glama-status-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| glama_statusA | Fleet-wide Glama score tracker -- query per-tool TDQS grades. Identifies worst-scoring tools, trigger rescans. When to use: check fleet repo Glama scores, find tools needing docstring fixes, or trigger a refresh. When NOT to use: for code diagnostics use the repo's own tools; for Glama submission/review use glama.ai directly. Preconditions: repo must have been submitted to Glama and scored. Operations:
|
| glama_scores_summaryB | Return compact fleet-wide score summary. Grades per repo and count per grade bucket. Returns: success, grade_distribution, repos, count, message. |
| glama_daily_reportA | Generate daily fleet health report. Includes grade distribution, recent changes, worst tools, stale repos. When to use: once daily for comprehensive fleet Glama score overview. When NOT to use: for single repo details use glama_status(get). Preconditions: at least one refresh must have been run. |
| show_glama_status_cardA | Show fleet Glama score overview as a rich Prefab card. Displays grade distribution, per-repo scores (worst first), worst tools across the fleet, and stale repos in a structured dashboard card suitable for in-chat viewing. Return FormatToolResult with PrefabApp card and plain-text fallback. Examplesawait show_glama_status_card() |
| show_glama_repo_cardA | Show per-repo Glama score breakdown as a rich Prefab card. Displays overall grade, TDQS scores, coherence dimensions, maintenance grade, and per-tool scores for a single repo. |
| glama_agentic_analyzeB | Analyze Glama scores with LLM sampling and generate fixable todos. Uses the connected LLM (via MCP ctx.sample) to intelligently analyze a repo's tool docstring scores and produce actionable fix tasks. |
| glama_generate_reportsA | Generate markdown analysis reports for repos. Creates per-repo fix-todo reports in the reports/ directory, suitable for ingestion by IDE LLMs. If repo_name is empty, generates reports for all scored repos. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| glama_improvement_plan | Generate a docstring improvement plan for a repo from its Glama scores. |
| glama_fleet_analysis_prompt | Generate a fleet-wide Glama analysis prompt for the connected LLM. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Prefab Renderer (show_glama_status_card) | |
| Prefab Renderer (show_glama_repo_card) |
TDQS
Scored across 7 tools
Some overlap exists between dedicated report tools (glama_daily_report, glama_scores_summary) and the glama_status tool which has its own 'report' and 'list' operations. Descriptions try to clarify, but an agent might struggle to choose the correct tool for a given task.
All tools follow a consistent 'glama_<descriptive_name>' pattern in snake_case, with clear verb_noun or descriptive names. There is no mixing of conventions or confusing styles.
With 7 tools, the set is well-scoped for monitoring and analyzing Glama scores. Each tool has a distinct purpose, and the count feels neither too few nor too many.
The set covers analysis, reporting, summary, detailed queries, and rich cards. Missing a direct submission tool is acceptable as it's outside the server's scope. Minor gaps like lacking a bulk update feature are not critical.