Click on "Deploy 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-substrate Help 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-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.
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.
Related MCP server: Advanced Cognitive MCP Server
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.
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"])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}))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"])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"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"))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.
pip install -e .[test]
python -m pytest src/tests -q # 16 testsThis server cannot be deployed
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