Tech Scouting Report MCP

Technology commercialization intelligence for AI agents.

Scout 8 data sources in parallel to evaluate research momentum, patent landscape, funding validation, and Technology Readiness Level (TRL). Get a composite score and investment verdict for any technology in seconds.

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┌─────────────────────────────────────────────────────────────────┐
│  TECH SCOUTING REPORT MCP                                       │
│                                                                 │
│  8 Data Sources  │  Parallel Fetch  │  Composite Scoring       │
│                                                                 │
│  OpenAlex: 250M+ papers    USPTO Patents                       │
│  Semantic Scholar           EPO Patents                         │
│  arXiv Preprints           NIH/Grants.gov                       │
│                            ClinicalTrials.gov                   │
└─────────────────────────────────────────────────────────────────┘

Quick Start

{
  "tool": "tech_scout_report",
  "arguments": {
    "technology": "CRISPR gene editing",
    "field": "molecular biology",
    "region": "US"
  }
}

Tools

1. tech_scout_report

Comprehensive technology scouting report with full scoring breakdown.

When to call: You need a complete investment assessment for a technology.

Example AI prompt: "Run a tech scouting report on mRNA therapeutics for my investment pipeline."

Input:

{
  "technology": "string",    // Required: Technology name
  "field": "string",        // Optional: Research field
  "region": "string"        // Optional: US, EU, Asia
}

Output:

{
  "compositeScore": 72.5,
  "verdict": "STRONG_CANDIDATE",
  "researchMomentum": { "score": 18.5, "citationVelocity": 12.3, ... },
  "patentCommerc": { "score": 22.0, "patentCount": 45, ... },
  "fundingValidation": { "score": 15.0, "nihGrants": 12, ... },
  "trlAssessment": { "estimatedTRL": 7, "trlLevel": "MEDIUM", ... },
  "allSignals": ["HIGH_TRL keyword: commercial", "45 USPTO patents found", ...],
  "recommendations": ["Prioritize licensing discussions", ...],
  "metadata": { "openAlexPapers": 234, "semanticPapers": 45, ... }
}

PPE: 8

2. tech_scout_research_momentum

Analyzes research momentum from academic publications and preprints.

When to call: You want to understand citation velocity and publication trends.

Example AI prompt: "What's the research momentum for quantum computing?"

Input: { technology: string, field?: string, region?: string }

Output:

{
  "technology": "quantum computing",
  "openAlexPapers": [...],
  "semanticScholarPapers": [...],
  "arxivPreprints": [...],
  "citationVelocity": 15.2,
  "momentumScore": 32.5
}

PPE: 3

3. tech_scout_patent_landscape

Scouts USPTO and EPO patent databases for technology patents.

When to call: You need to understand the patent landscape and freedom to operate.

Example AI prompt: "Map the patent landscape for solid-state batteries."

Input: { technology: string, field?: string, region?: string }

Output:

{
  "technology": "solid-state batteries",
  "usptoPatents": [...],
  "epoPatents": [...],
  "authorInventorMatches": 0,
  "patentScore": 28.5
}

PPE: 2

4. tech_scout_funding_landscape

Scouts NIH RePORTer, Grants.gov, and ClinicalTrials.gov for funding validation.

When to call: You want to validate that funding supports technology development.

Example AI prompt: "What's the funding landscape for Alzheimer's drug development?"

Input: { technology: string, field?: string, region?: string }

Output:

{
  "technology": "Alzheimer's treatment",
  "nihGrants": [...],
  "govGrants": [...],
  "clinicalTrials": [...],
  "fundingScore": 45.0
}

PPE: 3

5. tech_scout_trl_assessment

Assesses Technology Readiness Level via keyword analysis and milestone tracking.

When to call: You need to evaluate the maturity of a technology for commercialization.

Example AI prompt: "What's the TRL for autonomous vehicle sensor fusion?"

Input: { technology: string, field?: string, region?: string }

Output:

{
  "technology": "autonomous vehicles",
  "estimatedTRL": 7,
  "trlLevel": "MEDIUM",
  "highTrlKeywordsFound": 5,
  "medTrlKeywordsFound": 3,
  "patentGrantRatio": 62,
  "highestClinicalPhase": "None",
  "sbirPhase2Count": 4,
  "trlScore": 42.5,
  "signals": [...]
}

PPE: 3

6. tech_scout_batch

Batch scout multiple technologies for rapid portfolio analysis.

When to call: You need to compare multiple technologies at once.

Example AI prompt: "Rank these 5 technologies by investment potential: mRNA vaccines, CRISPR, solid-state batteries, quantum computing, neural interfaces."

Input:

{
  "technologies": ["mRNA vaccines", "CRISPR", "solid-state batteries", "quantum computing", "neural interfaces"],
  "field": "biotechnology",
  "region": "US"
}

Output:

{
  "results": [
    { "technology": "mRNA vaccines", "compositeScore": 78.5, "verdict": "INVEST_NOW", "rank": 1 },
    { "technology": "CRISPR", "compositeScore": 72.0, "verdict": "STRONG_CANDIDATE", "rank": 2 },
    ...
  ],
  "rankedBy": "compositeScore"
}

PPE: 8 per technology

Scoring Model

Weighted Composite Score

Component	Weight	Data Sources
Research Momentum	20%	OpenAlex, Semantic Scholar, arXiv
Patent Commercialization	25%	USPTO, EPO
Funding Validation	25%	NIH, Grants.gov, ClinicalTrials.gov
TRL Assessment	30%	Keyword analysis, patent grants, clinical phases

Research Momentum (20%)

Citation velocity: (totalCitations / publicationCount) * 2, capped at 35pts
+10 pts if >50% publications from 2023+
Semantic Scholar influential citations: 2x weight, capped at 25pts
arXiv preprints: 3pts each, capped at 25pts
+15 amplifier if avg citations >10 AND preprints >3

Patent Commercialization (25%)

USPTO: 4pts/granted, 2pts/application, +2pts if filed 2022+, capped at 35pts
EPO: 4pts/granted (kind B/A), capped at 25pts
Author-inventor cross-ref: 5pts/match, capped at 25pts

Funding Validation (25%)

NIH: 3pts/grant, +4pts for R01/R21/R35, +5pts for SBIR/STTR, capped at 35pts
Grants.gov: 3pts each, +$1M bonus (10pts), capped at 25pts
Clinical trials: 4pts each, +5pts Phase 2+ bonus, capped at 25pts

TRL Assessment (30%)

HIGH_TRL keywords (commercial, manufacturing, FDA approved, market, deployed): 4pts each, capped at 30pts
MED_TRL keywords (prototype, validation, proof of concept): 2pts each
LOW_TRL keywords (discovery, fundamental, theoretical): -1pt each
Patent grant ratio: up to 25pts
Phase 3 clinical trials: capped at 25pts

Verdict Logic

Composite Score	OR Condition	Verdict
75+	TRL>=7 AND COMMERCIAL_READY	INVEST_NOW
55-74	-	STRONG_CANDIDATE
35-54	-	MONITOR
15-34	-	TOO_EARLY
<15	-	PASS

Commercial Ready signals: 3+ HIGH_TRL keywords OR (Phase 3 clinical AND >50% patent grant ratio)

Data Sources

Source	Count	API Type
OpenAlex	250M+ papers	REST, no auth
Semantic Scholar	Influential citations	REST, optional API key
arXiv	Preprints	Atom feed, no auth
USPTO Patents	US patents	REST, no auth
EPO Open Patent Services	EU patents	REST, no auth
NIH RePORTer	Federal grants	REST, no auth
Grants.gov	Federal opportunities	REST, no auth
ClinicalTrials.gov	Clinical trials	REST, no auth

How It Compares to CB Insights

Aspect	Our MCP	CB Insights
Price	$2-$8 per report	$50k+/year (enterprise)
Access method	MCP (AI-native)	Web dashboard
Free tier	Yes - pay per call	No
Real-time data	Yes - 8 live API sources	Dashboard (refresh cycles)
Setup time	5 minutes	Weeks (enterprise sales, onboarding)

Why choose our MCP:

MCP protocol is designed for AI agent integration - generate tech scouting reports as part of AI workflows
8 live data sources (OpenAlex, Semantic Scholar, arXiv, USPTO, EPO, NIH, Grants.gov, ClinicalTrials.gov)
Composite scoring combines research momentum, patent landscape, funding validation, and TRL
Batch scouting for portfolio analysis at $8 per technology
No enterprise contract required - start using in minutes

CB Insights alternative: https://www.cbinsights.com/

Pricing

Action	PPE Cost
tech_scout_report	8
tech_scout_research_momentum	3
tech_scout_patent_landscape	2
tech_scout_funding_landscape	3
tech_scout_trl_assessment	3
tech_scout_batch	8 per technology

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    tech-scouting-report-mcp                     │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   Input: { technology, field?, region? }                        │
│                                                                 │
│   ┌─────────────────────────────────────────────────────────┐  │
│   │           Promise.all Parallel Fetch (120s timeout)     │  │
│   │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐      │  │
│   │  │OpenAlex │ │Semantic │ │ arXiv   │ │ USPTO   │      │  │
│   │  │ Papers  │ │ Scholar │ │Preprints│ │ Patents │      │  │
│   │  └─────────┘ └─────────┘ └─────────┘ └─────────┘      │  │
│   │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐      │  │
│   │  │   EPO   │ │   NIH   │ │Grants   │ │Clinical │      │  │
│   │  │ Patents │ │ Grants  │ │  .gov   │ │ Trials  │      │  │
│   │  └─────────┘ └─────────┘ └─────────┘ └─────────┘      │  │
│   └─────────────────────────────────────────────────────────┘  │
│                                                                 │
│   ┌─────────────────────────────────────────────────────────┐  │
│   │                    Scoring Engine                        │  │
│   │  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐   │  │
│   │  │  Research    │  │    Patent    │  │   Funding    │   │  │
│   │  │  Momentum    │  │  Commerc.    │  │  Validation │   │  │
│   │  │   (20%)      │  │    (25%)     │  │    (25%)     │   │  │
│   │  └──────────────┘  └──────────────┘  └──────────────┘   │  │
│   │                     ┌──────────────┐                     │  │
│   │                     │     TRL      │                     │  │
│   │                     │   (30%)      │                     │  │
│   │                     └──────────────┘                     │  │
│   └─────────────────────────────────────────────────────────┘  │
│                                                                 │
│   Output: { compositeScore, verdict, signals, recommendations }│
└─────────────────────────────────────────────────────────────────┘

Cross-Sells

academic-research-mcp

For deep academic literature analysis with citation graphs and institutional networks.

Use when: You need detailed paper-by-paper analysis, citation tracking, and author network mapping.

{
  "tool": "academic_search",
  "arguments": { "query": "CRISPR Cas9 applications", "max_results": 50 }
}

university-research-mcp

For identifying university technologies available for licensing.

Use when: You want to find startups spun out of university research, available licenses, and TTO contacts.

{
  "tool": "university_tech_search",
  "arguments": { "technology": "machine learning", "university": "MIT" }
}

patent-search-mcp

For detailed patent search and analysis with claim parsing.

Use when: You need to do prior art search, patent invalidation analysis, or freedom-to-operate analysis.

{
  "tool": "patent_search",
  "arguments": { "query": "neural network accelerator", "jurisdiction": "US" }
}

Example AI Agent Workflows

Investment Screening

1. tech_scout_batch for top 20 candidate technologies
2. Filter to STRONG_CANDIDATE or INVEST_NOW verdicts
3. tech_scout_report for deep dive on shortlisted tech
4. Cross-reference with patent-search-mcp for FTO analysis

Competitive Intelligence

1. tech_scout_report on competitor technology
2. tech_scout_patent_landscape to map patent portfolio
3. tech_scout_funding_landscape to understand R&D spend
4. Track momentum changes over time

Licensing Evaluation

1. tech_scout_trl_assessment for maturity signals
2. Identify NIH-funded research (tech_scout_funding_landscape)
3. Map inventor networks via academic-research-mcp
4. Cross-reference with university-research-mcp for available licenses

MCP Protocol

This actor implements the MCP (Model Context Protocol) for AI agent integration.

Endpoint: /mcp

Manifest: /mcp/manifest

Request format:

{
  "tool": "tech_scout_report",
  "arguments": { "technology": "CRISPR" }
}

Response format:

{
  "success": true,
  "result": { ... }
}

Deployment

This actor runs in standby mode on Apify, enabling efficient AI agent integration with pay-per-event pricing.

Actor ID: tech-scouting-report-mcp

Pricing: Event-based (PPE)

Status

Created: 2026-04-21
Data sources: 8 (all free APIs, no API keys required for most)
API coverage: OpenAlex, Semantic Scholar, arXiv, USPTO, EPO, NIH, Grants.gov, ClinicalTrials.gov
Scoring model: Weighted composite with TRL override logic

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
.actor		.actor
src		src
.gitignore		.gitignore
PRD.md		PRD.md
README.md		README.md
TUTORIAL.md		TUTORIAL.md
input_schema.json		input_schema.json
llms.txt		llms.txt
package.json		package.json

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Tech Scouting Report MCP

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Quick Start

Tools

1. tech_scout_report

2. tech_scout_research_momentum

3. tech_scout_patent_landscape

4. tech_scout_funding_landscape

5. tech_scout_trl_assessment

6. tech_scout_batch

Scoring Model

Weighted Composite Score

Research Momentum (20%)

Patent Commercialization (25%)

Funding Validation (25%)

TRL Assessment (30%)

Verdict Logic

Data Sources

How It Compares to CB Insights

Pricing

Architecture

Cross-Sells

academic-research-mcp

university-research-mcp

patent-search-mcp

Example AI Agent Workflows

Investment Screening

Competitive Intelligence

Licensing Evaluation

MCP Protocol

Deployment

Status

See Also

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