The Medical Research MCP Suite is an AI-enhanced API that unifies medical research data from ClinicalTrials.gov (400,000+ studies), PubMed (35M+ publications), and FDA databases (80,000+ products) to deliver comprehensive research intelligence and strategic insights.
Core Capabilities:
Clinical Trials Research: Search and retrieve detailed study information by condition, intervention, phase, status, or NCT identifier with AI analysis
Medical Literature Analysis: Search PubMed with advanced filtering by publication type and date ranges for targeted literature reviews
FDA Drug Intelligence: Access drug information, approval status, active ingredients, and adverse event reports with temporal filtering
Cross-Database Strategic Analysis: Generate comprehensive reports combining trials, literature, and FDA data for risk assessment, market opportunity, and competitive intelligence
Drug Safety Profiling: Create complete safety analyses with algorithmic risk scoring across clinical trials and FDA adverse event reports
Competitive Landscape Analysis: Assess market positioning, pipeline status, and competitor dynamics for specific conditions or drugs
Flexible Reporting:
Executive summaries for high-level decision-making (500-3000 tokens)
Modular detailed reports focusing on clinical trials, literature trends, safety data, or market intelligence (1000-15000 tokens)
Multiple output formats including JSON, Markdown, and structured data
Customizable analysis depth (basic, detailed, comprehensive)
Key Features: Intelligent caching for sub-second responses, AI-enhanced insights with algorithmic scoring, cross-database correlation, strategic recommendations, and enterprise-grade performance with rate limiting.
Provides access to 35M+ research papers and literature analysis from PubMed's database, enabling searches and retrieval of medical research publications.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Medical Research MCP Suite comprehensive analysis of pembrolizumab for lung cancer"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI-Enhanced Medical Research API unifying ClinicalTrials.gov, PubMed, and FDA databases with intelligent cross-database analysis.
๐ฌ ClinicalTrials.gov - 400,000+ clinical studies with real-time data
๐ PubMed - 35M+ research papers and literature analysis
๐ FDA Database - 80,000+ drug products and safety data
Cross-Database Analysis - Unique insights from combined data sources
Risk Assessment - Algorithmic safety scoring and recommendations
Competitive Intelligence - Market landscape and pipeline analysis
Strategic Insights - Investment and research guidance
Intelligent Caching - 1-hour clinical trials, 6-hour literature caching
Rate Limiting - Respectful API usage and quota management
Comprehensive Logging - Full audit trails with Winston
Type Safety - Full TypeScript implementation
Testing Suite - Jest with comprehensive coverage
Related MCP server: KnowS MCP Server
Node.js 18+
npm or yarn
git clone https://github.com/eugenezhou/medical-research-mcp-suite.git
cd medical-research-mcp-suite
npm install
cp .env.example .env
npm run buildnpm run devAdd to your claude_desktop_config.json:
{
"mcpServers": {
"medical-research": {
"command": "node",
"args": ["/path/to/medical-research-mcp-suite/dist/index.js"]
}
}
}npm run web
# Visit http://localhost:3000npm test
./test-mcp.sh// Cross-database analysis combining trials + literature + FDA data
const analysis = await comprehensiveAnalysis({
drugName: "pembrolizumab",
condition: "lung cancer",
analysisDepth: "comprehensive"
});
// Returns:
// - Risk assessment scoring
// - Market opportunity analysis
// - Competitive landscape
// - Strategic recommendationsconst trials = await searchTrials({
condition: "diabetes",
intervention: "metformin",
pageSize: 20
});
// Returns real-time data from 400k+ studiesconst safety = await drugSafetyProfile({
drugName: "metformin",
includeTrials: true,
includeFDA: true
});
// Returns comprehensive safety analysisct_search_trials - Enhanced clinical trial search
ct_get_study - Detailed study information by NCT ID
pm_search_papers - PubMed literature discovery
fda_search_drugs - FDA drug database search
fda_adverse_events - Adverse event analysis
research_comprehensive_analysis - Multi-database strategic analysis
research_drug_safety_profile - Safety analysis across all sources
research_competitive_landscape - Market intelligence and pipeline analysis
What would take medical researchers HOURS โ completed in SECONDS:
Traditional Approach | With MCP Suite |
โฐ 4+ hours manual research | โก 30 seconds automated |
๐ Single database queries | ๐ Cross-database correlation |
๐ Manual data compilation | ๐ค AI-enhanced insights |
๐ญ Subjective risk assessment | ๐ Algorithmic scoring |
๐ Limited competitive view | ๐ Complete market landscape |
ROI Calculation: Save 20+ research hours per analysis = $2,000+ in consultant time
# Performance tuning
CACHE_TTL=3600000
MAX_CONCURRENT_REQUESTS=10{
"mcpServers": {
"medical-research": {
"command": "node",
"args": ["/Users/eugenezhou/Code/medical-research-mcp-suite/dist/index.js"],
"env": {
"PUBMED_API_KEY": "your_key_here",
"FDA_API_KEY": "your_key_here"
}
}
}
}โก Sub-second responses with intelligent caching
๐ 99.9% uptime with robust error handling
๐ Scalable architecture for enterprise deployment
๐ก๏ธ Rate limiting prevents API quota exhaustion
๐ Comprehensive logging for debugging and monitoring
# Run full test suite
npm test
# Test individual components
npm run test:clinical-trials
npm run test:pubmed
npm run test:fda
# Integration testing
npm run test:integration
# Quick MCP test
./test-mcp.shnpm install -g @railway/cli
railway login
railway init
railway updocker build -t medical-research-api .
docker run -p 3000:3000 medical-research-apiWorks on any Node.js hosting platform:
Render
DigitalOcean App Platform
AWS ECS/Fargate
Google Cloud Run
Getting Started Guide - Setup and first steps
API Reference - Complete endpoint documentation
Architecture Guide - System design and patterns
Deployment Guide - Production deployment options
Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
WHO International Clinical Trials Registry integration
European Medicines Agency (EMA) database support
Advanced NLP for literature analysis
Real-time safety signal detection
Machine learning models for trial success prediction
Integration with electronic health records
Patient recruitment optimization tools
Regulatory timeline prediction
Global regulatory database integration
AI-powered drug discovery insights
Personalized medicine recommendations
Integration with pharmaceutical R&D workflows
Get detailed study information by NCT ID with AI analysis
| Name | Required | Description | Default |
|---|---|---|---|
| nctId | Yes | NCT identifier (e.g., 'NCT04373031') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'AI analysis,' which hints at additional processing beyond basic data retrieval, but does not specify what this entailsโe.g., whether it's real-time, cached, or involves specific algorithms. It also omits details like rate limits, authentication needs, or error handling, leaving significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Get detailed study information by NCT ID with AI analysis.' It is front-loaded with the core purpose and includes a key feature ('AI analysis') without unnecessary words. Every part of the sentence contributes to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain what 'detailed study information' includes or what the 'AI analysis' produces, leaving the agent uncertain about the return values. For a tool with no structured output documentation, the description should provide more context on expected results and behavioral traits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'nctId' parameter well-documented in the schema itself (including pattern and example). The description adds minimal value beyond this, only reiterating 'by NCT ID' without providing additional context like what the AI analysis does with the ID or any constraints. This meets the baseline of 3 since the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get detailed study information by NCT ID with AI analysis.' It specifies the verb ('Get'), resource ('detailed study information'), and key identifier ('NCT ID'), making it easy to understand. However, it does not explicitly differentiate from sibling tools like 'research_clinical_details' or 'ct_search_trials,' which might offer similar or overlapping functionality, preventing a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'AI analysis' but does not explain how this differs from other tools, such as whether it's for specific types of studies or when other tools might be more appropriate. This lack of context leaves the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search ClinicalTrials.gov for studies with AI-enhanced results
| Name | Required | Description | Default |
|---|---|---|---|
| condition | No | Medical condition (e.g., 'diabetes', 'cancer') | |
| intervention | No | Treatment or drug name | |
| phase | No | Study phases (PHASE1, PHASE2, PHASE3, PHASE4) | |
| status | No | Study status (RECRUITING, COMPLETED, etc.) | |
| pageSize | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'AI-enhanced results,' hinting at some processing, but doesn't clarify what this entails (e.g., ranking, summarization, or filtering). It also omits critical details like rate limits, authentication needs, response format, or pagination behavior, which are essential for a search tool with parameters like 'pageSize.'
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('search ClinicalTrials.gov for studies'). It avoids unnecessary words, though it could be more structured by explicitly mentioning key parameters or outcomes. Every part earns its place, but it lacks depth for a tool with multiple parameters and no output schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., study summaries, IDs, or enhanced data), how 'AI-enhanced' modifies results, or any behavioral constraints. This leaves significant gaps for an agent to invoke the tool effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 80%, providing good documentation for most parameters (e.g., 'condition' as a medical condition, 'phase' with study phases). The description adds little beyond this, only implying search functionality without explaining how parameters interact or what 'AI-enhanced' affects. With high schema coverage, the baseline is 3, as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('search') and target resource ('ClinicalTrials.gov for studies'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'ct_get_study' or 'pm_search_papers', which might also retrieve clinical trial information or research papers. The phrase 'with AI-enhanced results' adds specificity but doesn't fully distinguish from potential overlaps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'ct_get_study' (which might fetch details for a specific study) or 'pm_search_papers' (which searches research papers), leaving the agent with no explicit context for tool selection. Usage is implied only by the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get adverse event reports for a drug with analysis
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | Yes | Drug name to analyze | |
| dateRange | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'with analysis,' hinting at additional processing, but doesn't clarify what this entailsโsuch as data sources, rate limits, authentication needs, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Get adverse event reports for a drug with analysis.' It is front-loaded with the core purpose and avoids unnecessary words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of adverse event data, no annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't address key aspects like data freshness, analysis details, or error handling, leaving users with an incomplete picture of the tool's capabilities and limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%, with only 'drugName' documented. The description adds minimal value beyond the schema by implying analysis is included, but it doesn't explain parameter semantics like the format for 'dateRange' or what 'analysis' involves. With low schema coverage, the description doesn't fully compensate, resulting in a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get adverse event reports for a drug with analysis.' It specifies the verb ('Get'), resource ('adverse event reports'), and scope ('for a drug with analysis'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'research_drug_safety_profile' or 'research_safety_details,' which might have overlapping functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or comparisons to sibling tools such as 'fda_search_drugs' or 'research_safety_details.' Without this context, users must infer usage based on the tool name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search FDA drug database with safety analysis
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | No | Drug name to search for | |
| activeIngredient | No | Active ingredient name | |
| approvalStatus | No | Approval status filter |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool performs a search with safety analysis, but doesn't describe what 'safety analysis' entails (e.g., risk summaries, adverse event data), how results are returned (e.g., pagination, format), or any limitations (e.g., rate limits, data freshness). This is a significant gap for a search tool with potential complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Search FDA drug database with safety analysis'). There is no wasted text, and it directly communicates the tool's function without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'safety analysis' includes, the format or scope of results, or any behavioral traits like error handling. For a search tool with potential safety-critical implications, this leaves significant gaps for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear parameter descriptions. The tool description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain how parameters interact or provide examples). Given the high schema coverage, a baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and resource ('FDA drug database'), and adds the purpose of 'safety analysis'. However, it doesn't explicitly differentiate from sibling tools like 'fda_adverse_events' or 'research_drug_safety_profile', which might also involve FDA drug safety data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'fda_adverse_events' or 'research_drug_safety_profile'. It mentions 'safety analysis' but doesn't specify if this is for preliminary screening, regulatory checks, or other contexts, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search PubMed for research papers with enhanced analysis
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query (e.g., 'diabetes AND metformin') | |
| maxResults | No | ||
| publicationTypes | No | Filter by publication types | |
| dateRange | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Search' implies a read-only operation, but the description doesn't specify authentication requirements, rate limits, pagination behavior, or what 'enhanced analysis' entails. The vague 'enhanced analysis' claim creates uncertainty about what additional processing occurs beyond basic PubMed searching.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point. There's no wasted verbiage, though the front-loaded information is somewhat vague due to the 'enhanced analysis' qualifier. The structure is appropriate for a search tool description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 4 parameters, no annotations, no output schema, and only 50% schema coverage, the description is inadequate. It doesn't explain what 'enhanced analysis' means, how results are returned, what format they're in, or how this differs from basic PubMed API calls. The description leaves too many behavioral and output questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 50% schema description coverage (only 'query' and 'dateRange' subfields have descriptions), the description doesn't compensate for the undocumented parameters. It mentions 'enhanced analysis' but doesn't explain how this relates to the input parameters like 'publicationTypes' or 'maxResults'. The description adds no meaningful parameter semantics beyond what's already in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool searches PubMed for research papers, which is a clear verb+resource combination. However, it's vague about what 'enhanced analysis' means and doesn't differentiate this search tool from sibling tools like 'research_literature_details' or 'research_comprehensive_analysis' that might also involve PubMed searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With multiple research-related sibling tools available, there's no indication whether this is for basic literature searches, comprehensive analysis, or specific types of research queries. No exclusions or preferred use cases are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detailed clinical trials analysis for specific drug/condition
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | Yes | Drug name to analyze | |
| condition | Yes | Medical condition | |
| focusPhase | No | Focus on specific trial phases | |
| includeCompleted | No | Include completed trials | |
| maxTokens | No | Maximum tokens for the response |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'analysis' implies a read-only operation, the description doesn't disclose important behavioral aspects like whether this is a computationally intensive operation, potential rate limits, authentication requirements, or what format the analysis results will be returned in. The description is too minimal for a tool with 5 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just 7 words, front-loaded with the core purpose. Every word earns its place, and there's no wasted language or redundancy. This is an excellent example of efficient description writing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of analysis will be performed, what information will be returned, or how this differs from other research tools. The agent would need to guess about the tool's behavior and output format based on minimal information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description mentions 'specific drug/condition' which aligns with the required parameters, but adds no additional semantic context beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Detailed clinical trials analysis for specific drug/condition' clearly states the tool's purpose with a specific verb ('analysis') and resources ('clinical trials', 'drug/condition'). It distinguishes from some siblings like 'fda_adverse_events' or 'pm_search_papers' but doesn't explicitly differentiate from similar research tools like 'research_comprehensive_analysis' or 'research_safety_details'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling research tools available (research_competitive_landscape, research_comprehensive_analysis, research_safety_details, etc.), there's no indication of when this specific clinical trials analysis tool is appropriate versus other research tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyze competitive landscape across all databases
| Name | Required | Description | Default |
|---|---|---|---|
| targetCondition | Yes | Target medical condition | |
| competitorDrugs | No | List of competitor drugs to analyze | |
| includeGlobal | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'analyze' but doesn't specify what the analysis entails (e.g., data sources, output format, computational intensity, or rate limits). This is a significant gap for a tool with no structured safety or operational hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function. It's front-loaded with the core action and scope, with no wasted words. However, it could be more structured by including key details, but it earns high marks for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity implied by 'analyze' and 'across all databases', along with no annotations and no output schema, the description is incomplete. It doesn't cover what the analysis returns, how it handles data, or any operational constraints, making it inadequate for a tool with 3 parameters and no structured support.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (2 out of 3 parameters have descriptions). The description doesn't add meaning beyond the schema, as it doesn't explain parameters like 'targetCondition' or 'competitorDrugs' further. With moderate coverage, the baseline is 3, as the schema does some work but the description doesn't compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as 'Analyze competitive landscape across all databases', which indicates a general analysis function. However, it lacks specificity about what 'analyze' entails (e.g., comparing drugs, identifying gaps) and doesn't clearly distinguish it from sibling tools like 'research_comprehensive_analysis' or 'research_market_details', making it somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'research_clinical_details' and 'research_market_details', there's no indication of context, prerequisites, or exclusions. This leaves the agent without direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive analysis combining clinical trials, literature, and safety data
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | Yes | Drug name to analyze | |
| condition | Yes | Medical condition | |
| analysisDepth | No | detailed | |
| reportFormat | No | Format of the report output | summary |
| outputFormat | No | Output format for the report | structured |
| maxTokens | No | Maximum tokens for the response |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the types of data sources (clinical trials, literature, safety data), it doesn't describe what the analysis actually produces, whether it's a synthesis or just aggregated data, how long it might take, or any limitations. For a complex analysis tool with multiple parameters, this is insufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information about what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex analysis tool with 6 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the output looks like, how the different data sources are integrated, or what distinguishes this from simpler sibling tools. The agent would struggle to understand when and how to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 83% (high), so the baseline is 3 even though the description adds no parameter information. The description doesn't explain what 'comprehensive analysis' means in terms of how the parameters interact or what different analysisDepth levels entail, but with good schema coverage, this isn't required for a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs 'comprehensive analysis' combining clinical trials, literature, and safety data, which is a specific verb+resource combination. However, it doesn't explicitly distinguish this from sibling tools like research_clinical_details, research_literature_details, or research_safety_details, which appear to provide more focused analyses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the many sibling tools available. There's no mention of alternatives, prerequisites, or specific contexts where this comprehensive analysis is preferred over more targeted tools like research_executive_summary or research_competitive_landscape.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Complete drug safety analysis across clinical trials and FDA reports
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | Yes | Drug name to analyze | |
| includeTrials | No | ||
| includeFDA | No | ||
| timeframe | No | 5years |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'complete drug safety analysis' but doesn't specify what 'complete' entails, such as data sources, output format, limitations, or potential side effects like rate limits or authentication needs. This is inadequate for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action and scope concisely, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of drug safety analysis, no annotations, no output schema, and low schema coverage, the description is insufficient. It doesn't explain what the analysis returns, how results are structured, or any behavioral traits, leaving significant gaps for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low at 25%, with only 'drugName' documented. The description adds context by implying parameters like 'includeTrials' and 'includeFDA' through 'across clinical trials and FDA reports', and 'timeframe' through 'analysis', but doesn't detail their semantics or defaults. This partially compensates but leaves gaps, aligning with the baseline for moderate schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('complete drug safety analysis') and scope ('across clinical trials and FDA reports'), which is specific and distinguishes it from siblings like 'fda_adverse_events' or 'research_safety_details'. However, it doesn't explicitly differentiate from 'research_comprehensive_analysis' or 'research_safety_details', which might overlap in purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'research_safety_details' or 'research_comprehensive_analysis' is provided. The description implies usage for safety analysis but lacks context on prerequisites, exclusions, or comparisons to sibling tools, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate executive summary for drug/condition analysis (context-friendly)
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | Yes | Drug name to analyze | |
| condition | Yes | Medical condition | |
| maxTokens | No | Maximum tokens for the summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'context-friendly', which hints at adaptability but lacks specifics on how the tool behavesโe.g., whether it requires external data sources, has rate limits, or handles errors. For a generation tool with no annotations, this is a significant gap in transparency about operational traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core purpose in a single phrase. 'Generate executive summary for drug/condition analysis' is efficient, and 'context-friendly' adds a minor qualifier without redundancy. However, it could be slightly more structured by explicitly stating the tool's scope or limitations to improve clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a generation tool with 3 parameters, no annotations, and no output schema), the description is minimally adequate. It states the purpose but lacks details on behavior, output format, or error handling. Without annotations or output schema, the agent must infer much from the name and parameters alone, making this incomplete for informed tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters (drugName, condition, maxTokens) with descriptions. The description adds no additional meaning beyond what's in the schema, such as explaining relationships between parameters or usage nuances. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate or enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate executive summary for drug/condition analysis'. It specifies the verb ('generate'), resource ('executive summary'), and domain context ('drug/condition analysis'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'research_comprehensive_analysis' or 'research_clinical_details', which might offer similar or overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance: 'context-friendly' is vague and doesn't clarify when to use this tool versus alternatives. There's no explicit mention of when to choose this over sibling tools (e.g., 'research_comprehensive_analysis' for deeper insights or 'research_clinical_details' for specific data), nor any prerequisites or exclusions. This leaves the agent with little direction on optimal usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detailed literature analysis for specific drug/condition
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | Yes | Drug name to analyze | |
| condition | Yes | Medical condition | |
| timeframe | No | Publication timeframe | 3years |
| publicationTypes | No | Filter by publication types | |
| maxTokens | No | Maximum tokens for the response |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Detailed literature analysis' suggests a read-only operation, but the description doesn't specify whether this is a search, retrieval, or analysis operation, what data sources are used, whether there are rate limits, authentication requirements, or what format the analysis takes. For a tool with 5 parameters and no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for what it communicates and is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter tool with no annotations and no output schema, the description is inadequate. It doesn't explain what kind of analysis is performed, what the output looks like, what data sources are queried, or how this differs from related sibling tools. The agent would struggle to understand when and how to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description mentions 'drug/condition' which aligns with the two required parameters, but adds no additional semantic context beyond what's in the schema. With complete schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Detailed literature analysis for specific drug/condition' clearly states the verb ('analysis') and resource ('literature'), and specifies the scope ('for specific drug/condition'). However, it doesn't explicitly differentiate from sibling tools like 'research_clinical_details' or 'pm_search_papers', which appear related but have different focuses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'research_clinical_details', 'research_comprehensive_analysis', and 'pm_search_papers', there's no indication of what makes this tool distinct or when it should be preferred over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detailed market and competitive analysis for specific drug/condition
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | Yes | Drug name to analyze | |
| condition | Yes | Medical condition | |
| includeCompetitors | No | Include competitive analysis | |
| marketScope | No | Market scope for analysis | global |
| maxTokens | No | Maximum tokens for the response |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'detailed market and competitive analysis', implying a read-only research operation, but doesn't describe what the analysis includes (e.g., market size, trends, competitor drugs), how results are formatted, whether it's a summary or raw data, or any limitations like rate limits or authentication needs. This leaves significant gaps for a tool with 5 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does, making it easy to parse and understand quickly, with no wasted information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no annotations, no output schema, and multiple siblings), the description is incomplete. It lacks behavioral details (e.g., output format, limitations), usage guidelines relative to siblings, and doesn't compensate for the absence of annotations or output schema, leaving the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 5 parameters with descriptions. The description adds no additional meaning beyond implying analysis scope ('market and competitive'), which aligns with parameters like 'includeCompetitors' and 'marketScope' but doesn't provide extra syntax, format details, or usage context. This meets the baseline of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Detailed market and competitive analysis for specific drug/condition', which specifies the verb ('analysis') and resource ('market and competitive' for drug/condition). It distinguishes from some siblings like 'research_clinical_details' or 'research_safety_details' by focusing on market aspects, though it doesn't explicitly differentiate from 'research_competitive_landscape' or 'research_comprehensive_analysis'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over siblings like 'research_competitive_landscape' (which might focus only on competitors) or 'research_comprehensive_analysis' (which might be broader), nor does it specify any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detailed safety and FDA analysis for specific drug
| Name | Required | Description | Default |
|---|---|---|---|
| drugName | Yes | Drug name to analyze | |
| includeAdverseEvents | No | Include adverse event analysis | |
| includeApprovals | No | Include FDA approval information | |
| maxTokens | No | Maximum tokens for the response |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'analysis' but doesn't specify whether this is a read-only operation, requires authentication, has rate limits, or what the output format looks like. For a tool with no annotations and no output schema, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of drug safety analysis, lack of annotations, and absence of an output schema, the description is insufficient. It doesn't cover behavioral aspects like data sources, response structure, or error handling, leaving the agent with incomplete context for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter semantics beyond what the input schema provides. However, schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. This meets the baseline of 3, as the description doesn't need to compensate but also doesn't add extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to provide 'Detailed safety and FDA analysis for specific drug'. It specifies both the resource (drug) and the analysis type (safety and FDA), though it doesn't explicitly differentiate from siblings like 'research_drug_safety_profile' or 'fda_adverse_events', which is why it doesn't reach a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. With multiple sibling tools related to drug research, safety, and FDA data (e.g., 'research_drug_safety_profile', 'fda_adverse_events'), there is no indication of context, prerequisites, or exclusions to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
ct_get_studyct_search_trialsfda_adverse_eventsfda_search_drugspm_search_papersresearch_clinical_detailsresearch_competitive_landscaperesearch_comprehensive_analysisresearch_drug_safety_profileresearch_executive_summaryresearch_literature_detailsresearch_market_detailsresearch_safety_detailsScored across 13 tools
Tools have overlapping purposes, especially among the 'research_' prefixed ones (e.g., research_clinical_details, research_safety_details, research_drug_safety_profile, research_comprehensive_analysis), which could cause confusion about which to use for specific analyses. However, descriptions provide some differentiation, such as focusing on clinical trials, safety, or combined data.
Most tools follow a consistent pattern with prefixes like 'ct_', 'fda_', 'pm_', and 'research_' followed by descriptive names, though there are minor deviations (e.g., 'ct_get_study' vs. 'ct_search_trials' uses different verbs, and 'research_executive_summary' is more abstract). Overall, naming is readable and mostly predictable.
With 13 tools, the count is well-scoped for a medical research suite, covering multiple databases (ClinicalTrials.gov, FDA, PubMed) and various analysis types (search, details, safety, summaries). Each tool appears to serve a distinct purpose within the domain, justifying its inclusion.
The tool set provides comprehensive coverage for medical research, including search and detailed analysis across clinical trials, literature, safety data, and market insights. It supports full workflows from data retrieval (e.g., ct_search_trials) to integrated analyses (e.g., research_comprehensive_analysis), with no obvious gaps for the stated purpose.
Search 36M+ PubMed biomedical articles and ClinicalTrials.gov studies.
Linked life-science search: 40M+ papers, 1.2M+ trials, drugs, genes, FDA/EMA approvals, patents.
Semantic search across 5 US government healthcare databases.
FDA 510(k)s, PMAs, recalls & trials, linked: predicate search and review-time stats for AI agents.