Saferagenticai MCP

Name: Saferagenticai MCP
Author: NellInc

by NellIncversion 0.4.2i★ –iComputer or cloud

Registry nameio.github.NellInc/saferagenticai-mcp

Install

Overview

Description from the maker (original English)

Read-only tools over the Safer Agentic AI framework: 239 patterns + 15 heuristics.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi3 of 3 passedChecked from public information; code not scanned yet
UsageiVery new↓ 255/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Computer or cloud
Transport: stdio, Streamable HTTP
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 3 of 3 passed
Runs on the maker’s server: what you ask it goes through their server.
Can do 10 things (tools) list_suites, get_requirement, list_requirements, search_patterns …
The registry lists no key to fill in.

You need

Runs in the cloudNothing to install; it connects over the internet
PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Works in Claude on the web, desktop and mobile:

Open Settings → Connectors
Press Add custom connector
Paste the address below and press Add
If it asks you to sign in, sign in with the maker

https://mcp.saferagenticai.org/mcp

Or run it on your computer

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "saferagenticai": {
      "command": "uvx",
      "args": [
        "saferagenticai-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add --transport http saferagenticai https://mcp.saferagenticai.org/mcp

Or run it on your computer

claude mcp add saferagenticai -- uvx saferagenticai-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Or run it on your computer

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "saferagenticai": {
      "command": "uvx",
      "args": [
        "saferagenticai-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Or run it on your computer

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "saferagenticai": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "saferagenticai-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

ChatGPT connects only to servers on the internet:

Open ChatGPT on the web → Settings → Connectors (Apps)
Press Create or Add (some plans need Developer mode under Advanced first)
Paste the address below, give it a name and save
If it asks you to sign in, sign in with the maker

https://mcp.saferagenticai.org/mcp

Menu names can differ slightly by plan and over time.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Tool names and descriptions exactly as the server reported them on Nov 7, 2026. The maker wrote them, not us; we show them as plain text.

list_suites
List all 16 suites in the SaferAgenticAI framework (9 drivers + 7 inhibitors) with subgoal counts and titles. Call this first to orient.
get_requirement
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.
list_requirements
List subgoals matching filters (suite_id, suite_type, content_type, min_confidence, missing_pattern_only). Results capped by limit (default 50, max 100). Every pattern is a model-generated draft (review_status generated_draft; there is no human-review process); confidence is the
search_patterns
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
get_cross_references
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 inf
resolve_id
Resolve a loose reference (partial id, display_id, slug fragment, or title keyword) to canonical pattern_id(s). Case, separator and G/I prefix are normalised (d2.1, D2-1, 'D2 1', G1.2); a hyphen or space returns both the numbered requirement and its underlined variant. Call this
find_patterns_for_task
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 pa
get_reverse_references
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
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 speci
get_operational_heuristic
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.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account NellInc, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
✓ Pass
Endpoint responds
Responded in 900 ms (HTTP 200)
– Not applicable
Sign-in with OAuth
No sign-in needed
✓ Pass
No hidden instructions in tool descriptions
Read 10 tool descriptions and found no hidden instructions to the AI

Compared with others in Otheri

More stars than 0% of the other 18444 servers in this category
Published to the registry more recently than 95% of the other 18444 servers in this category
Basic checks equal to or better than 100% of the other 18444 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Saferagenticai MCP safe?

It passed 3 of 3 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

Either way works: install it on your computer or connect to the cloud version.

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.4.2) reached the registry 2 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Egovframe Scaffold MCP

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Egovframe Scaffold MCP

eGovFrame (전자정부 표준프레임워크) projects: templates, components, CRUD, build, 5.x migration, dependencies

by EricSeokgonEarly↓ 1.4K/wkUpdated today

Runs on your computer

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

SaferAgenticAI MCP Server

Serves the SaferAgenticAI framework (canonical criteria + Implementation Patterns layer) to coding assistants via the Model Context Protocol.

Available in

Published to the canonical MCP catalogues — install from a registry-aware client or the CLI below:

[PyPI](https://pypi.org/project/saferagenticai-mcp/) — saferagenticai-mcp
[Official MCP Registry](https://registry.modelcontextprotocol.io/v0/servers?search=io.github.NellInc/saferagenticai-mcp) — io.github.NellInc/saferagenticai-mcp

Also rolling out across the wider MCP ecosystem: mcp.directory, mcpservers.org, PulseMCP (via the registry ingest), and mcp.so.

Install

Pick the path that matches your setup.

Option 1 — `uvx` (fastest, no manual venv)

If you have uv installed, point your MCP client at:

uvx --from git+https://github.com/NellInc/saferagenticai-mcp saferagenticai-mcp

uv handles isolation and caches the install. Works for single-command config lines in ~/.claude/mcp.json.

Option 2 — `pipx` (isolated global install)

pipx install "git+https://github.com/NellInc/saferagenticai-mcp"

Exposes saferagenticai-mcp globally; updated with pipx upgrade saferagenticai-mcp.

Option 3 — manual venv (works offline from a checkout)

Homebrew / system Python blocks direct pip install under PEP 668, so if you've cloned the repo and want an editable install:

python3 -m venv research/mcp/.venv

research/mcp/.venv/bin/pip install -e research/mcp/server

Produces research/mcp/.venv/bin/saferagenticai-mcp. Pattern YAML edits in the repo are picked up live (editable mode).

Option 4 — from PyPI

pipx install saferagenticai-mcp

or, with the modern uv toolchain:

uv tool install saferagenticai-mcp

or plain pip:

pip install --user saferagenticai-mcp

For audit-trail reproducibility, pin the version: pipx install saferagenticai-mcp==0.3.6. The package bundles criteria-v1.json + 238 pattern YAMLs + 4 exemplars

operational_heuristics.yaml inside saferagenticai_mcp/_data/, so a

wheel install works without any repo checkout. (The 0.3.0 wheel predates the corpus extension and bundles only 214 patterns, no heuristics; 0.3.1 is the first complete build.)

Configure (Claude Code)

Add to ~/.claude/mcp.json (or your IDE's MCP config). Pick the variant that matches your install option.

With `uvx`

"mcpServers": { "saferagenticai": { "command": "uvx", "args": [ "--from", "git+https://github.com/NellInc/saferagenticai-mcp", "saferagenticai-mcp" ] } } }

With `pipx` or manual venv

"mcpServers": { "saferagenticai": { "command": "/absolute/path/to/saferagenticai-mcp" } } }

For a manual venv checkout, the absolute path is /research/mcp/.venv/bin/saferagenticai-mcp.

Restart Claude Code / your IDE after editing. The server will load on the first tool call from your assistant.

Tools (12 total)

Tool	Input	Returns
`list_suites`	—	16 suites with titles and subgoal counts
`get_requirement`	`id`, `include_pattern`	one subgoal + its Pattern layer; falls back to fuzzy candidates if no exact match
`list_requirements`	suite/type/content_type/confidence filters	filtered subgoal list with reliability signals
`search_patterns`	`query`, `limit`, `verbosity`	field-weighted ranked matches with `matched_in` and (in full mode) snippets + confidence flags. Field weights: title 10×, summary 4×, sfr 3×, description 2×, body 1×
`get_cross_references`	`id`, `include_inferred`	outgoing adjacencies
`get_reverse_references`	`id`	incoming adjacencies (who cites this pattern)
`resolve_id`	`query`	canonicalise a partial id, slug fragment, or display_id; always returns candidates
`find_patterns_for_task`	`task`, `limit`, `verbosity`	top patterns grouped by suite for a task description; defaults to compact mode for cheap triage
`list_unreviewed`	`limit`	patterns without `reviewed_by`, sorted low-confidence first
`review_stats`	—	coverage %, per-suite, per-confidence; plus validation issue count
`list_operational_heuristics`	`suite_id`?, `query`?	operational heuristics distilled from production agentic AI deployment, optionally filtered by suite or keyword
`get_operational_heuristic`	`id`	single operational heuristic by id (e.g. `OH::geoffrey-pattern`); returns full entry with principle, framework mapping, design patterns, and discovery narrative

Data sources

Normative framework: framework/catalog/, loaded through the generated assessor/src/data/criteria-v1.json projection
Pattern layer: research/mcp/suites/ / .yaml (238 files)
Exemplars: research/mcp/exemplars/*.yaml (fallback for four anchor subgoals)
Operational heuristics: research/mcp/operational_heuristics.yaml (14 heuristics)

At startup the server loads both and builds an in-memory index keyed by pattern_id. display_id lookups are also supported but may resolve to multiple subgoals (underlined variants).

Smoke test (without MCP installed)

python3 -c "

from saferagenticai_mcp.framework_loader import load_framework idx = load_framework() print(f'{len(idx.subgoals)} subgoals, {sum(1 for s in idx.subgoals.values() if s.has_pattern)} with patterns') "

Versioning

Canonical framework: follows criteria-v1.json's version field.
Pattern layer: v1-draft while this directory is being populated; v1 once reviewed.
Server: semantic versioning. Current release is 0.3.6 (framework 1.3-draft, full 238-pattern corpus, and operational heuristics bundled). Pin explicitly for audit reproducibility.

What's already built in

Hot reload — server stat-walks the source tree on each tool call; edits show up without restart.
Load-time validation — required fields, content_type enum, confidence enum. Invalid patterns log WARNINGs but don't fail the server.
`find_patterns_for_task` — natural-language task → top patterns grouped by suite. Replaces the need for a separate embedding index at current scale.
Reverse xref index — built at load, queried by get_reverse_references.

Not implemented

Auth / remote transport (stdio only).
Embedding-based semantic search — the field-weighted keyword scoring is sufficient at 238 patterns; embeddings would be worth it at 10× this scale.
mark_reviewed write tool — deliberately not added. Phase 3 review edits go through the YAML directly (editor + git diff = auditable); the MCP stays read-only.

The broader agent-native architecture proposes composite orientation, context-pack, workspace, planning, action, and verification operations. They are explicitly not part of the current 0.3.6 interface. See ../../architecture/README.md for the target and compatibility plan.

The authoritative catalog maps current MCP identifiers and slugs to permanent requirement IDs. Server 0.3.6 retains its twelve-tool interface, loads the generated packaged snapshot, validates saai.catalog.v1, and reports the shared snapshot hash.

License

This server (the code in this directory) is licensed MIT — see LICENSE.

The safety-framework *content* it serves (the patterns, canonical criteria, and operational heuristics bundled under saferagenticai_mcp/_data/) is part of the SaferAgenticAI framework, published under CC-BY-4.0 at the repository root. Attribution: Nell Watson and the Agentic AI Safety Community of Practice.

Saferagenticai MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Or run it on your computer

Or run it on your computer

Or run it on your computer

Or run it on your computer

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Egovframe Scaffold MCP

README from the maker

SaferAgenticAI MCP Server

Available in

Install

Option 1 — uvx (fastest, no manual venv)

Option 2 — pipx (isolated global install)

Option 3 — manual venv (works offline from a checkout)

Option 4 — from PyPI

or, with the modern uv toolchain:

or plain pip:

Configure (Claude Code)

With uvx

With pipx or manual venv

Tools (12 total)

Data sources

Smoke test (without MCP installed)

Versioning

What's already built in

Not implemented

License

Option 1 — `uvx` (fastest, no manual venv)

Option 2 — `pipx` (isolated global install)

With `uvx`

With `pipx` or manual venv