Server data from the Official MCP Registry
Unified MCP server for the eleven Kinetic Gain Protocol Suite specs — 63 governance tools.
Unified MCP server for the eleven Kinetic Gain Protocol Suite specs — 63 governance tools.
This is a well-engineered MCP server for the Kinetic Gain Protocol Suite with comprehensive schema validation, proper error handling, and appropriate permission scoping. The codebase demonstrates good security practices with no malicious patterns, hardcoded credentials, or dangerous operations. Minor code quality observations exist but do not meaningfully impact security posture. Supply chain analysis found 3 known vulnerabilities in dependencies (1 critical, 2 high severity). Package verification found 1 issue.
3 files analyzed · 8 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: AUDIT_STREAM_URL
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mizcausevic-dev-mcp-kinetic-gain": {
"env": {
"AUDIT_STREAM_URL": "your-audit-stream-url-here"
},
"args": [
"-y",
"mcp-kinetic-gain"
],
"command": "npx"
}
}
}From the project's GitHub README.
One MCP server, all eleven Kinetic Gain Protocol Suite specs + the v0.1.0 implementation tooling. Drop into Claude Desktop, Cursor, or any MCP-compatible client with a single config entry. The agent gains 63 tools (47 spec + 16 implementation-preview, v0.7.0): AEO Protocol, Prompt Provenance, Agent Cards, AI Evidence Format, MCP Tool Cards, AI Tutor Cards, Student AI Disclosure, Classroom AI AUP, Clinical AI Disclosure, AI Incident Card, AI Procurement Decision Card — plus hash attestation (ed25519), audit-stream event composition + chain verification (offline AND live against a running audit-stream-py via AUDIT_STREAM_URL), cross-spec drift detection, Decision Intelligence preview (Decision Card → PolicyBundle, rubric status inference, incident remediation planning). New in v0.7.0: audit_event_emit, audit_events_query, audit_chain_verify_live — Claude can now read and write to the audit-stream spine from inside a chat.
This is the unified read-side companion to kinetic-gain-visualizer: the visualizer renders any of the 11 specs for humans, this server exposes them as callable tools for agents.
npm install -g mcp-kinetic-gain
Or run without installing via npx:
npx mcp-kinetic-gain
Add to your claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):
{
"mcpServers": {
"kinetic-gain": {
"command": "npx",
"args": ["-y", "mcp-kinetic-gain"]
}
}
}
Restart Claude. All 63 tools appear in the tools panel. Try:
"Use aeo_inspect on https://mizcausevic-dev.github.io to summarize the entity declaration, then use ai_evidence_verify_hash to check the content_hash of an evidence object against my candidate text."
The same binary doubles as a Suite JSON validator outside any MCP host. Useful in CI, pre-commit hooks, or local sanity-checks.
# Validate a single document
npx mcp-kinetic-gain validate path/to/ai-entity.json
# Validate a tree of well-known files
npx mcp-kinetic-gain validate ".well-known/**/*.json"
# Multiple paths or globs
npx mcp-kinetic-gain validate cards/clinical-*.json cards/incident-*.json
# Other commands
npx mcp-kinetic-gain --version
npx mcp-kinetic-gain --help
The CLI auto-detects which Suite spec each file belongs to via its top-level version field (aeo_version, clinical_ai_card_version, aup_version, etc.) and validates it against the same zod schemas the MCP tools use. Output is GitHub-Actions-aware: when GITHUB_ACTIONS=true, failures emit ::error:: workflow commands so they surface as PR annotations.
Exit codes:
| Code | Meaning |
|---|---|
0 | Every matched file passed validation |
1 | At least one file failed validation, failed to parse, or hit a config error |
2 | No file in the input matched a known Suite spec |
3 | Usage error (missing arg, unknown flag) |
Running mcp-kinetic-gain with no arguments still launches the stdio MCP server — existing Claude Desktop / Cursor configs are unaffected.
| Spec | Tool | Notes |
|---|---|---|
| AEO Protocol | aeo_fetch | Fetch the full /.well-known/aeo.json for an origin |
aeo_inspect | Structured summary (entity, claim count, audit mode) | |
aeo_get_claim | Extract a single claim by ID; lists available IDs if missing | |
aeo_well_known_url | Compute the canonical URL without fetching | |
| Prompt Provenance | prompt_provenance_validate | Schema-validate a Prompt Provenance JSON document |
prompt_provenance_inspect | Summary: prompt identity, lineage, approval state, eval suites | |
prompt_provenance_eval_result | Extract one evaluation suite's result by name | |
| Agent Cards | agent_card_well_known_url | /.well-known/agents/<agent_id>.json |
agent_card_inspect | Summary; accepts either url (fetched) or document_json (inline) | |
agent_card_tool_disclosure | List tools the agent declares, with side-effect class + Tool Card URI | |
agent_card_validate | Schema-validate | |
| AI Evidence Format | ai_evidence_validate | Schema-validate |
ai_evidence_inspect | Summary: claim, source, retrieval method, synthesis role, hash | |
ai_evidence_verify_hash | Recompute SHA-256 over canonical text; compare to verification.content_hash | |
| MCP Tool Cards | tool_card_well_known_url | /.well-known/mcp-tools/<tool_name>.json |
tool_card_inspect | Summary: safety profile, refusal modes, test count, p99 latency | |
tool_card_tested_with | Filter the tested-with matrix by LLM substring | |
tool_card_validate | Schema-validate |
Specs with a well-known URL convention (AEO, Agent Cards, Tool Cards) get fetch tools. Specs without one (Prompt Provenance, AI Evidence — these usually travel inline with answers or in repos, not at fixed paths) get parse tools that take a document_json string.
agent_card_tool_disclosure to find a Tool Card URI, then call tool_card_inspect on that URI in the same conversation, all through one serversrc/
├── server.ts # MCP entrypoint, handler dispatch
├── tools.ts # 18 tool descriptors (JSON Schema inputs)
├── schemas.ts # zod schemas for every spec
├── common.ts # fetchJson, canonicalSha256, pretty
└── handlers/
├── aeo.ts
├── prompt-provenance.ts
├── agent-card.ts
├── ai-evidence.ts
└── tool-card.ts
Each handler module is independent and could be split into a separate package if needed.
ai_evidence_verify_hash follows the AI Evidence Format spec's canonical SHA-256 rules:
\nsha256:If your candidate_text produces an unexpected mismatch, check CRLF vs LF and trailing newlines first.
74 unit tests against an in-process Node HTTP server (no external network). Every tool's happy path + at least one error path, plus a live local-HTTP synthetic-index test for incident_index_fetch:
npm install
npm run typecheck
npm test
npm run build
This server: AGPL-3.0. Reference implementation. Commercial SaaS hosts must share modifications back.
The specs themselves: MIT. Maximally permissive. Anyone may implement, validate against, or extend any Kinetic Gain Protocol Suite specification. The dual-license split is deliberate: the protocol stays open, the reference server is copyleft.
| Spec | Tools in this server | Vertical |
|---|---|---|
| AEO Protocol | 4 | Core |
| Prompt Provenance | 3 | Core |
| Agent Cards | 4 | Core |
| AI Evidence Format | 3 | Core |
| MCP Tool Cards | 4 | Core |
| AI Tutor Cards | 4 | EdTech |
| Student AI Disclosure | 4 | EdTech (FERPA/COPPA) |
| Classroom AI AUP | 4 + 1 cross-spec (aup_check_compliance) | EdTech |
| Clinical AI Disclosure | 4 | HealthTech (FDA SaMD + HIPAA) |
| AI Incident Card | 4 + 1 cross-spec (incident_index_fetch) | Cross-cutting (EU AI Act Article 73) |
| AI Procurement Decision Card | 4 | Cross-cutting (buyer-side, OMB M-24-10 / NIST AI RMF rubric-friendly) |
Suite hub: suite.kineticgain.com Companion visualizer: kinetic-gain-visualizer Red-team bench: prompt-injection-bench
Connect: LinkedIn · Kinetic Gain · Medium · Skills
Be the first to review this server!
by Modelcontextprotocol · Developer Tools
Web content fetching and conversion for efficient LLM usage
by Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
by mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.