Research Insights MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NODE_ENV | No | Node environment (production, development, etc.) | production |
| LOG_LEVEL | No | Logging level (error, warn, info, verbose, debug, silly) | info |
| SUPABASE_URL | Yes | The URL of your Supabase project (e.g., https://your-project.supabase.co) | |
| SUPABASE_SERVICE_ROLE_KEY | Yes | The service role key for Supabase (admin access, keep secret) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_insights_by_scopeC | Search insights with scoped filters (call_type, sentiment, date_range, product, segment, quality_threshold). Handles bulk queries of 1500+ calls. |
| get_collection_itemsC | Retrieve contents of a research collection |
| search_by_confidenceC | Filter insights by confidence score range |
| search_by_validation_statusC | Filter insights by validation status |
| get_insight_provenanceC | Get full citation with timestamps and evidence |
| search_recordings_metadataC | Search recordings by date range |
| get_cross_workspace_insightsC | Aggregate insights across Sales, Support, and UX workspaces |
| aggregate_insights_by_themeC | Extract and group insights by themes |
| calculate_confidence_distributionC | Generate quality score histogram |
| generate_trend_analysisC | Compare insights across time periods |
| get_competitor_mentionsC | Find competitor mentions across recordings |
| analyze_feature_requestsC | Extract and analyze feature request frequency |
| detect_recurring_patternsC | Find patterns that appear across multiple calls (min_frequency 3+) |
| generate_research_briefC | Auto-generate executive briefs from multiple calls |
| auto_tag_recordingsC | AI-powered auto-tagging with confidence scores |
| batch_apply_tagsC | Bulk tag application to multiple recordings |
| create_research_alertD | Get notified when patterns emerge |
| monitor_kpi_thresholdsC | Alert when research metrics hit thresholds |
| create_stakeholder_reportC | Tailored reports for product/exec/sales/engineering |
| save_search_filterC | Save complex filter combinations for quick recall |
| load_search_filterC | Load saved search filters |
| track_pattern_trendsC | Compare patterns across time periods |
| compare_cohortsC | Compare insights between customer segments |
| track_cohort_over_timeD | See how a cohort's feedback evolves |
| analyze_sentiment_shiftsC | Track sentiment changes within conversations |
| identify_emotional_triggersD | What causes positive/negative reactions |
| detect_anomaliesC | Find statistically unusual patterns |
| explain_anomalyC | Understand what caused unusual patterns |
| map_customer_journeyC | Link insights to customer journey stages |
| identify_journey_gapsC | Find stages with missing feedback/issues |
| create_insight_snapshotC | Save current analysis as reusable snapshot |
| search_research_historyC | Find similar past research |
| add_research_noteC | Add contextual notes to insights |
| get_team_annotationsC | See what team members have noted |
| sync_to_jiraC | Create Jira tickets from high-frequency feature requests |
| export_to_productboardD | Send insights to ProductBoard |
| enrich_salesforce_accountC | Add research insights to Salesforce account records |
| create_customer_briefingC | Generate CS briefing before renewal calls |
| suggest_research_questionsC | AI suggests follow-up questions based on data gaps |
| identify_knowledge_gapsD | Find what you don't know |
| test_hypothesisC | Validate research hypotheses with data |
| calculate_sample_sizeC | How many calls needed for statistical validity |
| assess_research_qualityD | Score research quality |
| detect_research_biasC | Identify leading questions, confirmation bias |
| audit_data_usageC | Track who accessed what insights |
| anonymize_insightsC | Remove PII before sharing |
| validate_insight_batchC | Bulk validate multiple insights |
| get_validation_queueC | Get insights pending manual review |
| predict_validation_outcomeC | ML-based prediction of validation outcome |
| override_validationC | Manual override of validation status |
| export_to_signalC | Prepare validated insights for Signal platform export |
| track_signal_usageC | Record usage events from Signal platform |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ecidk/mcp-research-insights'
If you have feedback or need assistance with the MCP directory API, please join our Discord server