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Glama

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

Maintenance

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

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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/sandraschi/glama-status-mcp'

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