Chronova MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PORT | No | Server listen port | 3001 |
| CHRONOVA_API_KEY | Yes | Your Chronova API key (or set in config file) | |
| CHRONOVA_API_URL | No | Chronova API base URL | https://chronova.dev/api/v1 |
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 | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_ai_insightsA | Get AI-assisted coding analytics including adoption timeline (AI vs manual coding over time), contribution share (percentage of AI vs manual work), human vs AI comparison by language, project-level AI dependency, and efficiency trends. |
| get_developer_contextA | Get the authenticated user's developer profile including coding statistics, subscription status, GitHub integration status, and organization memberships. No parameters required — uses the configured API key. |
| get_productivity_summaryA | Get aggregated coding productivity statistics for a time range. Returns total coding time, language breakdown, editor breakdown, and project breakdown. |
| get_recent_activityA | Get recent coding heartbeats (activity events). Returns paginated results — use 'page' and 'per_page' parameters to navigate through large result sets. The response includes 'total' count and pagination metadata. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool targets a distinct area: AI insights, developer profile, productivity aggregates, and raw activity events. The descriptions clearly differentiate the data returned, leaving no ambiguity about which tool to select.
All tool names follow a consistent get_<object> pattern with clear snake_case naming. The verbs and nouns are uniform, making the API predictable and easy to navigate.
With only 4 tools, the server is tightly scoped to read-only coding analytics. Each tool covers a distinct and necessary data view without unnecessary bloat or redundancy.
The set covers the major analytics surfaces: AI insights, user context, productivity summaries, and raw activity logs. Minor gaps like team-level analytics or detailed project history could exist, but core personal coding analytics needs are well served.