io.github.shashwatgtm/icp-intelligence-mcp
NPM · @SHASHWATGTMALPHA/ICP-INTELLIGENCE-MCP · SCANNED AUG 3
Deep ICP Analysis with Pattern Detection - 9 B2B targeting tools
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score →
Supply Chain Security70
- No malware found by supply-chain analysis.Pass
- CVE check failed: a known high-severity CVE affects @modelcontextprotocol/sdk 0.6.1, a direct dependency. A fixed version is available. View diagnostics → Fail
- No install/post-install scripts declared.Pass
- Only part of the dependency tree could be resolved (13 of 14), so this covers what we could see, not the whole tree. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 197 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability66
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 807 tokens (~89/item across 9 items; 9 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management23
- Stability observed for 7 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage99
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 97% of tool parameters carry a description.Partial
Capabilities20
- Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28. See how to fix → Fail
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
npm · @shashwatgtmalpha/icp-intelligence-mcp
claude mcp add shashwatgtm-icp-intelligence-mcp -- npx -y @shashwatgtmalpha/icp-intelligence-mcp
codex mcp add shashwatgtm-icp-intelligence-mcp -- npx -y @shashwatgtmalpha/icp-intelligence-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"shashwatgtm-icp-intelligence-mcp": {
"type": "local",
"command": [
"npx",
"-y",
"@shashwatgtmalpha/icp-intelligence-mcp"
],
"enabled": true
}
}
} openclaw mcp add shashwatgtm-icp-intelligence-mcp --command npx --arg -y --arg @shashwatgtmalpha/icp-intelligence-mcp
mcp_servers:
shashwatgtm-icp-intelligence-mcp:
command: "npx"
args: ["-y", "@shashwatgtmalpha/icp-intelligence-mcp"] {
"mcpServers": {
"shashwatgtm-icp-intelligence-mcp": {
"command": "npx",
"args": [
"-y",
"@shashwatgtmalpha/icp-intelligence-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 3 Aug 26 +4
- Stability: unverified → 0.23 ▲ functional
- 2 Aug 26 +12
- Malware scan: unverified → pass ▲ security
- 31 Jul 26 −14
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 30 Jul 26 −15
- Known CVEs: fail → unverified ▼ security
- Provenance: fail → unverified ▼ security
- Install scripts: pass → unverified ▼ security
- CVE-2025-66414 no longer affects this package ▲ security
- License: pass → unverified ▼ functional
- Dependency health: partial → unverified ▼ functional
- Maintenance: pass → unverified ▼ functional
- Licence: MIT functional
- 28 Jul 26 +47
- CVE-2025-66414 affects this package: high ▼ security
- Provenance: unverified → fail ▼ security
- Known CVEs: unverified → fail ▼ security
- Install scripts: unverified → pass ▲ security
- License: unverified → pass ▲ functional
- Tool coverage: unverified → 100 ▲ functional
- Dependency health: unverified → partial ▲ functional
- Maintenance: unverified → pass ▲ functional
- First check of Schema quality: fail functional
- First check of Tool coverage: 97 functional
- First check of Schema quality: pass functional
- First check of Schema quality: good functional
- Licence: MIT functional
- 27 Jul 26 24
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 3 Aug 2026 · Analysed npm/@shashwatgtmalpha/[email protected]
Provenance none
Ecosystem: npm · Outcome: none
Vulnerabilities 1 finding
| ID | CVE | Severity | Vector | Fix available |
|---|---|---|---|---|
| GHSA-w48q-cv73-mx4w | CVE-2025-66414 | high | yes |
Dependencies 13 packages
13 packages in the resolved dependency tree · 13 deprecated · 6 stale.
The dependency tree was only partially resolved, so these counts may be incomplete.
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.
account_prioritization ~48
Rank and prioritize accounts using multi-dimensional scoring
| Name | Type | Req | Description |
|---|---|---|---|
| accounts | array | — | List of accounts to prioritize |
| prioritization_weights | object | — | Custom weights (must sum to 100) |
No output schema declared.
No examples provided.
buyer_group_analyzer ~104
Map buyer group dynamics, influence relationships, and decision-making process
| Name | Type | Req | Description |
|---|---|---|---|
| deal_size | string | — | ACV range (e.g., "$50K-100K") |
| known_stakeholders | array | — | Roles you know are involved |
| product_category | string | yes | What you sell |
| target_company_size | string | — | Company size (e.g., "500-1000 employees") |
| typical_champion | string | — | Your typical champion role |
No output schema declared.
No examples provided.
icp_deep_dive ~75
Analyze customer data to detect ICP patterns - firmographics, technographics, buying behavior
| Name | Type | Req | Description |
|---|---|---|---|
| customer_descriptions | string | — | Alternative: Describe your best customers in text format |
| customers | array | — | List of customer objects with available attributes |
| product_category | string | — | What type of product you sell |
No output schema declared.
No examples provided.
icp_evolution_tracker ~99
Track how your ICP should evolve based on market changes and data
| Name | Type | Req | Description |
|---|---|---|---|
| current_icp | string | yes | Your current ICP definition |
| market_changes | string | — | Recent market or competitive changes |
| recent_losses | string | — | Description of recent lost deals |
| recent_wins | string | — | Description of recent successful customers |
| time_period | string | — | Time period for analysis (e.g., "Q4 2024") |
No output schema declared.
No examples provided.
icp_gap_analysis ~72
Analyze gaps between current customer base and ideal ICP
| Name | Type | Req | Description |
|---|---|---|---|
| current_customers | string | yes | Description of your current customer base |
| current_metrics | object | — | Current performance metrics |
| ideal_icp | string | yes | Description of your ideal customer profile |
| target_metrics | object | — | Target performance metrics |
No output schema declared.
No examples provided.
icp_interview_synthesizer ~76
Extract ICP patterns from customer interview notes or transcripts
| Name | Type | Req | Description |
|---|---|---|---|
| analysis_focus | string | — | What to focus on: pain_points, buying_journey, value_props, all |
| interview_notes | array | — | Structured interview notes |
| raw_transcripts | string | — | Alternative: Paste raw interview transcripts or notes |
No output schema declared.
No examples provided.
icp_scoring_model ~80
Create qualification scoring model with auto-weighted criteria based on your success patterns
| Name | Type | Req | Description |
|---|---|---|---|
| product_category | string | — | — |
| scoring_criteria | array | — | Criteria for scoring with importance levels |
| success_correlation | string | — | What correlates with success? (e.g., "deals with VP Sales champion close 2x faster") |
No output schema declared.
No examples provided.
lookalike_signal_generator ~107
Generate platform-specific targeting criteria and search queries (generates criteria, not data)
| Name | Type | Req | Description |
|---|---|---|---|
| buying_triggers | array | — | Events that trigger buying |
| champion_titles | array | yes | Job titles of your champions |
| icp_firmographics | object | — | Firmographic criteria |
| icp_technographics | array | — | Technologies your ICP typically uses |
| platforms | array | — | Platforms to generate criteria for (linkedin, google_ads, 6sense, zoominfo) |
No output schema declared.
No examples provided.
tam_sam_som_calculator ~146
Calculate TAM/SAM/SOM using bottom-up methodology from your data (calculation framework, not data source)
| Name | Type | Req | Description |
|---|---|---|---|
| average_contract_value | number | yes | Your average ACV in dollars |
| data_sources | string | — | Where you got your numbers (for documentation) |
| icp_percentage | number | — | Percentage that match your ICP (1-100) |
| segment_name | string | — | Name of the market segment |
| total_potential_companies | number | yes | Estimated total companies that could buy (from LinkedIn, industry reports) |
| year1_market_share_target | number | — | Realistic Year 1 market share percentage (typically 1-5%) |
No output schema declared.
No examples provided.