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sandraschi

glama-status-mcp

by sandraschi
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

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 + snapshot

Related MCP server: mcp-federated-data

MCP Tools

Tool

Purpose

glama_status

Portmanteau: list, get, worst_tools, refresh, history, staleness, report, deltas

glama_scores_summary

Compact grade distribution and per-repo stats

glama_daily_report

Full markdown report with deltas, worst tools, stale repos

Web Dashboard

Route

Description

/

Sortable fleet score table

/api/repos

All repos with per-tool breakdowns

/api/repos/{name}

Single repo tool breakdown

/api/report

Full daily report JSON

/api/deltas

Score changes since last snapshot

/api/refresh (POST)

Trigger rescrape + snapshot

/api/worst-tools

Lowest-scoring tools fleet-wide

How it works

  1. Scraper fetches glama.ai/mcp/servers/{author}/{repo}/score pages

  2. Parser extracts per-tool grades, 6 TDQS dimension scores, coherence, and maintenance data

  3. SQLite stores current scores + snapshot history for delta tracking

  4. Daily refresh via Scheduled Task or just refresh creates a new snapshot

  5. Delta 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

Install Server
A
license - permissive license
A
quality
A
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
1Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Tools

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