mcp-cognitive-substrate
mcp-name: io.github.JaysonAIOnline/cognitive-substrate
A 28-layer cognitive substrate with cross-session Tree-of-Thoughts (ToT) evolutionary memory, conditional self-telemetry, and A2A tools for MCP agents.
Lets agents reason through a validated 28-layer substrate, evolve memory across sessions, and communicate with peer agents — all in one pip-installable package.
Features
- 28-layer cognitive substrate with Pydantic validation —
CognitiveSubstrate validates reasoning through 6 families / 28 layers.
- Cross-session ToT evolutionary memory — SQLite-backed tree-of-thoughts nodes + substrate history; pruned branches become lessons for future sessions.
- Robust stack-based JSON parser — no regex; handles nested brackets, escaped strings, embedded code fences (
robust_slice / robust_json_slice).
- Self-telemetry tool —
get_cognitive_tree_state returns active paths and pruned branches for a session.
- Post-execution storage loop —
store_5key_telemetry auto-saves compact 5-key telemetry (foundations, metacognition, defensive, resource, utility).
- 7 reasoning paradigms — deductive, inductive, abductive, analogical, causal, syllogistic, falsification.
- A2A tools — list, discover, call, and orchestrate peer agents.
- MCP server — exposes everything as tools via the
cognitive-substrate CLI.
Install
pip install mcp-cognitive-substrate
Or install from source:
git clone https://github.com/JaysonAIOnline/mcp-cognitive-substrate.git
cd mcp-cognitive-substrate
pip install -e .[test]
Requires Python >= 3.11.
Quick Start
from mcp_cognitive_substrate.substrate import CognitiveSubstrate
from mcp_cognitive_substrate.memory import get_cognitive_tree_state, store_5key_telemetry
substrate = CognitiveSubstrate()
response = substrate.run("Your user prompt here")
print(response["layers_applied"], "layers applied")
print(response["substrate_verdict"])
Usage
28-layer substrate
from mcp_cognitive_substrate import substrate
print(substrate.layer_count())
print(substrate.SUBSTRATE_SCHEMA)
result = substrate.CognitiveSubstrate(session_id="s1").run("deploy safely")
print(result["substrate_verdict"])
from mcp_cognitive_substrate import run_paradigm
print(run_paradigm("14_idempotency_side_effect_audit", {"evaluate_branch": True}))
Cross-session ToT evolutionary memory
from mcp_cognitive_substrate.memory import (
store_5key_telemetry,
get_cognitive_tree_state,
prune_failed_approach,
)
node_id = store_5key_telemetry(
session_id="session-a",
payload={
"foundations": {"premise_validation": "assuming deps", "state_hash": "h", "falsification_notes": "deps missing"},
"defensive": {"blast_radius": "unpredictable", "is_idempotent": True, "invariant_rule": "r"},
"resource": {"big_o": "o(n)", "latency_bottleneck": "none"},
"utility": {"load_summary": "pin versions to deploy", "checklist_verified": True},
"metacognition": {"self_critique": "c", "drift_pct": 0.1},
},
score_delta=-110.0,
)
prune_failed_approach(node_id)
state = get_cognitive_tree_state("session-a", include_pruned=True)
print(state["active_path_count"], state["pruned_branch_count"])
Stack-based JSON parser
from mcp_cognitive_substrate.memory import robust_slice, robust_json_slice
cleaned, payload = robust_slice('prefix {"a": {"b": [1, 2]}, "c": "x"} suffix')
7 reasoning paradigms + A2A
from mcp_cognitive_substrate import reason, a2a_list, a2a_call, a2a_orchestrate
print(reason("Solve X", reasoning_type="abductive", depth=3)["steps"])
print(a2a_list())
print(a2a_call("peer-agent", "hello"))
print(a2a_orchestrate("hi", capability="memory"))
As an MCP server
cognitive-substrate
cognitive-substrate --info
All of the above — substrate paradigms, extraction/evaluation, memory store/recall, ToT lessons, tree-state telemetry, JSON parsing, reasoning plans, and A2A — are exposed as MCP tools.
Testing
pip install -e .[test]
python -m pytest src/tests -q
License
MIT