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 — saferagenticai-mcp
Official MCP Registry — 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 <repo>/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/<SUITE>/<pattern_id>.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.