Morphogenetic Innovation MCP Server

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?

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

Input parameters

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

map_fitness_landscape

predict_technology_trajectory

detect_innovation_bifurcation

simulate_evolutionary_dynamics

analyze_patent_topology

assess_funding_to_innovation

compute_error_threshold

forecast_disruption_timing

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)

map_fitness_landscape fields

forecast_disruption_timing fields

simulate_evolutionary_dynamics fields

analyze_patent_topology fields

assess_funding_to_innovation fields

compute_error_threshold fields

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.

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

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~morphogenetic-innovation-mcp/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{}'

# Fetch results (replace DATASET_ID from the run response)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

For direct MCP calls from code, use the Streamable HTTP endpoint:

import httpx

response = httpx.post(
    "https://morphogenetic-innovation-mcp.apify.actor/mcp",
    json={
        "jsonrpc": "2.0",
        "id": 1,
        "method": "tools/call",
        "params": {
            "name": "forecast_disruption_timing",
            "arguments": {
                "technology": "solid-state batteries",
                "maxResults": 15
            }
        }
    }
)
print(response.json())

How the Morphogenetic Innovation MCP Server works

Phase 1: Parallel data collection

Each tool call dispatches between 5 and 12 Apify actors concurrently using runActorsParallel. For example, forecast_disruption_timing queries all 16 sources at once — USPTO, EPO, OpenAlex, ArXiv, GitHub, Hacker News, Finnhub, Job Market, Company Deep Research, SaaS Intel, StackExchange, and NIH — with a 180-second timeout per actor. Raw items are extracted into a uniform TechnologyNode structure carrying id, name, type, citations, year, field, funding amount, and market cap.

Phase 2: Graph construction

Technology nodes are sorted chronologically and connected into a directed citation graph using a temporal proximity and field-similarity heuristic: edges are created between nodes within 3 years of each other or in the same field, weighted by 1/(1 + time distance). This directed acyclic graph is the substrate for both the path homology computation and the genealogy algorithms.

Phase 3: Mathematical analysis

Each tool invokes a dedicated computation function from scoring.ts:

• computeNKFitnessLandscape — builds the epistatic interaction table, generates spin glass coupling constants J_ij, enumerates the 2^N genotype space, identifies local optima via single-step neighborhood checks, and computes autocorrelation length by sampling random adaptive walks.
• computeQuasiSpecies — constructs the mutation-selection matrix Q·W where Q is the per-bit mutation matrix and W is the diagonal fitness matrix, extracts the Perron-Frobenius dominant eigenvalue, and computes information content from the steady-state distribution.
• computePathHomology — builds chain complexes C_0, C_1, C_2 from the node-edge-triangle structure, applies boundary operators ∂_1 and ∂_2, and counts Betti numbers from kernel/image dimensions.
• computeBifurcationAnalysis — evaluates the cusp catastrophe potential at a grid of control parameter values, detects fold points where ∂V/∂x = 0 and ∂²V/∂x² = 0 simultaneously, and classifies Waddington channel attractors by basin depth.
• computeNelsonWinter — runs a discrete-time agent simulation where firms draw innovation shocks from a Poisson process, compete via relative productivity for market share, and exit below a survival threshold. Fleming-Viot diversity is tracked via empirical entropy of the market share distribution.
• computeCausalMediation — implements the TMLE targeting step with a clever covariate H(A,W) = A/g(W) - (1-A)/(1-g(W)), applies the fluctuation parameter ε update, and bootstraps confidence intervals for the natural direct and indirect effects.
• computeCoalescent — simulates Kingman's coalescent backward in time, drawing coalescence times from Exp(k(k-1)/(2Ne)) at each step, assigns MRCA year by subtracting tree height from the most recent data year, and computes Tajima's D from segregating site counts.
• computeDisruptionTiming — bins technology data by year, fits the logistic growth curve via Levenberg-Marquardt, applies CUSUM on the residuals to detect change points, and maps current position to the five S-curve phases using fixed thresholds (emergence <10%, growth 10–30%, acceleration 30–60%, maturity 60–90%, saturation >90%).

Phase 4: Interpretation and output

Each tool assembles a JSON response containing the raw numeric result, a branching interpretation string that selects the most informative summary based on metric thresholds (e.g., HHI > 0.25 triggers the oligopoly narrative), and a dataSources block recording how many records each underlying actor returned. This provenance data lets downstream consumers assess data quality and rerun with higher maxResults if needed.

Tips for best results

1. Start with forecast_disruption_timing for any new technology. It queries all 16 sources and gives you the broadest signal. Use the S-curve phase to decide which deeper tools to run next: emerging technologies benefit most from map_fitness_landscape; mature technologies benefit from simulate_evolutionary_dynamics.

2. Calibrate K for the technology domain. Software technologies typically have K=2–4 (modular, low epistasis). Biotechnology and materials science have K=6–10 (high epistasis, many interdependencies). Setting K too low underestimates ruggedness; setting it too high makes the landscape computationally extreme.

3. Use detect_innovation_bifurcation before large R&D bets. If aboveThreshold: true is returned, the dominant design is fragmenting. Entering a market in error catastrophe means competing against a cloud of variants, not a single incumbent — a fundamentally different competitive strategy.

4. Interpret Tajima's D carefully. Run compute_error_threshold for both the target technology and a known stable technology in the same field as a baseline. Relative D values are more informative than absolute thresholds.

5. Increase maxResults for high-stakes analysis. The default of 15–20 per source is sufficient for directional signals. For due diligence or published research, raise maxResults to 40–50 and expect run times of 3–6 minutes per tool call.

6. Chain assess_funding_to_innovation with grant search data. The tool queries NIH Reporter and Grants.gov for funding. For technologies with significant DARPA or EU Horizon funding, supplement by running WHOIS Domain Lookup or Company Deep Research to capture private investment flows not in government databases.

7. Save dataSources from each run. If a tool returns sparse data (e.g., patents: 2), the mathematical results are less reliable. Flag runs with fewer than 10 total nodes for review before acting on the output.

Combine with other Apify actors

Limitations

• NK landscape results are seeded from the data corpus. Running the same technology query at different times may produce different landscape shapes if the underlying patent or paper data changes. The mulberry32 seed is derived from node IDs, so identical input data produces identical landscapes.
• Quasi-species error threshold assumes a flat fitness landscape for the threshold calculation (1/sequence_length). Real innovation landscapes are more complex — treat the threshold as a heuristic rather than a precise boundary.
• S-curve fitting requires sufficient historical data. Technologies with fewer than 5 years of patent or paper history produce logistic fits with low R² values. The confidenceBand field reflects this uncertainty, but very new technologies should be treated as directional only.
• Nelson-Winter simulation is stylized. The model captures canonical Schumpeterian dynamics but does not represent specific named companies or incorporate regulatory constraints, network effects, or platform dynamics explicitly.
• Causal mediation via TMLE uses proxy variables. Citation counts and grant amounts are proxies for innovation output; unmeasured confounders (e.g., talent concentration, geography) are not directly controlled. The confounders array flags known sources of bias.
• Path homology is approximated. Full persistent homology computation over thousands of nodes is computationally expensive. The implementation bounds the chain complex to the immediate neighborhood of the citation DAG, which may undercount high-dimensional topological features.
• Financial data (Finnhub) covers public equities only. Private company valuations and pre-IPO startup activity are not captured, making this signal less representative for early-stage technology landscapes.
• All analyses are point-in-time. This server does not store previous results or compute longitudinal trajectories automatically. Use Apify scheduling to run the same tool call periodically and track metric evolution yourself.

Integrations

• Zapier — trigger a technology landscape analysis on a schedule and push results to a Google Sheet or Notion database for weekly review
• Make — build a pipeline that runs forecast_disruption_timing on a watchlist of technologies and sends a Slack alert when any crosses from growth to acceleration phase
• Google Sheets — export disruption timing and HHI metrics from multiple technology runs into a comparative tracking sheet
• Apify API — call any tool programmatically from Python or JavaScript research pipelines, embedding quantitative technology analysis into AI agent workflows
• Webhooks — fire a webhook when a run completes to trigger downstream processing in your own infrastructure
• LangChain / LlamaIndex — connect this MCP server to LangChain agents via the Apify MCP integration so your RAG pipeline can call analyze_patent_topology or assess_funding_to_innovation as a retrieval step

Troubleshooting

Tool returns very few data points in dataSources. The underlying actor for that source may be returning sparse results for an obscure or recently coined technology name. Try a broader query term (e.g., "machine learning" instead of "sparse autoencoders") or increase maxResults. If a source consistently returns zero results, it may be temporarily unavailable — the tool degrades gracefully and completes with whatever data is available.

Run times out before returning results. Each actor call has a 180-second timeout. If 5 or more source actors time out simultaneously, the tool may return an empty or partial result. Reduce maxResults to 10 or fewer per source to speed up individual actor calls. The forecast_disruption_timing tool is the most data-intensive; consider running it with maxResults: 8 for faster results at the cost of signal coverage.

Fitness landscape ruggednessMeasure is always near 0.5. This occurs when the technology corpus has very few nodes (fewer than 8). With a small node set, the seeded landscape generation has limited variation. Ensure the technology name returns at least 10 results across patents and papers by using a well-established domain name.

TMLE estimate in assess_funding_to_innovation is near zero. If both NIH Reporter and Grants.gov return no results, the funding dataset is empty and TMLE defaults to a near-zero estimate. Verify that the technology name appears in federal grant databases. Government-funded research areas (biomedical, defense, energy) work best; purely commercial software domains may have insufficient grant coverage.

S-curve R² is below 0.3 in forecast_disruption_timing. Low fit quality indicates insufficient historical spread in the data — all records cluster in a narrow year range. This is common for very new technologies (post-2022) or very old ones where patent and paper databases have sparse records. Treat the phase classification as approximate and prioritize the changePoints output instead.

Responsible use

• This server accesses publicly available data from patent offices, academic repositories, government grant databases, and public financial APIs.
• Respect the terms of service of each underlying data source: USPTO, EPO, EUIPO, OpenAlex, ArXiv, Semantic Scholar, GitHub, Finnhub, CoinGecko, NIH Reporter, and Grants.gov.
• Mathematical model outputs are analytical tools, not investment advice. Do not represent model outputs as financial forecasts to investors or regulators.
• Causal mediation results should be interpreted with domain expertise; observational data cannot replace randomized controlled experiments for establishing causality.
• For guidance on web scraping and data collection legality, see Apify's guide.

FAQ

How does technology landscape analysis with this MCP server differ from a standard patent search? A standard patent search returns a list of documents. This server applies eight mathematical frameworks — NK fitness landscapes, path homology, Kingman's coalescent, and others — to the same raw data to produce quantified metrics: ruggedness scores, Betti numbers, Tajima's D, and disruption timing. The output is structured JSON that an AI agent or analyst can reason over, not a document list to read manually.

How many technology domains can I analyze in one session? There is no hard limit per session. Each tool call is independent. Analyzing 10 technologies across all 8 tools would require 80 tool calls at approximately $3.20 in compute credits. Use the spending limit feature in the Apify console to cap total cost for a session.

How long does a typical tool call take? Tools that query 5–6 sources (e.g., map_fitness_landscape, detect_innovation_bifurcation) typically complete in 60–120 seconds. forecast_disruption_timing, which queries all 16 sources, typically takes 90–180 seconds depending on how quickly individual actors respond.

What does a rugged fitness landscape mean for technology strategy? High K (epistatic interactions) means many local optima exist in the technology space. R&D teams can get trapped on suboptimal designs that are locally superior but globally inferior. Rugged landscapes favor modular architectures that reduce K by isolating dependencies between components. Landscape ruggedness above 0.7 is a signal to invest in architectural exploration rather than incremental improvement.

What is error catastrophe in a technology context? When the per-component innovation rate exceeds the quasi-species error threshold (approximately 1/sequence_length), the dominant technology design loses its information content and fragments into a cloud of competing variants. This is analogous to RNA virus quasi-species theory. A technology in error catastrophe does not have a clear dominant design to compete against — the competitive landscape is fragmented and fast-moving.

How is Tajima's D interpreted for technology lineages? Negative D (below -1.5) indicates purifying selection: technology lineages are converging toward a dominant design, and inferior variants are being eliminated. Positive D (above +1.5) indicates balancing selection: multiple designs coexist, each viable in different niches. Values near zero indicate neutral drift with no strong selective pressure.

Can I use this server to analyze cryptocurrency or blockchain technologies? Yes. The server includes CoinGecko as a data source, which covers digital asset market data. For blockchain-specific technology analysis, the GitHub and StackExchange sources provide strong signals. simulate_evolutionary_dynamics applied to "decentralized finance" or "layer 2 scaling" will incorporate both crypto market data and developer activity.

Is it legal to use this MCP server's data for investment decisions? The server accesses publicly available data from government databases, academic repositories, and public APIs. Using the output for internal investment analysis is generally permissible. However, the server's outputs are quantitative models, not licensed financial data products — consult your compliance team before using outputs in regulated investment contexts. See Apify's legal guide for general data use guidance.

How accurate is the S-curve disruption timing forecast? Accuracy depends on data coverage. For technologies with 10+ years of patent and paper history and broad coverage across multiple data sources, R² values above 0.8 are typical. For emerging technologies (fewer than 5 years of data), R² drops significantly and the confidenceBand widens. The changePoints output from CUSUM detection is often more actionable than the raw S-curve parameters for recent technologies.

What happens if I request a technology that no data source knows about? All source actors degrade gracefully — if a query returns zero results, the actor returns an empty array. The mathematical functions handle empty or minimal node sets by returning conservative estimates. The dataSources block in the output will show which sources returned data so you can assess result reliability. Very new or highly specialized technology names may benefit from a broader parent domain query.

Can I schedule this MCP server to run periodically and track metrics over time? Yes. Use Apify's scheduling feature to trigger a run on any cadence. The server itself is stateless — it does not store previous results. Store outputs in Google Sheets via the Apify integration, or use webhooks to push results to your own database for longitudinal tracking.

How is this different from using a general-purpose AI agent to research a technology? A general AI agent reasons qualitatively from text. This server computes quantified mathematical metrics from structured data across 16 sources simultaneously. The output includes specific numbers — NK ruggedness 0.72, HHI 0.28, Tajima's D -2.1 — that are reproducible, comparable across technologies, and citable in research or investment memos. It is designed to complement, not replace, qualitative AI reasoning.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.