glama-status-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@glama-status-mcpshow fleet score summary"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
glama-status-mcp
Daily-refreshed Glama score tracker for the sandraschi fleet. Scrapes per-tool TDQS grades from glama.ai, stores in SQLite, surfaces via MCP tools and a web dashboard. Tracks deltas between snapshots, generates daily reports, and flags stale repos needing rescan.
Quickstart
just install
just web # HTTP backend on :11072
just web-frontend # Vite dev on :11073
just web-dev # Full stack
just refresh # Manual scrape + snapshotRelated MCP server: mcp-federated-data
MCP Tools
Tool | Purpose |
| Portmanteau: list, get, worst_tools, refresh, history, staleness, report, deltas |
| Compact grade distribution and per-repo stats |
| Full markdown report with deltas, worst tools, stale repos |
Web Dashboard
Route | Description |
| Sortable fleet score table |
| All repos with per-tool breakdowns |
| Single repo tool breakdown |
| Full daily report JSON |
| Score changes since last snapshot |
| Trigger rescrape + snapshot |
| Lowest-scoring tools fleet-wide |
How it works
Scraper fetches
glama.ai/mcp/servers/{author}/{repo}/scorepagesParser extracts per-tool grades, 6 TDQS dimension scores, coherence, and maintenance data
SQLite stores current scores + snapshot history for delta tracking
Daily refresh via Scheduled Task or
just refreshcreates a new snapshotDelta engine compares latest two snapshots to show changes
Scored repos (10 of 35 registered on Glama)
Repo | Grade | Score | Tools |
blender-mcp | C | 2.70 | 67 |
windows-operations-mcp | B | 3.00 | 17 |
virtualization-mcp | B | 3.06 | 9 |
worldlabs-mcp | B | 3.38 | 20 |
robotics-mcp | A | 3.58 | 8 |
bumi-mcp | A | 3.64 | 2 |
xkcd-mcp | A | 3.67 | 6 |
cursor-mcp | A | 3.80 | 6 |
steam-mcp | A | 3.81 | 14 |
email-mcp | A | 3.82 | 10 |
25 more registered on Glama but not yet analyzed (page exists, no tools).
Scoring (Glama TDQS)
Dimension | Weight | Description |
Purpose Clarity | 25% | First sentence states what the tool does |
Usage Guidelines | 20% | When to / not to call, preconditions |
Behavioral Transparency | 20% | Returns, side effects, error conditions |
Parameter Semantics | 15% | Every param: type, values, what it affects |
Conciseness & Structure | 10% | Not a wall of text, not a one-liner |
Contextual Completeness | 10% | Enough context to use without reading source |
Server-level = 60% mean + 40% minimum - one bad tool pulls the whole score down.
Ports
Backend (FastAPI + MCP HTTP): 11072
Frontend (Vite dev): 11073
Maintenance
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MCP directory API
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/sandraschi/glama-status-mcp'
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