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kenningai

Temporal Knowledge Substrate

by kenningai
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Temporal Knowledge Substrate

An MCP server that lets LLM agents accumulate organizational knowledge across sessions, scoped by domain, backed by Neo4j. Tools enforce structural invariants so the graph can't corrupt itself — no raw Cypher writes.

Two-layer architecture:

  • Process layer (immutable) — Domains own Runs, Runs chain forward in time via NEXT_RUN. The arrow of time is enforced by the tool, not by instructions.

  • Knowledge layer (append-only) — 10 entity types (System, Process, Configuration, Gotcha, Rationale, etc.) connected by 7 relationship types. Knowledge evolves via EVOLVED_FROM chains — old versions are preserved, never overwritten.

Prerequisites

  • Python 3.10+

  • uv package manager

  • Neo4j 5.x instance (local or remote)

Quick Start

# Install
uv sync

# Run (stdio transport, default for MCP clients)
mcp-temporal-knowledge --db-url bolt://localhost:7687

Configuration

All options can be set via CLI flags or environment variables. CLI takes precedence.

CLI Flag

Env Var

Default

Description

--db-url

NEO4J_URI or NEO4J_URL

bolt://localhost:7687

Neo4j connection URL

--username

NEO4J_USERNAME

neo4j

Neo4j username

--password

NEO4J_PASSWORD

password

Neo4j password

--database

NEO4J_DATABASE

neo4j

Neo4j database name

--transport

NEO4J_TRANSPORT

stdio

Transport: stdio, sse, or streamable-http

--namespace

NEO4J_NAMESPACE

(none)

Tool name prefix (e.g. myapp -> myapp-begin_session)

--server-host

NEO4J_MCP_SERVER_HOST

127.0.0.1

HTTP host (non-stdio transports)

--server-port

NEO4J_MCP_SERVER_PORT

8000

HTTP port (non-stdio transports)

--server-path

NEO4J_MCP_SERVER_PATH

/mcp/

HTTP path (non-stdio transports)

MCP Client Configuration

Claude Desktop / Claude Code

Add to your MCP config:

{
  "mcpServers": {
    "temporal-knowledge": {
      "command": "mcp-temporal-knowledge",
      "args": ["--db-url", "bolt://localhost:7687"]
    }
  }
}

HTTP transport

mcp-temporal-knowledge \
  --db-url bolt://localhost:7687 \
  --transport streamable-http \
  --server-host 0.0.0.0 \
  --server-port 8000 \
  --allow-origins "http://localhost:3000" \
  --allowed-hosts "localhost,127.0.0.1"

Tool Surface (20 tools)

Session workflow

Every session follows: create_domain (once) -> begin_session -> create/evolve/confirm knowledge -> end_session

Tool

Description

list_domains

List all domains with run counts and last activity

create_domain

Create a knowledge domain (idempotent)

begin_session

Start a session — returns run_id needed by all knowledge tools

end_session

Close a session with a summary of what was learned

Knowledge mutation

Tool

Description

create_knowledge

Create entities (System, Gotcha, Rationale, etc.) linked to the current session

evolve_knowledge

Create a new version of an entity — old version preserved via EVOLVED_FROM

confirm_knowledge

Record that entities were reviewed and found unchanged

create_connections

Link entities (ENABLING, REQUIRING, CAUSING, etc.)

Querying

Tool

Description

search_knowledge

Fulltext search across names and descriptions

get_domain_state

All current (head-of-chain) entities for a domain

get_run_history

Session history — who worked on what, when

read_cypher

Read-only Cypher escape hatch (writes rejected)

Taxonomy & Analytics

Tool

Description

list_knowledge_types

The 10 entity types with descriptions

list_connection_types

The 7 knowledge + 4 process edge types

gds_create_projection

Create a GDS graph projection for analytics

gds_drop_projection

Drop a GDS projection

gds_pagerank

PageRank centrality

gds_betweenness

Betweenness centrality (bridge nodes)

gds_louvain

Louvain community detection

gds_wcc

Weakly connected components

Development

# Install with dev dependencies
uv sync --dev

# Run tests
uv run pytest

# Type checking
uv run pyright

See HOWTO.xml for the ontological commitments and the tools' behavior. Provide this to the LLM as invariant scaffolding.

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