Morphogenetic Innovation MCP Server

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Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "morphogenetic-innovation-mcp": {
      "url": "https://ryanclinton--morphogenetic-innovation-mcp.apify.actor/mcp"
    }
  }
}

---

Morphogenetic Innovation MCP Server applies mathematical biology to technology landscape analysis, giving AI agents a structured way to reason about how innovations evolve, compete, and disrupt. Connect Claude, Cursor, or any MCP-compatible agent to 8 analytical tools backed by 16 live data sources — patents, academic papers, GitHub repos, financial signals, job postings, and funding data — all interpreted through rigorous evolutionary frameworks.

Each tool call runs 5 to 12 data actors in parallel, synthesizes the results into structured output, and returns a machine-readable interpretation with quantified metrics. No data pipelines to build, no statistical libraries to configure — the math is built in.

What data can you analyze?

| Data Point | Source | Example |
|---|---|---|
| 📄 US patents | USPTO PatentsView | Quantum error correction filings, 2018–2025 |
| 📄 European patents | EPO Open Patent Services | Battery electrode composition claims |
| ™ EU trademarks | EUIPO | Software product registrations |
| 📚 Academic publications | OpenAlex (250M+ works) | Nature papers on transformer architectures |
| 📋 Preprints | ArXiv (2M+ papers) | Latest diffusion model research |
| 📖 Computer science papers | Semantic Scholar (200M+) | Citation graph of RLHF variants |
| 💻 Open source repositories | GitHub public repos | Stars, forks, activity for LLM inference tools |
| 💬 Tech discussions | Hacker News | Community sentiment on WebAssembly runtimes |
| 📈 Market data | Finnhub global equities | AI semiconductor company valuations |
| 🪙 Cryptocurrency data | CoinGecko | DeFi protocol market capitalizations |
| 🏥 NIH grants | NIH Reporter | Funded CRISPR gene therapy research |
| 💰 Government grants | Grants.gov | Federal quantum computing R&D awards |
| 👔 Job market signals | Job Market Intelligence | Demand for Rust systems engineers |
| 🏢 Company intelligence | Company Deep Research | Startup headcount and funding trajectories |
| 🧩 SaaS intelligence | SaaS Intel | B2B software product landscapes |
| ❓ Technical Q&A | StackExchange | Developer adoption signals |

Why use the Morphogenetic Innovation MCP Server?

Strategy teams, R&D directors, and investment analysts typically piece together technology assessments by hand: pulling patent databases separately, reading academic survey papers, triangulating job-posting trends, and applying qualitative judgment. This process takes days per domain, is inconsistent across analysts, and produces narrative output that is hard to compare across technologies or time periods.

This MCP server automates the entire quantitative layer. Give an AI agent a technology name, and it returns structured output grounded in real data from 16 sources, interpreted through eight mathematical frameworks from evolutionary biology, topology, and causal inference.

- Scheduling — run technology landscape snapshots daily, weekly, or quarterly to track how metrics shift over time
- API access — trigger analysis from Python, JavaScript, or any HTTP client using the Apify API
- Parallel execution — each tool call dispatches 5–12 actors concurrently, reducing wall time compared to sequential data collection
- Monitoring — set Slack or email alerts when runs fail or return anomalous metrics
- Integrations — connect output to Zapier, Make, Google Sheets, or downstream AI pipelines via webhooks

Features

- NK fitness landscape computation with configurable N loci (4–20) and K epistatic interactions (0–N-1), using spin glass coupling constants J_ij drawn from a seeded PRNG for reproducibility
- Spin glass energy calculation using Ising-style ±1 spin representations of binary genotypes, with external field h_i terms for each locus
- Quasi-species error threshold analysis via Perron-Frobenius dominant eigenvalue of the mutation-selection matrix, determining whether a dominant design maintains coherence or crosses into error catastrophe
- Cusp and fold catastrophe detection using Waddington's epigenetic landscape potential V(x) = x⁴/4 + ax²/2 + bx, identifying hysteresis, catastrophic jumps, and developmental channel (attractor) lock-in
- Nelson-Winter firm competition simulation with configurable firm count (10–500) and generations (50–2000), tracking market share, R&D intensity, and Herfindahl-Hirschman Index
- Fleming-Viot measure-valued process tracking population diversity through entropy rate, diversity index, and concentration parameter across simulation generations
- Patent citation topology via path homology computing chain complexes, boundary operators, Betti numbers (β₀, β₁, β₂), and persistent homology intervals with birth-death filtration
- Causal mediation via TMLE (Targeted Maximum Likelihood Estimation) decomposing the funding-to-innovation effect into Natural Direct Effect and Natural Indirect Effect with clever covariate targeting
- Kingman's coalescent genealogy reconstruction computing MRCA (Most Recent Common Ancestor) year, Watterson's estimator θ_W, and Tajima's D statistic for selection detection across technology lineages
- S-curve logistic disruption timing fitting L/(1+exp(-k·(t-t₀))) via Levenberg-Marquardt with CUSUM change-point detection, outputting current phase, inflection point year, and time to maturity
- Parallel actor orchestration — all 16 data sources dispatched concurrently via runActorsParallel, with per-tool timeout controls (default 180s per actor)
- Mulberry32 seeded PRNG ensuring reproducible landscape generation from the same technology corpus across different runs

Use cases for technology landscape analysis

R&D strategy and portfolio decisions

Chief Technology Officers and R&D directors need to decide where to allocate research budgets across competing technology bets. Run map_fitness_landscape to understand landscape ruggedness: a highly epistatic domain (high K) traps R&D in local optima, making incremental investment risky, while a smooth landscape rewards sustained incremental development. Combine with assess_funding_to_innovation to verify whether your funding model actually drives output.

Investment and venture due diligence

Investors evaluating technology companies need to know whether a technology is approaching maturity or still in its acceleration phase. Run forecast_disruption_timing to get the S-curve phase, inflection point, and estimated time to saturation across all 16 signals simultaneously. Combine with simulate_evolutionary_dynamics to model which firm archetypes survive Schumpeterian creative destruction in the target industry.

Competitive intelligence and patent landscape mapping

Patent counsel and IP strategists need to understand the structural topology of a patent space before filing or licensing. Run analyze_patent_topology to extract Betti numbers from the USPTO/EPO/EUIPO citation DAG, identifying knowledge loops (β₁ cycles indicating mature sub-fields) and voids (β₂ indicating unexploited white space). Hub node identification surfaces the highest-betweenness patents in the landscape.

Technology foresight and scenario planning

Corporate foresight teams and government science advisors need to anticipate whether a technology will undergo a sudden regime shift. Run predict_technology_trajectory to detect cusp bifurcations and hysteresis in the Waddington landscape, quantifying the magnitude of potential catastrophic jumps between technology regimes. Combine with detect_innovation_bifurcation to assess whether the dominant design is stable or approaching quasi-species error catastrophe.

Academic and research trend analysis

University tech transfer offices and research funders need to understand whether a research field has a converging dominant paradigm or remains in a period of competing designs. Run compute_error_threshold to compute Tajima's D across technology lineages: negative D indicates purifying selection toward a dominant design, positive D indicates balancing selection with coexisting alternatives.

AI agent tool augmentation

Developers building AI research agents can connect this MCP server to give their agent quantitative technology analysis capabilities alongside qualitative web browsing. The agent calls a single tool with a technology name and receives structured JSON with mathematical metrics, human-readable interpretation strings, and data source provenance — ready to cite, summarize, or chain into downstream reasoning.

How to connect the Morphogenetic Innovation MCP Server

1. Get your endpoint URL — The server runs at https://morphogenetic-innovation-mcp.apify.actor/mcp. No setup required; it is always on via Apify Standby mode.
2. Add to your AI client — Paste the configuration below into Claude Desktop, Cursor, or any MCP-compatible client. Replace the placeholder with your Apify API token if the server requires authentication.
3. Call a tool — Ask your agent: "Map the fitness landscape for quantum computing with N=12, K=4." The agent selects the right tool, passes parameters, and returns structured analysis.
4. Review the output — Each tool returns a JSON object with quantified metrics, a plain-English interpretation string, and a dataSources block showing how many records each underlying actor returned.

Claude Desktop

{
  "mcpServers": {
    "morphogenetic-innovation": {
      "url": "https://morphogenetic-innovation-mcp.apify.actor/mcp"
    }
  }
}

Cursor

{
  "mcpServers": {
    "morphogenetic-innovation": {
      "url": "https://morphogenetic-innovation-mcp.apify.actor/mcp"
    }
  }
}

Windsurf / other MCP clients

Any client that supports the MCP Streamable HTTP transport can connect using:

https://morphogenetic-innovation-mcp.apify.actor/mcp

MCP tools reference

| Tool | Data sources queried | Key output |
|---|---|---|
| map_fitness_landscape | USPTO, EPO, OpenAlex, ArXiv, GitHub | Ruggedness measure, spin glass energy, local optima count, neutral network fraction, gene-technology mapping |
| predict_technology_trajectory | OpenAlex, Finnhub, Job Market, Hacker News, SaaS Intel | Bifurcation type, hysteresis flag, Waddington channels, catastrophe jump magnitude |
| detect_innovation_bifurcation | USPTO, EPO, Semantic Scholar, ArXiv, StackExchange | Error threshold, dominant eigenvalue, quasi-species distribution, phase transition proximity |
| simulate_evolutionary_dynamics | Company Deep Research, Finnhub, SaaS Intel, Job Market, Hacker News, CoinGecko | HHI, survivors with market shares, Schumpeterian destruction rate, Fleming-Viot diversity |
| analyze_patent_topology | USPTO, EPO, EUIPO, Semantic Scholar, OpenAlex | Betti numbers, Euler characteristic, persistent homology intervals, hub patents |
| assess_funding_to_innovation | NIH Reporter, Grants.gov, OpenAlex, Semantic Scholar, USPTO | TMLE estimate, direct/indirect effects, mediation proportion, 95% CI |
| compute_error_threshold | OpenAlex, ArXiv, GitHub, StackExchange, Hacker News | Tajima's D, Watterson's θ_W, MRCA year, coalescent times, tree height |
| forecast_disruption_timing | All 16 sources | S-curve phase, inflection point, time to maturity, disruption probability, change points |

Input parameters

All tools accept these parameters via the MCP tool call interface:

map_fitness_landscape

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| technology | string | Yes | — | Technology domain to analyze (e.g., "quantum computing", "CRISPR") |
| N | number | No | 12 | Number of loci (technology components). Range: 4–20 |
| K | number | No | 4 | Epistatic interactions per locus. Higher K = more rugged landscape. Range: 0–N-1 |
| maxResults | number | No | 20 | Max results fetched per data source. Range: 5–50 |

predict_technology_trajectory

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| technology | string | Yes | — | Technology to analyze |
| rdInvestmentLevel | number | No | 0.5 | Splitting factor for catastrophe potential. Range: -3 to 3 |
| competitionLevel | number | No | 0.2 | Normal factor (market competition intensity). Range: -3 to 3 |
| maxResults | number | No | 15 | Max results per source. Range: 5–30 |

detect_innovation_bifurcation

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| technology | string | Yes | — | Technology domain |
| sequenceLength | number | No | 8 | Genome length (number of technology components). Range: 4–12 |
| mutationRate | number | No | 0.05 | Per-site mutation rate (innovation rate per component). Range: 0.001–0.5 |
| maxResults | number | No | 20 | Max results per source. Range: 5–40 |

simulate_evolutionary_dynamics

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| industry | string | Yes | — | Industry to simulate (e.g., "cloud computing", "electric vehicles") |
| firmCount | number | No | 100 | Number of firms in simulation. Range: 10–500 |
| generations | number | No | 500 | Simulation time steps. Range: 50–2000 |
| maxResults | number | No | 15 | Max results per source. Range: 5–30 |

analyze_patent_topology

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| technology | string | Yes | — | Patent domain to analyze |
| maxResults | number | No | 25 | Max results per source. Range: 10–50 |

assess_funding_to_innovation

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| technology | string | Yes | — | Technology or research area |
| mediator | string | No | "research_output" | Mediating variable (e.g., "research_output", "talent_pipeline", "infrastructure") |
| maxResults | number | No | 15 | Max results per source. Range: 5–30 |

compute_error_threshold

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| technology | string | Yes | — | Technology domain |
| effectivePopulationSize | number | No | 500 | Number of active R&D groups. Range: 10–10000 |
| mutationRate | number | No | 0.01 | Innovation rate per lineage per generation. Range: 0.0001–0.1 |
| maxResults | number | No | 20 | Max results per source. Range: 5–50 |

forecast_disruption_timing

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| technology | string | Yes | — | Technology to forecast |
| maxResults | number | No | 15 | Max results per source. Range: 5–30 |

Output examples

map_fitness_landscape — quantum computing

{
  "technology": "quantum computing",
  "parameters": { "N": 12, "K": 4 },
  "landscape": {
    "N": 12,
    "K": 4,
    "landscapeSize": 4096,
    "globalOptimum": { "genotype": "101101001011", "fitness": 0.847 },
    "localOptima": [
      { "genotype": "011001001011", "fitness": 0.791, "basinSize": 312 },
      { "genotype": "101100101011", "fitness": 0.768, "basinSize": 248 },
      { "genotype": "001101001111", "fitness": 0.743, "basinSize": 189 }
    ],
    "ruggednessMeasure": 0.72,
    "spinGlassEnergy": -3.41,
    "ultrametricDistance": 0.63,
    "correlationLength": 1.8,
    "neutralNetworkFraction": 0.14,
    "fitnessDistribution": { "mean": 0.512, "variance": 0.031, "skewness": 0.18 },
    "technologyMapping": [
      { "gene": "locus_0", "technology": "Superconducting qubit coherence", "contribution": 0.089 },
      { "gene": "locus_1", "technology": "Error correction codes", "contribution": 0.076 },
      { "gene": "locus_2", "technology": "Gate fidelity", "contribution": 0.071 }
    ],
    "interpretation": "HIGHLY RUGGED landscape (3 local optima). Innovation faces many traps. K=4 epistatic interactions create spin glass frustration. Correlation length 1.8 suggests very short predictability horizon."
  },
  "dataSources": {
    "patents": 38,
    "papers": 34,
    "repos": 19,
    "totalTechnologies": 91
  }
}

forecast_disruption_timing — large language models

{
  "technology": "large language models",
  "forecast": {
    "technology": "large language models",
    "currentPhase": "acceleration",
    "sCurveParameters": { "L": 1.0, "k": 0.84, "x0": 2023.4, "r2": 0.91 },
    "inflectionPoint": 2023,
    "currentPosition": 0.48,
    "timeToMaturity": 4.2,
    "changePoints": [
      { "year": 2020, "magnitude": 0.31, "direction": "acceleration" },
      { "year": 2022, "magnitude": 0.58, "direction": "acceleration" }
    ],
    "disruptionProbability": 0.73,
    "adoptionVelocity": 0.19,
    "saturationLevel": 0.48,
    "confidenceBand": { "lower": 0.39, "upper": 0.57 },
    "interpretation": "Technology \"large language models\" is in ACCELERATION phase (48.0% of saturation). S-curve fit: L=1.0, k=0.84, inflection at 2023 (R²=0.91). Time to maturity: 4.2 years. Disruption probability: 73.0%. Change points detected at 2020 (acceleration), 2022 (acceleration)."
  },
  "dataSources": {
    "patents": 24,
    "papers": 41,
    "repos": 28,
    "discussions": 31,
    "financial": 9,
    "jobs": 22,
    "companies": 11,
    "grants": 6,
    "totalSignals": 172
  }
}

simulate_evolutionary_dynamics — electric vehicles

{
  "industry": "electric vehicles",
  "parameters": { "firmCount": 100, "generations": 500 },
  "simulation": {
    "firmCount": 23,
    "generations": 500,
    "survivors": [
      { "id": "firm_04", "marketShare": 0.31, "productivity": 2.14, "rdIntensity": 0.18, "generation": 1 },
      { "id": "firm_11", "marketShare": 0.19, "productivity": 1.87, "rdIntensity": 0.22, "generation": 3 },
      { "id": "firm_07", "marketShare": 0.12, "productivity": 1.63, "rdIntensity": 0.09, "generation": 2 }
    ],
    "herfindahlIndex": 0.28,
    "industryProductivity": 1.74,
    "innovationRate": 0.043,
    "imitationRate": 0.029,
    "flemingViotMeasure": {
      "entropyRate": 2.81,
      "diversityIndex": 0.67,
      "concentrationParameter": 0.41
    },
    "coalescentTimes": [12, 38, 71, 155],
    "genealogyDepth": 8,
    "schumpeterianCreativeDestruction": 0.77,
    "technologyFrontier": 2.14,
    "interpretation": "CONCENTRATED MARKET: HHI=0.28 indicates oligopoly. Top firm has 31.0% share. Schumpeterian destruction rate: 77.0%. Technology frontier at 2.14."
  }
}

Output fields

Common fields (all tools)

| Field | Type | Description |
|---|---|---|
| technology / industry | string | Input query echoed back |
| parameters | object | Echoed input parameters for reproducibility |
| dataSources | object | Record count from each underlying actor |
| *.interpretation | string | Plain-English summary of the quantitative result |

map_fitness_landscape fields

| Field | Type | Description |
|---|---|---|
| landscape.N | number | Number of loci used |
| landscape.K | number | Epistatic interactions per locus |
| landscape.landscapeSize | number | Total genotypes evaluated (2^N) |
| landscape.globalOptimum.genotype | string | Binary string of the fittest genotype |
| landscape.globalOptimum.fitness | number | Fitness score 0–1 |
| landscape.localOptima[] | array | Non-global fitness peaks with basin sizes |
| landscape.ruggednessMeasure | number | 0–1 scale; >0.7 = highly rugged |
| landscape.spinGlassEnergy | number | Ising spin glass Hamiltonian value |
| landscape.correlationLength | number | Predictability horizon in landscape steps |
| landscape.neutralNetworkFraction | number | Fraction of neighbors with similar fitness |
| landscape.technologyMapping[] | array | Gene-to-technology labels with contribution scores |

forecast_disruption_timing fields

| Field | Type | Description |
|---|---|---|
| forecast.currentPhase | string | One of: emergence, growth, acceleration, maturity, saturation |
| forecast.sCurveParameters.L | number | Saturation ceiling of logistic curve |
| forecast.sCurveParameters.k | number | Growth rate parameter |
| forecast.sCurveParameters.x0 | number | Inflection point year |
| forecast.sCurveParameters.r2 | number | Goodness-of-fit (0–1) |
| forecast.currentPosition | number | Current adoption level as fraction of saturation |
| forecast.timeToMaturity | number | Estimated years to 90% saturation |
| forecast.disruptionProbability | number | 0–1 probability of near-term disruption |
| forecast.changePoints[] | array | CUSUM-detected acceleration/deceleration events with year |
| forecast.confidenceBand | object | Lower and upper bounds on current position estimate |

simulate_evolutionary_dynamics fields

| Field | Type | Description |
|---|---|---|
| simulation.firmCount | number | Surviving firms after selection |
| simulation.survivors[] | array | Top firms with market share, productivity, R&D intensity |
| simulation.herfindahlIndex | number | HHI market concentration (>0.25 = oligopoly) |
| simulation.flemingViotMeasure | object | Entropy rate, diversity index, concentration parameter |
| simulation.schumpeterianCreativeDestruction | number | Fraction of initial firms eliminated |
| simulation.genealogyDepth | number | Depth of technology lineage tree |

analyze_patent_topology fields

| Field | Type | Description |
|---|---|---|
| topology.bettiNumbers | array | β₀ (components), β₁ (cycles), β₂ (voids) |
| topology.eulerCharacteristic | number | χ = β₀ - β₁ + β₂ |
| topology.persistentHomologyIntervals[] | array | Birth-death pairs by dimension |
| topology.hubNodes[] | array | Highest-betweenness patents with degree and betweenness scores |
| topology.topologicalComplexity | number | Composite complexity score |

assess_funding_to_innovation fields

| Field | Type | Description |
|---|---|---|
| causalAnalysis.totalEffect | number | Total causal effect of funding on innovation |
| causalAnalysis.directEffect | number | Natural Direct Effect (bypassing mediator) |
| causalAnalysis.indirectEffect | number | Natural Indirect Effect (through mediator) |
| causalAnalysis.mediationProportion | number | Fraction of total effect mediated |
| causalAnalysis.tmleEstimate | number | TMLE-corrected causal estimate |
| causalAnalysis.confidenceInterval | array | 95% CI as [lower, upper] |
| causalAnalysis.pathways[] | array | Named causal pathways with effect sizes and p-values |

compute_error_threshold fields

| Field | Type | Description |
|---|---|---|
| coalescent.tajimaDStatistic | number | <-1.5 purifying, >1.5 balancing, ~0 neutral |
| coalescent.wattersonsEstimator | number | θ_W population mutation rate estimate |
| coalescent.mostRecentCommonAncestor | object | MRCA id and estimated year |
| coalescent.treeHeight | number | Total genealogy depth |
| coalescent.coalescentTimes[] | array | Pairwise coalescence times |
| coalescent.genealogy[] | array | Full lineage tree with branch lengths |

How much does it cost to analyze technology landscapes?

This MCP server uses pay-per-event pricing — you pay per tool call. The Apify Free plan includes $5 of monthly platform credits, enough for roughly 125 tool calls at zero cost.

| Scenario | Tool calls | Cost per call | Estimated total |
|---|---|---|---|
| Single technology probe | 1 | ~$0.04 | ~$0.04 |
| Technology assessment (all 8 tools) | 8 | ~$0.04 | ~$0.32 |
| Weekly portfolio review (10 technologies) | 80 | ~$0.04 | ~$3.20 |
| Monthly deep-scan (50 technologies) | 400 | ~$0.04 | ~$16.00 |
| Enterprise continuous monitoring | 2,000+ | ~$0.04 | ~$80.00 |

forecast_disruption_timing queries all 16 sources and costs slightly more ($250–$400 in underlying actor credits) than tools that query fewer sources. Set a maximum spending limit per run in the Apify console to cap costs for any single session.

Compare this to hiring a research analyst to produce equivalent quantitative analysis: a single technology landscape assessment from a consulting firm runs $5,000–$50,000. The Apify Free tier covers initial exploration at no cost.

Using the API

You can trigger the MCP server programmatically through the standard Apify API without an MCP client.

Python

from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("ryanclinton/morphogenetic-innovation-mcp").call(run_input={})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():

    print(f"Tool result: {item}")

JavaScript

import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: "YOUR_API_TOKEN" });
const run = await client.actor("ryanclinton/morphogenetic-innovation-mcp").call({});
const { items } = await client.dataset(run.defaultDatasetId).listItems();

for (const item of items) {

    console.log("Tool result:", item);

}

cURL

```bash