MCP server

Saferagenticai

io.github.NellInc/saferagenticai-mcp

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

Read-only tools over the Safer Agentic AI framework: 238 patterns + 14 heuristics.

Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

10

Token cost

About 1,624 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 CursorAdd to VS CodeVS 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 saferagenticai-mcp https://mcp.saferagenticai.org/mcp

Claude Desktop

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

Codex (config.toml)

[mcp_servers.saferagenticai-mcp]
url = "https://mcp.saferagenticai.org/mcp"

Gemini CLI

{"mcpServers":{"saferagenticai-mcp":{"httpUrl":"https://mcp.saferagenticai.org/mcp"}}}

Cursor

{"mcpServers":{"saferagenticai-mcp":{"url":"https://mcp.saferagenticai.org/mcp"}}}

VS Code

{"servers":{"saferagenticai-mcp":{"type":"http","url":"https://mcp.saferagenticai.org/mcp"}}}

Antigravity

agy mcp add saferagenticai-mcp https://mcp.saferagenticai.org/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":"saferagenticai-mcp","server_url":"https://mcp.saferagenticai.org/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"saferagenticai-mcp","server_url":"https://mcp.saferagenticai.org/mcp"}

Tools

• find_patterns_for_task Runs commands · ~210 tokens
  Given a natural-language task description (e.g., 'I'm building a tool-using agent that runs shell commands'), return the most relevant patterns grouped by suite. Use this as a starting point for any cross-cutting design question; then follow up with get_requirement on specific pattern_ids. Defaults
• get_cross_references ~169 tokens
  Return outgoing adjacencies for a pattern. `explicit_cross_references` are author-asserted (each pattern's `cross_references` YAML field). `inferred_adjacent` (when include_inferred=true) currently returns *same-suite siblings only* — it does not do semantic similarity. Treat inferred entries as 'ne
• get_operational_heuristic ~120 tokens
  Retrieve a single operational heuristic by id (e.g., 'OH::geoffrey-pattern'). Returns the full entry: principle, framework mapping, evidence sources from production deployment, design patterns, anti-patterns, and discovery narrative.
• get_requirement ~133 tokens
  Retrieve one subgoal (framework normative content + Pattern layer guidance) by pattern_id (e.g., 'D3::idx2::sandboxing') or display_id (e.g., 'D3.2'). display_id may resolve to multiple subgoals — underlined variants share display_ids.
• get_reverse_references ~108 tokens
  Return patterns that reference the given pattern_id in their cross_references. Complement to get_cross_references (outgoing); this shows incoming. Use to find all consumers of a given pattern.
• list_operational_heuristics ~220 tokens
  List operational heuristics distilled from production agentic AI deployment (Claude Code, Rewind). These are cross-cutting safety principles discovered through building and operating AI agents, mapped to framework suites. Optional filters: suite_id (heuristics relevant to a specific suite), query (k
• list_requirements ~201 tokens
  List subgoals matching filters (suite_id, suite_type, content_type, min_confidence, missing_pattern_only). Results capped by limit (default 50, max 100).
• list_suites ~73 tokens
  List all 16 suites in the SaferAgenticAI framework (9 drivers + 7 inhibitors) with subgoal counts and titles. Call this first to orient.
• resolve_id ~146 tokens
  Resolve a loose reference (partial id, display_id, slug fragment, or title keyword) to canonical pattern_id(s). Call this when you have a rough reference and need the exact id before calling get_requirement. Always returns candidates — never 'not found'.
• search_patterns ~244 tokens
  Field-weighted keyword search across the framework. Terms match at word starts on lowercased text and are IDF-weighted, so rare terms outrank ubiquitous ones; field weights: title 10x, summary 4x, SFR text 3x (all of a subgoal's SFRs scored as one field), description 2x, pattern body 1x. `matched_in

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• Searching your documents (RAG)

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