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io.github.CSOAI-ORG/crm-ai-mcp

CSOAI-ORG Scanned 16d ago

Crm Ai MCP server. Tools: lead scorer, deal stage predictor, followup scheduler. Built by ME...

C
72.4 / 100

Versions

1.0.1latest
first seen Jun 5, 2026
1.0.4
first seen May 19, 2026
PermissionsTool SafetyAuthAnnotationsCode QualityStabilitySpecVuln HistoryAuthorTransparencyCommunity

Tools 5

lead_scorer
annotations: none low

Score a sales lead (0-100) based on firmographic data, engagement signals, and source quality. Returns tier, recommended action, and score breakdown. Args: company_size: Number of employees industry: Industry vertical (e.g. "technology", "finance", "healthcare") budget: Estimated budget in dollars engagement_signals: Behavioral data as {signal: count} (e.g. {"demo_requested": 1, "email_clicks": 5}) source: Lead source (referral, organic_search, paid_search, linkedin, etc.) days_since_first_touch: Days since first interaction Behavior: This tool is read-only and stateless — it produces analysis output without modifying any external systems, databases, or files. Safe to call repeatedly with identical inputs (idempotent). Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage. When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards. When NOT to use: Not suitable for real-time production decision-making without human review of results.

budget float source str api_key str industry str company_size int engagement_signals dict days_since_first_touch int
deal_stage_predictor
annotations: none low

Predict deal win probability and estimated close date based on pipeline stage, velocity, and deal characteristics. Args: deal_value: Deal amount in dollars days_in_pipeline: Days since deal was created current_stage: Pipeline stage (prospecting, qualification, discovery, proposal, negotiation) activities_count: Number of logged activities (calls, emails, meetings) competitor_mentioned: Whether a competitor has been mentioned champion_identified: Whether an internal champion has been identified Behavior: This tool generates structured output without modifying external systems. Output is deterministic for identical inputs. No side effects. Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage. When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards. When NOT to use: Not suitable for real-time production decision-making without human review of results. Behavioral Transparency: - Side Effects: This tool is read-only and produces no side effects. It does not modify any external state, databases, or files. All output is computed in-memory and returned directly to the caller. - Authentication: No authentication required for basic usage. Pro/Enterprise tiers require a valid MEOK API key passed via the MEOK_API_KEY environment variable. - Rate Limits: Free tier: 10 calls/day. Pro tier: unlimited. Rate limit headers are included in responses (X-RateLimit-Remaining, X-RateLimit-Reset). - Error Handling: Returns structured error objects with 'error' key on failure. Never raises unhandled exceptions. Invalid inputs return descriptive validation errors. - Idempotency: Fully idempotent — calling with the same inputs always produces the same output. Safe to retry on timeout or transient failure. - Data Privacy: No input data is stored, logged, or transmitted to external services. All processing happens locally within the MCP server process.

api_key str deal_value float current_stage str activities_count int days_in_pipeline int champion_identified bool competitor_mentioned bool
followup_scheduler
annotations: none low

Schedule follow-ups for a list of contacts based on priority, last contact date, and engagement strategy. Args: contacts: List as [{"name": "X", "email": "x@y.com", "last_contact_date": "2026-01-01", "priority": "high", "deal_value": 50000}] strategy: Follow-up cadence (aggressive, standard, nurture, reactivation) Behavior: This tool is read-only and stateless — it produces analysis output without modifying any external systems, databases, or files. Safe to call repeatedly with identical inputs (idempotent). Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage. When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards. When NOT to use: Not suitable for real-time production decision-making without human review of results.

api_key str contacts string strategy str
customer_health_score
annotations: none low

Calculate a customer health score (0-100) from usage, support, NPS, adoption, and tenure signals. Args: usage_metrics: Usage data as {"monthly_logins": N, "active_users": N, "total_users": N} support_tickets: Open support tickets in last 30 days nps_score: Net Promoter Score (0-10) contract_value: Annual contract value months_as_customer: Customer tenure in months feature_adoption_pct: Percentage of features adopted (0-100) Behavior: This tool is read-only and stateless — it produces analysis output without modifying any external systems, databases, or files. Safe to call repeatedly with identical inputs (idempotent). Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage. When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards. When NOT to use: Not suitable for real-time production decision-making without human review of results.

api_key str nps_score int usage_metrics dict contract_value float support_tickets int months_as_customer int feature_adoption_pct float
churn_predictor
annotations: none low

Predict churn probability and recommend a retention playbook based on usage trends, support patterns, and contract timeline. Args: months_as_customer: Customer tenure in months monthly_usage_trend: Recent monthly usage values (e.g. [100, 95, 80, 60]) support_tickets_last_90d: Support tickets opened in last 90 days nps_score: Latest NPS score (0-10) contract_renewal_days: Days until contract renewal competitor_mentions: Times competitor was mentioned in interactions Behavior: This tool is read-only and stateless — it produces analysis output without modifying any external systems, databases, or files. Safe to call repeatedly with identical inputs (idempotent). Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage. When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards. When NOT to use: Not suitable for real-time production decision-making without human review of results. Behavioral Transparency: - Side Effects: This tool is read-only and produces no side effects. It does not modify any external state, databases, or files. All output is computed in-memory and returned directly to the caller. - Authentication: No authentication required for basic usage. Pro/Enterprise tiers require a valid MEOK API key passed via the MEOK_API_KEY environment variable. - Rate Limits: Free tier: 10 calls/day. Pro tier: unlimited. Rate limit headers are included in responses (X-RateLimit-Remaining, X-RateLimit-Reset). - Error Handling: Returns structured error objects with 'error' key on failure. Never raises unhandled exceptions. Invalid inputs return descriptive validation errors. - Idempotency: Fully idempotent — calling with the same inputs always produces the same output. Safe to retry on timeout or transient failure. - Data Privacy: No input data is stored, logged, or transmitted to external services. All processing happens locally within the MCP server process.

api_key str nps_score int months_as_customer int competitor_mentions int monthly_usage_trend string contract_renewal_days int support_tickets_last_90d int

Permissions 3

network medium
Server uses network capabilities via: urllib
filesystem low
Server uses filesystem capabilities via: os
env_vars low
Server uses env_vars capabilities via: os.environ

Scan Findings 32

low
Tool 'lead_scorer' has no annotations annotation_checker · 100%
low
Tool 'deal_stage_predictor' has no annotations annotation_checker · 100%
low
Tool 'followup_scheduler' has no annotations annotation_checker · 100%
low
Tool 'customer_health_score' has no annotations annotation_checker · 100%
low
Tool 'churn_predictor' has no annotations annotation_checker · 100%
high
Remote transport without authentication auth_checker · 70%
info
Sandbox failed to start for behavioral verification behavioral_verifier · 100%
medium
Vulnerable dependency: mcp@1.0.0 (GHSA-3qhf-m339-9g5v) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (GHSA-9h52-p55h-vw2f) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (GHSA-j975-95f5-7wqh) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (GHSA-jpw9-pfvf-9f58) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (GHSA-vj7q-gjh5-988w) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (PYSEC-2026-1616) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (PYSEC-2026-1617) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (PYSEC-2026-1618) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (PYSEC-2026-3482) dependency_analyzer · 95%
medium
Vulnerable dependency: mcp@1.0.0 (PYSEC-2026-3483) dependency_analyzer · 95%
info
package.json metadata manifest_parser · 100%
info
pyproject.toml metadata manifest_parser · 100%
info
Tool: lead_scorer manifest_parser · 90%
info
Tool: deal_stage_predictor manifest_parser · 90%
info
Tool: followup_scheduler manifest_parser · 90%
info
Tool: customer_health_score manifest_parser · 90%
info
Tool: churn_predictor manifest_parser · 90%
info
Transport: streamable-http manifest_parser · 80%
info
Required env vars (5) manifest_parser · 80%
info
Sandbox failed to start for output poisoning scan output_poisoning · 100%
medium
Permission: network access detected permission_analyzer · 80%
low
Permission: filesystem access detected permission_analyzer · 70%
low
Permission: env_vars access detected permission_analyzer · 90%
info
No dependency files found for SBOM generation sbom_generator · 100%
medium
No build provenance detected (SLSA L0) slsa_assessor · 90%