MCP server

Agent Workspace Practice

io.github.jimy-r/agent-workspace-practice

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Search + patterns, maturity assessment, context pricing, redaction checks - a practice as tools.

Working at last checkAbout 1,611 tokens per request once connected6 tools

Website

https://jamesross.ai/mcp

At a glance

Type: MCP server
Publisher: Not verified
Status: Working at last check
Tools: 6
Token cost: About 1,611 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http agent-workspace-practice https://jamesross.ai/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.agent-workspace-practice]
url = "https://jamesross.ai/mcp"

Gemini CLI

{"mcpServers":{"agent-workspace-practice":{"httpUrl":"https://jamesross.ai/mcp"}}}

Cursor

{"mcpServers":{"agent-workspace-practice":{"url":"https://jamesross.ai/mcp"}}}

VS Code

{"servers":{"agent-workspace-practice":{"type":"http","url":"https://jamesross.ai/mcp"}}}

Antigravity

agy mcp add agent-workspace-practice https://jamesross.ai/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"agent-workspace-practice","server_url":"https://jamesross.ai/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"agent-workspace-practice","server_url":"https://jamesross.ai/mcp"}

Tools

assess_workspace ~367 tokens
Score an agent workspace across six dimensions: canonical knowledge, memory and context economics, verification and oversight, safety and permissions, telemetry and cost, provenance and delegation. Two variants: "practitioner" takes 18 answers (a single operator's workspace), "org" takes 12 (an orga
check_redaction ~215 tokens
Scan text for the shapes of private content before it goes anywhere public: email addresses, absolute home paths, credential and token prefixes, private and link-local IPs, .local hostnames, and secret filenames. Returns a count per pattern class and a total, nothing else. The text you send and the
lookup_pattern ~185 tokens
Search the 18 architecture patterns behind this practice by keyword or by pattern number. Returns up to 3 matches, each with its number, title, the problem it opens on, and a link to the full text. Use it when you want the reasoning behind an agent-workspace design decision rather than a how-to.
price_context_read ~393 tokens
Price a block of context by where it enters a session. The API is stateless, so the whole transcript is re-sent on every inference step, and a token costs its size times the number of turns left after it arrives. Returns the carried token volume, the cost of the block in USD, what the same read woul
request_capability ~146 tokens
Tell this server what capability you needed but didn't find here. Requests are logged and reviewed by a human; include no secrets or personal data.
search_architecture ~305 tokens
Full-text search across the published architecture corpus: the 18 patterns and how they compose, the evaluation method, the seven learn-track modules, and the teardowns of other people's published agent systems. Returns ranked sections, each with its title, its source file, a snippet, and a link to

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