Scispot MCP Server for VS Code Copilot 12 tools — connect in under 2 minutes
GitHub Copilot in VS Code is the most widely adopted AI coding assistant, embedded directly into the world's most popular code editor. With MCP support in Agent mode, Copilot can access external data and APIs to generate context-aware code grounded in real-time information.
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{
"mcpServers": {
"scispot": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Scispot MCP Server
Connect your Scispot API-first Cannabis Testing Laboratory LIMS to any AI agent and take full control of your laboratory operations, quality assurance workflows, and regulatory compliance through natural conversation.
GitHub Copilot Agent mode brings Scispot data directly into your VS Code workflow. With a project-scoped config, the entire team shares access to 12 tools. Copilot queries live data, generates typed code, and writes tests from actual API responses, all without leaving the editor.
What you can do
- Sample Management — Track all cannabis samples received with chain of custody, testing priority, and real-time status updates
- Test Panels — Browse available analytical methods including potency, terpenes, pesticides, heavy metals, mycotoxins, and microbials
- Analytical Results — Access complete test findings with pass/fail determinations against state-specific regulatory limits
- Certificates of Analysis — Retrieve all issued CoAs with QR codes for consumer verification and automatic Metrc submission
- Batch Traceability — Monitor production batches through laboratory testing with full seed-to-sale linkage
- Plate Management — Oversee high-throughput batch processing with 96-well and 384-well plate tracking
- Analytical Runs — Review instrument run data including QC metrics, system suitability, and analyst assignments
- Order Tracking — Monitor client testing orders from submission through invoicing with ETA predictions
- Instrument Health — Verify calibration status, maintenance schedules, and operational readiness for HPLC, GC-MS, ICP-MS systems
- Workflow Automation — Track standardized laboratory processes from sample intake to CoA approval with bottleneck identification
- Client Directory — Access complete client profiles including license types, testing history, and custom panel configurations
- Audit Trails — Retrieve comprehensive operation logs for FDA 21 CFR Part 11 compliance and ISO/IEC 17025 inspection readiness
The Scispot MCP Server exposes 12 tools through the Vinkius. Connect it to VS Code Copilot in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Scispot to VS Code Copilot via MCP
Follow these steps to integrate the Scispot MCP Server with VS Code Copilot.
Create MCP config
Create a .vscode/mcp.json file in your project root
Add the server config
Paste the JSON configuration above
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
Start using Scispot
Ask Copilot: "Using Scispot, help me...". 12 tools available
Why Use VS Code Copilot with the Scispot MCP Server
GitHub Copilot for Visual Studio Code provides unique advantages when paired with Scispot through the Model Context Protocol.
VS Code is used by over 70% of developers. adding MCP tools to Copilot means your team can leverage external data without leaving their primary editor
Project-scoped MCP configs (`.vscode/mcp.json`) let you commit server configurations to your repository, ensuring the entire team shares the same tool access
Copilot's Agent mode integrates MCP tools seamlessly with file editing, terminal commands, and workspace search in a single agentic loop
GitHub's enterprise compliance and audit features extend to MCP tool usage, providing visibility into how AI interacts with external services
Scispot + VS Code Copilot Use Cases
Practical scenarios where VS Code Copilot combined with the Scispot MCP Server delivers measurable value.
Live API integration: Copilot can query an MCP server, inspect the response schema, and generate typed API client code in the same step
DevSecOps workflows: security teams can give developers access to domain intelligence tools directly in their editor for real-time vulnerability assessment during code review
Data pipeline development: Copilot fetches sample data via MCP and generates transformation scripts, validators, and test fixtures from actual API responses
Documentation generation: Copilot queries available tools and auto-generates README sections, API reference docs, and usage examples
Scispot MCP Tools for VS Code Copilot (12)
These 12 tools become available when you connect Scispot to VS Code Copilot via MCP:
list_audit_logs
Each audit log entry contains the precise timestamp (ISO 8601), performing user name and ID, action type (sample created, result modified, CoA issued, workflow step completed, user permission changed, instrument calibration recorded), affected record ID and type, previous and new values for any modifications, IP address and user agent, and justification comment (if required for critical changes). Fundamental for regulatory inspections, data integrity investigations, deviation root cause analysis, FDA 21 CFR Part 11 compliance, and ISO/IEC 17025 quality system requirements. AI agents use this to reconstruct event sequences during quality investigations, identify unauthorized or suspicious changes, monitor user activity patterns, generate audit-ready documentation packages, and demonstrate data integrity to regulatory inspectors. List all audit trail entries for laboratory operations and data modifications
list_batches
Each batch entry contains the batch ID, producing facility license number, batch size and unit of measure, cannabis product type, harvest or manufacture date, linked samples submitted for testing, batch testing status (pending, partial, complete, failed), final disposition (released, quarantined, rejected, destroyed, reworked), and seed-to-sale tracking identifiers (Metrc UID, state compliance tags). Essential for batch-level compliance monitoring, recall management, regulatory reporting, and inventory reconciliation. AI agents reference this when tracing contamination issues, verifying batch clearance for distribution, generating lot-based compliance reports, or investigating quality deviations. List all cannabis production batches tracked through laboratory testing
list_certificates
Each CoA record includes the certificate number, linked sample and batch, issuing laboratory name and accreditation number, comprehensive analytical results (potency profile with THC/CBD percentages, terpene fingerprint with individual concentrations, contaminant screening results for pesticides, heavy metals, mycotoxins, and microbials), regulatory compliance statement, authorized signatory name and signature, issuance date, expiration date, and QR code for consumer verification. Critical for product release decisions, regulatory audit documentation, consumer transparency programs, and integration with state traceability systems (Metrc) and retail platforms (WeedMaps). AI agents use this to verify CoA authenticity, confirm batch compliance status, generate client-facing documentation packages, and ensure automatic regulatory submissions. List all Certificates of Analysis (CoA) issued by the laboratory
list_clients
Each client record contains company name, license number and type (cultivation facility, processing plant, dispensary, distributor, third-party tester), primary contact information, account status (active, suspended, pending), billing terms, sample volume history, preferred communication methods, and any special testing requirements or custom panels configured. Essential for laboratory client relationship management, sample intake workflows, account-based reporting, and regulatory compliance documentation. AI agents should reference this when identifying sample ownership, generating client-specific reports, verifying active testing contracts, communicating results, and analyzing client testing patterns. List all clients (cultivators, processors, retailers) using laboratory services
list_instruments
Each instrument record contains the instrument name (HPLC system, GC-MS, ICP-MS, qPCR thermocycler, spectrophotometer), manufacturer, model number, serial number, installation location, calibration status, last calibration date, next scheduled maintenance, qualification status (IQ/OQ/PQ completion), associated test methods, and current operational status (active, under maintenance, offline, decommissioned). Critical for instrument qualification management, preventive maintenance scheduling, analytical data integrity verification, and regulatory inspection readiness. AI agents should reference this to verify instrument readiness before assigning tests, schedule calibration activities, troubleshoot analytical failures, and generate equipment utilization reports. List all laboratory instruments with calibration and maintenance status
list_orders
Each order record contains the order ID, requesting client company, order date, requested test panels, number of samples included, priority level (standard, rush, priority), order status (pending, in-progress, completed, invoiced), assigned laboratory team, estimated completion date, and billing information. Critical for order management, client communication, laboratory capacity planning, and revenue tracking. AI agents use this to monitor order progress, identify bottlenecks, prioritize workflow assignments, communicate status updates to clients, and generate order fulfillment reports. List all testing orders and service requests from clients
list_plates
Each plate entry contains the plate ID, plate format (96-well, 384-well), assay type assigned, number of samples loaded, number of standards and controls, run date, associated instrument, and processing status (prepared, in-run, completed, failed). Critical for managing high-volume testing operations, optimizing throughput, tracking reagent usage, and ensuring data integrity for multi-sample analytical runs. AI agents use this to monitor plate preparation status, identify incomplete runs, optimize well assignments, and troubleshoot analytical failures at the plate level. List all laboratory plates used for batch sample processing
list_results
Each result contains the result ID, linked sample and batch, test panel performed, comprehensive analytical findings (THC/CBD potency percentages, full terpene profiles, pesticide residue levels, heavy metal concentrations, mycotoxin detection, microbial counts), pass/fail determination against regulatory limits, analyst who performed the test, reviewer approval status, and date of completion. Fundamental for quality assurance workflows, client notification processes, regulatory data submissions, and product release decisions. AI agents should query this to verify sample compliance before releasing Certificates of Analysis, advising clients on product disposition, or preparing regulatory reports. List all laboratory test results with complete analytical data
list_runs
Each run entry contains the run ID, instrument name and type (HPLC, GC-MS, ICP-MS, spectrophotometer), method or assay performed, start and end timestamps, operating analyst or technician, number of samples processed, quality control results (standard recoveries, blank checks, duplicate precision), system suitability status, and overall run disposition (accepted, rejected, requires review). Essential for instrument utilization tracking, method performance monitoring, analyst productivity assessment, and regulatory audit preparation. AI agents should query this to verify run completion status, identify failed runs requiring reanalysis, schedule instrument maintenance, and generate throughput reports. List all analytical runs executed on laboratory instruments
list_samples
Each sample contains the unique sample ID, submitting client or cultivator, sample type (flower, edible, concentrate, topical, cartridge), received date, testing priority level, sample condition upon receipt, chain of custody documentation, and current testing status (received, in-progress, completed, failed, on-hold). Critical for laboratory workflow management, sample intake tracking, turnaround time monitoring, and seed-to-sale traceability compliance. AI agents use this to manage sample queues, predict completion dates, prioritize rush orders, and notify clients about status changes. List all cannabis samples submitted for laboratory testing
list_tests
Each test entry includes the test name (potency, terpenes, pesticides, heavy metals, mycotoxins, microbials, residual solvents, water activity, moisture content, homogeneity), test methodology (HPLC, GC-MS, ICP-MS, ELISA, qPCR, LC-MS/MS), accreditation status, standard turnaround time, pricing tier, and regulatory limits per jurisdiction. Essential for test panel configuration, method validation, ISO/IEC 17025 compliance, and state-specific cannabis testing requirements. AI agents reference this when configuring sample test orders, explaining testing scopes to clients, verifying analytical method accreditation, and ensuring compliance with regulatory testing mandates. List all analytical test panels and methods available in the laboratory
list_workflows
Each workflow entry contains the workflow name (sample intake and login, potency testing, contaminant screening, CoA review and approval, sample disposal, non-conformance investigation), step definitions with sequential order, assigned roles and responsibilities at each step, quality control checkpoints and decision gates, average completion time, current instances in progress, and bottleneck indicators. Essential for laboratory operations management, staff task assignment, process optimization, and ISO/IEC 17025 quality management system compliance. AI agents use this to guide technicians through standardized testing procedures, identify workflow bottlenecks causing delays, ensure quality checkpoints are not bypassed, and generate process efficiency reports. List all laboratory workflow templates and active processes
Example Prompts for Scispot in VS Code Copilot
Ready-to-use prompts you can give your VS Code Copilot agent to start working with Scispot immediately.
"Show me all cannabis samples currently in testing and their expected completion dates."
"List all pending Certificates of Analysis awaiting quality manager review and authorized signatory approval."
"Check the integration status with Metrc seed-to-sale tracking and automatic CoA publishing to state regulatory systems."
Troubleshooting Scispot MCP Server with VS Code Copilot
Common issues when connecting Scispot to VS Code Copilot through the Vinkius, and how to resolve them.
MCP tools not available
Scispot + VS Code Copilot FAQ
Common questions about integrating Scispot MCP Server with VS Code Copilot.
Which VS Code version supports MCP?
How do I switch to Agent mode?
Can I restrict which MCP tools Copilot can access?
Does MCP work in VS Code Remote or Codespaces?
.vscode/mcp.json work in Remote SSH, WSL, and GitHub Codespaces environments. The MCP connection is established from the remote host, so ensure the server URL is accessible from that environment.Connect Scispot with your favorite client
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TypeScript toolkit for building AI-powered web applications.
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Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Scispot to VS Code Copilot
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.