io.github.JaysonAIOnline/cognitive-substrate
OfficialClick on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@io.github.JaysonAIOnline/cognitive-substrateHelp me reason through deploying safely and save lessons for the next session."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 —
CognitiveSubstratevalidates 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_statereturns active paths and pruned branches for a session.Post-execution storage loop —
store_5key_telemetryauto-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-substrateCLI.
Related MCP server: Advanced Cognitive MCP Server
Install
pip install mcp-cognitive-substrateOr 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
# Layer count and schema
print(substrate.layer_count()) # 28
print(substrate.SUBSTRATE_SCHEMA) # the full 6-family schema
# Validate a prompt through the substrate
result = substrate.CognitiveSubstrate(session_id="s1").run("deploy safely")
print(result["substrate_verdict"]) # heuristic pruning verdict
# Run a single paradigm
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')
# payload == {"a": {"b": [1, 2]}, "c": "x"}; cleaned == "prefix 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 # starts stdio MCP server
cognitive-substrate --info # prints package summaryAll 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 # 16 testsLicense
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