quality-screener-mcp-server: Instructions file for Claude Code

CLAUDE.md

quality-screener-mcp-server CLAUDE.md is an instructions file for Claude Code from quality-screener/quality-screener-mcp-server. It costs 1,836 tokens per session, scanned A, original, MIT.

A set of coding instructions for a small MCP server, a service that lets AI agents call tools, and its related quality-screening backend.

In plain words
What is it for?
Maintaining the MCP server, shaping request data, calling the deployed REST API, following repository workflow rules, and running the Python test suite.
Why use it?
It keeps the server focused on passing requests to the backend instead of placing business logic in the wrong repository. It also documents the required branch, worktree, deployment order, and test commands.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: mentions CLAUDE.md; mentions AGENTS.md.

This is quality-screener/quality-screener-mcp-server's own configuration. It tells Claude Code how to work on quality-screener-mcp-server itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything quality-screener-mcp-server configures →

Reuse

Borrowing it

Nothing to install: this file belongs to quality-screener/quality-screener-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/quality-screener/quality-screener-mcp-server/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/quality-screener/quality-screener-mcp-server

Made for: Claude Code.

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Per session 1,836 This file is loaded in full into every session.
When invoked 1,836 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.01836 $0.01836
Opus 5 $0.00918 $0.00918
Sonnet 5 $0.00367 $0.00367
Haiku 4.5 $0.00184 $0.00184

Measured 10d ago against content hash ae2f04a25eac, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

quality-screener-mcp-server CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 130 lines

How it starts

The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md / AGENTS.md

Guidance for AI coding agents working in this repository. AGENTS.md is a symlink to this file. The workspace root (../CLAUDE.md) owns the cross-repo rules: worktrees, memory, and the REST contract with the quality-screener backend.

Critical rules

  1. No business logic here. This is a thin, stateless façade. If a change needs computation, it belongs behind a REST endpoint in quality-screener/. Payload shaping (_slim_score_rows, normalize_config) is the one allowed exception — see Adapters.
  2. Branch off main (this repo's default), in a worktree under ../worktrees/quality-screener-mcp-server/<branch-slug>/.
  3. The backend ships first. Tools talk to the deployed API; a tool released ahead of its endpoint is broken in production.

Commands

Requires uv. Python >=3.11. No linter or type checker is configured.

uv sync                                          # install deps (incl. dev group)
uv run pytest                                    # full suite; no live backend needed
uv run pytest tests/test_tool_filters.py::test_screen_share_builds_full_url
uv run qscreener-mcp                             # stdio transport (default)

# HTTP mode, mirroring the remote deployment:
QSCREENER_MCP_TRANSPORT=streamable-http QSCREENER_MCP_PORT=8080 \
  QSCREENER_API_URL=http://localhost:8001 uv run qscreener-mcp

Architecture

An MCP façade over the Quality Screener REST API (the "stobot" backend), with no dependency on the backend Python package — every tool builds a request and returns the decoded JSON. Extending it means adding an @mcp.tool() that delegates to _guard.

Four modules under qscreener_mcp/:

  • server.py — the whole tool surface (~760 lines). The FastMCP instance, every @mcp.tool(), token resolution, and the main() transport entry point. Config (_API_URL, _WEBSITE_URL, _PUBLIC_URL, _MCP_HOST) resolves at import time from env vars, so tests patch the module attribute (monkeypatch.setattr(server, "_WEBSITE_URL", ...)) rather than the env var.
  • constants.py — everything fixed at build time: header names, env-var names and defaults, on-disk filenames, user-facing messages, and the Tool enum of canonical tool names. Env values are deliberately not resolved here.
  • client.pyApiClient, a minimal httpx wrapper. Attaches the bearer token as X-Stobot-CLI-Token, adds the analytics headers, decodes JSON (None for empty bodies), raises ApiError on any non-2xx. Self-contained copy of the backend CLI's client — keep it dependency-free.
  • oauth.pyStobotOAuthProvider for HTTP mode. OAuth state is in-memory (_pending, _codes) → single-process only. Registered clients persist to ~/.config/qscreener/mcp_clients.json.

Read the full file on GitHub · 130 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 130 lines · 1,836 tokens per session scan A ae2f04a25eac

Subscribe to this mod's changes

quality-screener-mcp-server CLAUDE.md is an instructions file published in the GitHub repository quality-screener/quality-screener-mcp-server (0 stars, last pushed 6d ago), licensed MIT. It adds 1,836 tokens to every session, about $0.0092 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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