Cognitive Warfare & PsyOps Analysis
About
Cognitive warfare and PSYOPS analysis for AI agents via the Model Context Protocol.
Details
- License
- MIT
Explore
- Coupled SIR-Hawkes model — infection rate in the SIR compartmental model is modulated by Hawkes self-exciting point process intensity λ(t) = μ + Σ α·exp(-β(t-tᵢ)), enabling detection of coordinated amplification campaigns that would appear organic to simpler models
- DeGroot social learning with stubborn agents — iterative belief update x_i(t+1) = λ_i·b_i + (1-λ_i)·Σ(w_ij·x_j(t)) where λ_i is stubbornness (0 = fully susceptible, 1 = immovable), with eigenvector centrality and spectral gap computation
- Bayesian Stackelberg game (BSSE via multiple-LP) — leader (defender) commits to mixed strategy; follower (adversary) best-responds given beliefs; optimal timing via Shiryaev-Roberts statistic R_n = (1+R_{n-1})·LR_n and CUSUM S_n = max(0, S_{n-1}+log(LR_n))
- Submodular greedy influence maximization — Independent Cascade model with Monte Carlo simulation and (1-1/e) approximation guarantee for finding optimal influence seeds
- Persistent homology (Vietoris-Rips filtration) — Betti numbers β₀ (connected components = fragmentation), β₁ (1-cycles = echo chambers), β₂ (voids = higher-order structure) computed via Union-Find on semantic simplicial complexes
- Doubly-robust causal inference (Rubin causal model) — combines propensity score weighting (inverse probability) with outcome regression; consistent if either model is correctly specified; Rosenbaum sensitivity analysis quantifies hidden bias needed to invalidate conclusions
- Price equation evolutionary dynamics — decomposes fitness change into selection differential Cov(w,z)/w̄ and transmission bias E(w·Δz)/w̄; replicator dynamics with mutation for frequency evolution across narrative variants
- Haar wavelet packet decomposition — approximation a[k]=(x[2k]+x[2k+1])/√2 and detail d[k]=(x[2k]-x[2k+1])/√2 at hourly-tactical through weekly-strategic scales; cross-scale coherence reveals hierarchical coordination invisible at any single scale
- Hegselmann-Krause bounded confidence + q-state Potts model — Hamiltonian H = -J·Σδ(σᵢ,σⱼ) with critical temperature T_c = J/ln(1+√q); spontaneous symmetry breaking below T_c signals irreversible radicalization
- Five operation type classifiers — amplification, suppression, distortion, fabrication, polarization — each with coordination score and four-tier threat level
- Four regime classifiers — CONSENSUS, PLURALISM, POLARIZED, FRAGMENTED — derived from order parameter (magnetization) and susceptibility
- Network reproduction number — reports R₀ for narrative spread across the multiplex social-geopolitical network
- Infrastructure attribution — DNS and IP geolocation data feeds directly into causal attribution for narrative source identification
- Seeded PRNG for reproducibility — mulberry32 algorithm ensures deterministic scoring given identical inputs
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Cognitive Warfare & PsyOps AnalysisCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Add to your MCP client (Claude Desktop, Cursor, Windsurf):
{
"mcpServers": {
"cognitive-warfare-psyops-mcp": {
"url": "https://ryanclinton--cognitive-warfare-psyops-mcp.apify.actor/mcp"
}
}
}
---
Cognitive warfare and PSYOPS analysis for AI agents via the Model Context Protocol. This MCP server gives Claude, Cursor, Windsurf, and any MCP-compatible client access to 8 specialized tools that detect coordinated narrative operations, model belief propagation, optimize counter-narrative strategy, and forecast polarization phase transitions — all powered by 16 Apify data sources queried in parallel.
Each tool applies a distinct mathematical framework derived from epidemiology, evolutionary biology, statistical physics, and game theory. The result is a structured, quantitative analysis of information warfare that goes beyond keyword monitoring into causal attribution, topological network mapping, and memetic fitness modeling — built for STRATCOM analysts, platform integrity teams, counter-disinformation researchers, and intelligence-augmented AI agents.
detect_narrative_operations
$0.050
model_belief_dynamics
$0.045
optimize_counter_narrative
$0.055
map_influence_topology
$0.040
attribute_narrative_causation
$0.045
simulate_memetic_evolution
$0.050
detect_cross_scale_coordination
$0.045
forecast_polarization_phase_transition
$0.050
Parameter
Type
query
string
maxResults
number
| Tool | Price | Description |
|------|-------|-------------|
| detect_narrative_operations | $0.050 | Detect coordinated narrative operations via coupled SIR-Hawkes epidemiological model. Classifies type (amplification/suppression/distortion/fabrication/polarization) with threat levels (LOW/MEDIUM/HIGH/CRITICAL). |
| model_belief_dynamics | $0.045 | Model belief propagation via DeGroot social learning on influence networks. Returns eigenvector centrality, spectral gap (convergence rate), polarization index, and belief cluster structure. |
| optimize_counter_narrative | $0.055 | Optimize counter-narrative strategy via Bayesian Stackelberg game. Returns optimal intervention actions, CUSUM/Shiryaev-Roberts timing alarms, and Stackelberg equilibrium payoffs. |
| map_influence_topology | $0.040 | Map influence network topology via submodular greedy influence maximization and persistent homology. Returns Betti numbers, echo chamber count, fragmentation index, and optimal influence seeds. |
| attribute_narrative_causation | $0.045 | Attribute narrative effects to specific actors via doubly-robust causal inference. Returns ATE, propensity scores, Rosenbaum sensitivity analysis, and ranked causal actors. |
| simulate_memetic_evolution | $0.050 | Simulate narrative variant competition via Price equation evolutionary dynamics. Returns fitness landscape, selection vs transmission decomposition, and dominant variant trajectory. |
| detect_cross_scale_coordination | $0.045 | Detect coordination patterns across individual/group/network/population scales via Haar wavelet packet decomposition. Returns cross-scale coherence matrix and dominant coordination signals. |
| forecast_polarization_phase_transition | $0.050 | Forecast polarization phase transitions via q-state Potts model. Returns order parameter, critical temperature, susceptibility, regime (CONSENSUS/PLURALISM/POLARIZED/FRAGMENTED), and irreversibility risk. |
All tools share the same two input parameters:
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| query | string | Yes | — | The narrative, topic, campaign, or threat vector to analyze. Natural language. Examples in each tool description. |
| maxResults | number | No | 30 | Maximum results per data source (range: 5–100). Higher values produce richer networks but increase response time. |
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"cognitive warfare & psyops analysis": {
"cognitive-warfare-psyops-mcp": {
"url": "https://ryanclinton--cognitive-warfare-psyops-mcp.apify.actor/mcp"
}
}
}
}McpServers
{
"cognitive-warfare-psyops-mcp": {
"url": "https://ryanclinton--cognitive-warfare-psyops-mcp.apify.actor/mcp"
}
}Cognitive Warfare & PSYOPS MCP Server
> View on ApifyForge | Use on Apify Store
---
Quick Start
Add to your MCP client (Claude Desktop, Cursor, Windsurf):
{
"mcpServers": {
"cognitive-warfare-psyops-mcp": {
"url": "https://ryanclinton--cognitive-warfare-psyops-mcp.apify.actor/mcp"
}
}
}
---
Cognitive warfare and PSYOPS analysis for AI agents via the Model Context Protocol. This MCP server gives Claude, Cursor, Windsurf, and any MCP-compatible client access to 8 specialized tools that detect coordinated narrative operations, model belief propagation, optimize counter-narrative strategy, and forecast polarization phase transitions — all powered by 16 Apify data sources queried in parallel.
Each tool applies a distinct mathematical framework derived from epidemiology, evolutionary biology, statistical physics, and game theory. The result is a structured, quantitative analysis of information warfare that goes beyond keyword monitoring into causal attribution, topological network mapping, and memetic fitness modeling — built for STRATCOM analysts, platform integrity teams, counter-disinformation researchers, and intelligence-augmented AI agents.
What data does this MCP server access?
| Data Point | Source | Coverage |
|-----------|--------|----------|
| 📡 Social media posts and engagement | Bluesky Social | Real-time Bluesky network |
| 💬 Community discussions and trends | Hacker News | Tech community discourse |
| 📚 Encyclopedic context and edit history | Wikipedia | 6M+ articles |
| 🏛️ US federal regulatory activity | Federal Register | All federal actions |
| 🚨 International wanted persons | Interpol Red Notices | Global notices |
| ⚠️ Global sanctions and PEP watchlists | OpenSanctions | 100+ programs |
| 🔄 Website content changes | Website Change Monitor | Media and government sites |
| 🕰️ Historical content evolution | Wayback Machine | Web Archive snapshots |
| 🌍 Country profiles and demographics | REST Countries | All UN member states |
| 🌪️ Global disaster and crisis events | GDACS Disaster Alerts | Worldwide coverage |
| 📜 Congressional legislation | Congress Bill Tracker | Current sessions |
| 🌩️ Weather events and crisis windows | NOAA Weather | US and global |
| 🛠️ Open-source tools and code | GitHub Repo Search | All public repositories |
| 📄 Policy documents (full text) | Website Content to Markdown | Any webpage |
| 🔍 DNS infrastructure records | DNS Lookup | Any domain |
| 📍 IP geolocation attribution | IP Geolocation | Global coverage |
MCP Tools
| Tool | Price | Description |
|------|-------|-------------|
| detect_narrative_operations | $0.050 | Detect coordinated narrative operations via coupled SIR-Hawkes epidemiological model. Classifies type (amplification/suppression/distortion/fabrication/polarization) with threat levels (LOW/MEDIUM/HIGH/CRITICAL). |
| model_belief_dynamics | $0.045 | Model belief propagation via DeGroot social learning on influence networks. Returns eigenvector centrality, spectral gap (convergence rate), polarization index, and belief cluster structure. |
| optimize_counter_narrative | $0.055 | Optimize counter-narrative strategy via Bayesian Stackelberg game. Returns optimal intervention actions, CUSUM/Shiryaev-Roberts timing alarms, and Stackelberg equilibrium payoffs. |
| map_influence_topology | $0.040 | Map influence network topology via submodular greedy influence maximization and persistent homology. Returns Betti numbers, echo chamber count, fragmentation index, and optimal influence seeds. |
| attribute_narrative_causation | $0.045 | Attribute narrative effects to specific actors via doubly-robust causal inference. Returns ATE, propensity scores, Rosenbaum sensitivity analysis, and ranked causal actors. |
| simulate_memetic_evolution | $0.050 | Simulate narrative variant competition via Price equation evolutionary dynamics. Returns fitness landscape, selection vs transmission decomposition, and dominant variant trajectory. |
| detect_cross_scale_coordination | $0.045 | Detect coordination patterns across individual/group/network/population scales via Haar wavelet packet decomposition. Returns cross-scale coherence matrix and dominant coordination signals. |
| forecast_polarization_phase_transition | $0.050 | Forecast polarization phase transitions via q-state Potts model. Returns order parameter, critical temperature, susceptibility, regime (CONSENSUS/PLURALISM/POLARIZED/FRAGMENTED), and irreversibility risk. |
Why use this MCP server for narrative intelligence?
Traditional social listening tools count mentions and measure sentiment. They cannot tell you whether a campaign is coordinated, which actors are causally responsible for a belief shift, how far a population is from an irreversible polarization transition, or what the optimal intervention timing would be against a modeled adversary.
This server provides the quantitative frameworks that analysts in STRATCOM, platform integrity, and counter-disinformation research already use — SIR epidemic modeling, Bayesian game theory, topological data analysis, evolutionary dynamics — delivered as MCP tools that an AI agent can call directly. No infrastructure to manage. No data pipeline to build. Query 16 data sources in parallel and receive a structured analytical output in a single tool call.
Key platform advantages:
- Standby mode — the server stays warm between calls; no cold start latency for time-sensitive operations
- API access — trigger any tool from Python, JavaScript, or any HTTP client with your Apify token
- Spending limits — each tool call checks your event charge limit and stops gracefully if reached
- 16 parallel data sources — social, regulatory, sanctions, infrastructure, geopolitical, and archival data in a single call
- Structured JSON output — every tool returns a typed result ready for downstream agent reasoning
Features
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