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  3. Cognitive Substrate
C
Health: ActiveRecent health check succeeded.Last checked 9/7/2026, 5:45:46 PM

Cognitive Substrate

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository1 GitHub StarsTotal stargazers on GitHub for the source repository (1 stars).

28-layer cognitive substrate with cross-session ToT evolutionary memory and self-telemetry tools

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "cognitive-substrate": {
      "command": "uvx",
      "args": [
        "mcp-cognitive-substrate"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

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 β€” all in one pip-installable package.

Features

  • 28-layer cognitive substrate with Pydantic validation β€” CognitiveSubstrate validates reasoning through 6 families / 28 layers.
  • Cross-session ToT evolutionary memory β€” SQLite-backed tree-of-thoughts nodes + substrate history; pruned branches become lessons for future sessions.
  • Robust stack-based JSON parser β€” no regex; handles nested brackets, escaped strings, embedded code fences (robust_slice / robust_json_slice).
  • Self-telemetry tool β€” get_cognitive_tree_state returns active paths and pruned branches for a session.
  • Post-execution storage loop β€” store_5key_telemetry auto-saves compact 5-key telemetry (foundations, metacognition, defensive, resource, utility).
  • 7 reasoning paradigms β€” deductive, inductive, abductive, analogical, causal, syllogistic, falsification.
  • A2A tools β€” list, discover, call, and orchestrate peer agents.
  • MCP server β€” exposes everything as tools via the cognitive-substrate CLI.

Install

Terminal
pip install mcp-cognitive-substrate

Or install from source:

bash
git clone https://github.com/JaysonAIOnline/mcp-cognitive-substrate.git
cd mcp-cognitive-substrate
pip install -e .[test]

Requires Python >= 3.11.

Quick Start

server.ts
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

server.ts
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

server.ts
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

server.ts
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

server.ts
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

bash
cognitive-substrate          # starts stdio MCP server
cognitive-substrate --info   # prints package summary

All of the above β€” substrate paradigms, extraction/evaluation, memory store/recall, ToT lessons, tree-state telemetry, JSON parsing, reasoning plans, and A2A β€” are exposed as MCP tools.

Testing

Terminal
pip install -e .[test]
python -m pytest src/tests -q     # 16 tests

License

MIT

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
1
Stargazers on the source repository.
Last commit
2d ago
Most recent push to the default branch.

Reviews

No reviews yet β€” be the first to share how this listing worked for you.

Frequently Asked Questions about Cognitive Substrate

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "cognitive-substrate": { "command": "npx", "args": ["-y", "cognitive-substrate"] } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimePython
Last updatedSep 5, 2026
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars1
GitHub Star CountTotal stargazers on GitHub representing community popularity (1 stars).
Last commit2d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 5, 2026
40Quality signal: Fair Β· 40/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity4/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 4h ago via OSV.dev Β· mcp-cognitive-substrate (PyPI)

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