TL;DR
Clinical Trials Data is an open-source Model Context Protocol (MCP) server that provides AI agents with structured, real-time access to the ClinicalTrials.gov database. It is designed for medical researchers and AI developers who need to perform complex searches, statistical analyses, and trial matching directly within LLM environments like Claude Desktop. Its key differentiator is its specialized set of tools for trend analysis and batched data retrieval, which significantly reduces the manual effort required for clinical data extraction.
What Users Actually Pay
No user-reported pricing yet.
Our Take
Clinical Trials Data occupies a vital niche in the rapidly expanding ecosystem of AI-native research tools. By leveraging the Model Context Protocol, it transforms the traditionally cumbersome ClinicalTrials.gov API into a conversational interface, allowing researchers to query medical data as easily as they would chat with a colleague. This project effectively bridges the gap between high-level AI reasoning and the rigorous, structured data required for clinical research and healthcare decision-making. The tool’s primary strength lies in its comprehensive toolset, which goes beyond simple keyword searching to include statistical functions like phase distribution analysis and field-specific statistics. These features are particularly useful for competitive intelligence and meta-analyses, where understanding the landscape of a specific therapeutic area is just as important as finding individual trial results. The inclusion of batched retrieval and robust error handling suggests a developer focus on stability that is often missing in early-stage open-source projects. However, potential users should be aware of the technical overhead involved. As an open-source GitHub repository rather than a standalone SaaS platform, it requires a basic understanding of MCP configuration and CLI tools. Furthermore, like many deep-integration tools for LLMs, there is a risk of 'context bloat,' where the AI model may consume a significant portion of its memory just managing the tool definitions, potentially limiting the length of complex analytical sessions. Overall, Clinical Trials Data is best suited for tech-forward medical researchers, clinical trial recruiters, and data scientists who already use AI agents in their daily workflows. It is an excellent free alternative to expensive proprietary medical intelligence platforms, provided the user has the technical appetite to manage the setup and monitor API limitations.
Similar Products
Pros
- + Seamless integration with MCP-enabled AI assistants like Claude, enabling natural language queries of complex medical data.
- + Specialized statistical tools that allow for immediate analysis of trial phases, conditions, and sponsor distributions.
- + Efficient batched retrieval capabilities that streamline the process of gathering comprehensive details for multiple NCT IDs at once.
- + Robust error handling and validation logic that prevent common API-related crashes and provide descriptive feedback to the AI agent.
- + Completely free and open-source, offering high-level medical intelligence without the subscription costs of enterprise software.
Cons
- - Requires manual installation and technical configuration, which may be a barrier for non-technical medical professionals.
- - Subject to the rate limits and uptime of the official ClinicalTrials.gov API, which can lead to throttled responses during high-volume usage.
- - Can contribute to 'context window' exhaustion in LLMs, as the server's numerous tool definitions consume a portion of the model's memory.
- - Lacks a dedicated graphical user interface (GUI), making it entirely dependent on the host AI application's capabilities.
- - No formal enterprise support or guaranteed update roadmap, common for community-maintained GitHub projects.
Sentiment Analysis
No reviews or mentions found across G2, Capterra, TrustRadius, Reddit, or X (Twitter) for the software product 'Clinical Trials Data' or the GitHub repository https://github.com/plainyogurt21/clintrials-mcp. The project appears to be a very new or niche open-source MCP server with minimal public visibility and no user feedback in the specified sources.
Sentiment Over Time
Agent Readiness
14/100The 'Clinical Trials Data' (clintrials-mcp) is an open-source MCP server providing LLM-friendly access to ClinicalTrials.gov data via tools for search, retrieval, and analysis. It lacks a traditional public API, focusing on MCP protocol over HTTP/SSE, making it unsuitable for standard REST integrations but viable for agentic LLM workflows if deployed. Free deployment tiers available, good repo docs, but poor for broad agent automation due to no no-code integrations, webhooks, or versioning.
Last checked Mar 20, 2026
MCP Integrations
1 server12 tools4,387 total usesProvide structured access to ClinicalTrials.gov data for searching, retrieving, and analyzing clinical trial information. Enable multi-parameter searches, detailed trial retrievals, and statistical analyses to support medical research and healthcare decision-making. Deliver robust error handling and flexible field selection to optimize data responses.
12 tools
search_trials_by_conditionSearch clinical trials by medical condition(s). This tool allows you to search for clinical trials based on a list of medical conditions. Input: - `conditions`: A list of strings, where each string is a medical condition to search for. The search will find trials related to any of the specified conditions. Example: `['cancer', 'diabetes']` - `max_studies`: The maximum number of studies to return. Defaults to 50. - `fields`: A list of specific fields to return in the results. If not provided, returns SEARCH_TOOL_DEFAULTS (9 essential fields: NCTId, BriefTitle, Acronym, Condition, Phase, InterventionName, LeadSponsorName, OverallStatus, HasResults).search_trials_by_interventionSearch clinical trials by intervention/treatment. This tool allows you to search for clinical trials based on a list of interventions or treatments. Input: - `interventions`: A list of strings, where each string is an intervention or treatment to search for. The search will find trials related to any of the specified interventions. Example: `['aspirin', 'chemotherapy']` - `max_studies`: The maximum number of studies to return. Defaults to 50. - `fields`: A list of specific fields to return in the results. If not provided, returns SEARCH_TOOL_DEFAULTS (9 essential fields: NCTId, BriefTitle, Acronym, Condition, Phase, InterventionName, LeadSponsorName, OverallStatus, HasResults).search_trials_by_sponsorSearch clinical trials by sponsor/organization. This tool allows you to search for clinical trials based on a list of sponsor organizations. Input: - `sponsors`: A list of strings, where each string is a sponsor organization to search for. The search will find trials sponsored by any of the specified organizations. Example: `['National Cancer Institute', 'Pfizer']` - `max_studies`: The maximum number of studies to return. Defaults to 50. - `fields`: A list of specific fields to return in the results. If not provided, returns SEARCH_TOOL_DEFAULTS (9 essential fields: NCTId, BriefTitle, Acronym, Condition, Phase, InterventionName, LeadSponsorName, OverallStatus, HasResults).search_trials_by_acronymSearch clinical trials by study acronym. Uses the Acronym field (protocolSection.identificationModule.acronym) to find trials by their public short name. Example: 'TETON'. The API search is seeded with the provided acronyms to narrow results, then results are filtered locally to ensure the acronym field matches the requested value(s). Input: - `acronyms`: One or more acronyms to search for (e.g., ['TETON']). - `max_studies`: Maximum number of studies to request from the API. - `exact_match`: When true (default), matches acronyms exactly (case-insensitive). When false, matches if any provided acronym is contained within the study acronym (case-insensitive partial match). - `fields`: A list of specific fields to return in the results. If not provided, returns ACRONYM_SEARCH_DEFAULTS (8 fields optimized for acronym discovery: NCTId, BriefTitle, Acronym, Condition, InterventionName, Phase, LeadSponsorName, HasResults).search_trials_by_nct_idsRetrieve specific clinical trials by NCT ID(s). This tool allows you to retrieve the details of specific clinical trials by providing their NCT IDs. Input: - `nct_ids`: A list of strings, where each string is an NCT ID to retrieve. Example: `['NCT04280705', 'NCT04280718']` - `fields`: A list of specific fields to return in the results. If not provided, returns SEARCH_TOOL_DEFAULTS (9 essential fields: NCTId, BriefTitle, Acronym, Condition, Phase, InterventionName, LeadSponsorName, OverallStatus, HasResults).search_trials_combinedSearch clinical trials using multiple criteria. This tool allows you to perform a combined search using multiple criteria such as conditions, interventions, sponsors, and general terms. Input: - `conditions`: A list of medical conditions to search for. - `interventions`: A list of interventions or treatments to search for. - `sponsors`: A list of sponsor organizations to search for. - `terms`: A list of general search terms. - `nct_ids`: A list of specific NCT IDs to include in the search. - `max_studies`: The maximum number of studies to return. Defaults to 50. - `fields`: A list of specific fields to return in the results. If not provided, returns SEARCH_TOOL_DEFAULTS (9 essential fields: NCTId, BriefTitle, Acronym, Condition, Phase, InterventionName, LeadSponsorName, OverallStatus, HasResults).get_trial_detailsGet comprehensive details for a single clinical trial. This tool retrieves detailed information for a single clinical trial given its NCT ID. Input: - `nct_id`: The NCT ID of the trial to retrieve. Example: `'NCT04280705'` - `fields`: A list of specific fields to return. If not provided, returns DETAIL_TOOL_DEFAULTS (25 comprehensive fields covering: NCTId, BriefTitle, OfficialTitle, Acronym, Condition, Keyword, Phase, OverallStatus, InterventionType, InterventionName, InterventionDescription, ArmGroupLabel, ArmGroupType, ArmGroupDescription, EligibilityCriteria, MinimumAge, MaximumAge, Sex, PrimaryOutcomeMeasure, SecondaryOutcomeMeasure, BriefSummary, LocationFacility, LocationCountry, LeadSponsorName, CollaboratorName, HasResults).get_trial_details_batchedRetrieve detailed clinical trial records in batches to reduce payload during discovery. Accepts a list of NCT IDs and fetches details in batches (default 10). Preserves the order of input NCT IDs in the returned list. Use this after search tools which return a minimal field set. Input: - `nct_ids`: List of NCT IDs to retrieve in batches. - `fields`: Specific fields to return. If not provided, returns DETAIL_TOOL_DEFAULTS (25 comprehensive fields covering: NCTId, BriefTitle, OfficialTitle, Acronym, Condition, Keyword, Phase, OverallStatus, InterventionType, InterventionName, InterventionDescription, ArmGroupLabel, ArmGroupType, ArmGroupDescription, EligibilityCriteria, MinimumAge, MaximumAge, Sex, PrimaryOutcomeMeasure, SecondaryOutcomeMeasure, BriefSummary, LocationFacility, LocationCountry, LeadSponsorName, CollaboratorName, HasResults). - `batch_size`: Number of trials to fetch per API call (default 10).analyze_trial_phasesAnalyze the distribution of trial phases for given search criteria. This tool analyzes the distribution of clinical trial phases (e.g., Phase 1, Phase 2, Phase 3) for a given set of search criteria. Input: - `conditions`: A list of medical conditions to filter the analysis. - `interventions`: A list of interventions to filter the analysis. - `sponsors`: A list of sponsors to filter the analysis. - `max_studies`: The maximum number of studies to include in the analysis. Defaults to 1000.get_field_statisticsGet statistical information about field values. This tool retrieves statistical information about the values of specified fields in the ClinicalTrials.gov database. Input: - `field_names`: A list of field names to get statistics for. - `field_types`: A list of field types to filter by. Example: `['ENUM', 'STRING']`get_available_fieldsGet organized list of available fields for customizing search results. This tool provides a list of available fields that can be used to customize the results of other search tools. The fields are organized into categories. Input: - `category`: An optional string to specify a category of fields to return. If not provided, all categories and default fields will be returned. Example: `'conditions'`search_trials_nct_ids_onlyLightweight search returning only NCT IDs and minimal metadata for discovery. This tool performs a lightweight search that returns only the NCT IDs and minimal metadata for the purpose of discovering relevant trials. Input: - `conditions`: A list of medical conditions to search for. - `interventions`: A list of interventions or treatments to search for. - `sponsors`: A list of sponsor organizations to search for. - `terms`: A list of general search terms. - `max_studies`: The maximum number of studies to return. Defaults to 100.
Last checked Apr 29, 2026
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