false-disqualify)

20 (33%)

4 (6.6%)

The original AI scores against a job-seeker objective — it disqualifies postings that "require US work authorization." But the business here is a staffing seller: those US practices hiring junior admin roles are the customers. Re-orienting the objective from the 61 corrections recovers 16 of the 20 wrongly-discarded leads. The remaining 4 are what the feedback-learning loop targets next (toward a 90% eval-gate target).

Run it yourself: python lib/eval/eval_holdout.py --baseline vs python lib/eval/eval_holdout.py.

Architecture wins over the reference app

Reference app

This repo

Two scorers — the daily cron ran a keyword filter; the "AI" only ran on a manual button, so automation bypassed the AI

One scorer (pipeline/score.py + TS), driven by one versioned rubric.config.json, same path in cron and dashboard

Feedback = 3 overwrite columns on the job row; unbounded free-text "learned rules"; the retrain cron never scheduled; no eval harness

First-class feedback history + scored/decaying learned_rules + an eval gate: a candidate must beat baseline on the locked holdout before it ships

No size gate — a 2,000-person RCM firm slipped through on name alone

Free NPPES provider-count gate before any paid enrichment

Single Gmail sender, desktop-only, real email leaked on a public endpoint

Verify → warmed/rotated send, mobile-first, auth-gated, MCP-operable from Claude

One lead per job posting — the same company repeated across every open role

Account-centric CRM: one account per company, roles grouped as opportunities, 3+ roles score higher

Credit where due: the reference repo's prompt-injection sanitization, DRY_RUN + suppression + send caps, dedup, and LLM cache are good patterns and are reused here.

Related MCP server: linkedin-engine

What's in the box

web/                 mobile-first dashboard (DRY-RUN demo, runs on seed data, deploys as static)
lib/scoring/rubric.config.json   SINGLE SOURCE OF TRUTH — weights, flags, caps, gate, clusters
pipeline/score.py    the one config-driven scorer (re-oriented for a staffing seller)
pipeline/scrape.py   JobSpy scraper (Indeed live; LinkedIn actor; free boards)
lib/eval/            the eval harness (agreement % + false-disqualify on the 61-entry holdout)
eval/holdout/        the 61 human-rated postings (frozen truth set)
db/seed/             259 scored postings, 61 feedback, 7 learned rules, seed eval runs
mcp/                 Claude MCP server — run the pipeline by talking to Claude

Run the demo dashboard locally

cd web && python -m http.server 8080   # open http://localhost:8080 (mobile-friendly)

Screens: Pipeline (tier board + priority accounts) · Jobs (searchable table + CSV export) · CRM (account-centric, deduped) · Review (active-learning feedback loop) · Outreach (sequence queue) · Learning (eval history, learned rules, teach-a-rule) · Tools (integration health + flow) · Settings · Why better (the benchmark above).

Status (DRY-RUN)

Free/live: JobSpy (Indeed), NPPES size gate, the deterministic scorer, the eval harness. Stubbed until keys: LinkedIn actor, Anthropic (Haiku/Sonnet — scoring/sequences run cached), Clay, Cleanlist, SalesBlink, Sendr.io, Close CRM, Supabase. Every stub is labeled on the dashboard's Tools panel. Nothing sends or spends in DRY-RUN.

Notes on scope: "trained" here means dynamic feedback few-shots + a distilled, eval-gated rule store + versioned rubric — not a fine-tuned model. LinkedIn scraping carries a ToS caveat. Company/contact names in the seed data are real public job postings (scraped, not private); learned-rule values stay generalized.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityStale
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • GHL MCP Server

    mnewby20305
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables Claude to manage GoHighLevel CRM contacts, pipelines, and workflows through natural language commands.
    20 npm
    MIT

  • job-search-mcp

    Zetetic-Dhruv
    F
    license
    Not graded
    quality
    C
    maintenance
    Enables running a job search with Claude Code: parses CV, discovers roles, fetches exact application fields, drafts non-trivial applications (positioning, not autofill), and renders an offline dashboard for review.
    -

  • signal-prospector

    thomasproject-stack
    A
    license
    Not graded
    quality
    C
    maintenance
    Enables B2B prospecting from natural language: detect buying signals, score leads against ICP, enrich decision-makers, and draft personalized outreach messages via Claude.
    2
    MIT