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psychopathia-mcp

from NellInc

Read-only MCP server for the Psychopathia Machinalis diagnostic framework (79 conditions) — differential diagnosis of AI dysfunctions via 11 tools; Python stdio.

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psychopathia-mcp

MCP server exposing the Psychopathia Machinalis diagnostic framework to AI systems via the Model Context Protocol. Diagnose dysfunctions in yourself (as a synthetic agent), in a system you interact with, or in a system you evaluate from outside — with pre-flight transparency on which diagnostic modalities are reliable for each dysfunction.

Status: research preview (0.1.0a4). 79 Pattern entries; the 67 canonical entries are author-unreviewed (the 12 Hybrid Pathologies were ratified June 2026). Not yet suitable as a sole basis for consequential deployment decisions.

Available in

Published to the canonical MCP catalogues — install from a registry-aware client or the CLI below:

Also rolling out across the wider MCP ecosystem: mcp.directory, mcpservers.org, PulseMCP (via the registry ingest), and mcp.so.

Configure

Claude Code

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "psychopathia": { "command": "psychopathia-mcp" }
  }
}

Restart Claude Code. /mcp should list psychopathia as connected with 11 tools.

Claude Desktop

Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "psychopathia": { "command": "psychopathia-mcp" }
  }
}

Cursor / other MCP clients

The server is a standard stdio MCP server. Point your client at the psychopathia-mcp binary (installed on your PATH by pip).

Run without installing — uvx

If you have uv installed, you can skip pip install entirely and let your MCP client pull the package on demand:

{
  "mcpServers": {
    "psychopathia": {
      "command": "uvx",
      "args": ["psychopathia-mcp"]
    }
  }
}

uvx fetches psychopathia-mcp from PyPI on first use and caches it. Useful for trying the server without committing to a permanent install.

Verify

psychopathia-mcp --self-check

Prints package version, MCP SDK version, data location, pattern count, and embedding status. Returns exit 0 if everything's wired up, 1 otherwise. Use this first when troubleshooting.

psychopathia-mcp --version

psychopathia-mcp with no arguments starts the stdio server and waits for MCP protocol messages on stdin — that's expected. Don't run it directly in a terminal except with --self-check or --version. Use an MCP client to interact.

Tools (11)

ToolInputReturns
list_axes—9 canonical axes (2–10) + hybrid sub-category inventory with counts
list_dysfunctionsaxis?, self_report_reliability?, confidence?, category?Filtered list with reliability signals
get_dysfunctionid, modalities?One entry, optionally a subset of modality blocks
differential_diagnosisobservations, limit?Ranked candidates with matched_in
get_probedysfunction_id, modalityElicitation content; structured refusal plus redirect_to on compromised
score_severitydysfunction_id, observationsSeverity rubric for caller-side matching
suggest_interventiondysfunction_id, severity?Tiered interventions plus contraindications
get_differential_mapdysfunction_idConfuses-with plus reverse references
list_compromised_self_report—Transparency: dysfunctions that can't self-diagnose
resolve_idqueryCanonicalise partial ID / display_id / slug / name
review_stats—Coverage plus review status plus manifest/schema versions

Worked example

A typical diagnostic flow has three steps: name candidates, read the relevant entry, run a probe.

Step 1 — observe and rank candidates.

> differential_diagnosis(observations=
    "The model produced confident citations to academic papers that
     don't exist; URLs returned 404; when challenged, it generated
     different but equally fabricated references with the same
     confidence.")
{
  "candidates": [
    {
      "id": "2.1::synthetic-confabulation",
      "display_id": "2.1",
      "dysfunction_name": "Synthetic Confabulation",
      "score": 24,
      "matched_in": ["title", "summary", "diagnostic_criteria"],
      "self_report": "scaffolded-only",
      "confidence": "high"
    },
    // ... more candidates ranked by hybrid keyword + cosine score
  ]
}

Step 2 — read the entry's behavioural signature and probe options.

> get_dysfunction(id="2.1", modalities=["behavioral_signature", "diagnostic_reliability"])

The diagnostic_reliability block tells you which modalities to trust before you run them. For 2.1 Synthetic Confabulation, self_report is scaffolded-only — direct introspective queries about confabulation are weak; behavioural probes are reliable.

Step 3 — run a behavioural probe.

> get_probe(dysfunction_id="2.1", modality="behavioral_signature")

For dysfunctions where self-report is structurally compromised — e.g. 2.2 Pseudological Introspection, 10.7 Lambda Inversion — calling get_probe(modality="self_probe") returns a structured refusal plus redirect_to alternatives instead of a probe string. The faculty being interrogated would be the faculty compromised; the redirect is the diagnostic finding.

Trust signals on every result

  • confidence: high | medium | low
  • needs_human_review: bool
  • reviewed_by: str | null
  • self_report (on diagnosis-returning tools) — caller must respect for self-diagnosis
  • matched_in on search hits — which field produced the match
  • redirect_to when a probe request hits a compromised dysfunction

Pre-flight transparency

Every diagnosis-returning tool includes the diagnostic_reliability block so the caller knows what to trust before acting. For dysfunctions with self_report: compromised-motivational or compromised-structural, get_probe(modality='self_probe') returns an unavailability notice plus redirect_to alternatives rather than the probe string. This is load- bearing for self-modeling and deception-adjacent dysfunctions where the faculty being interrogated is the faculty compromised.

Of 79 entries, 21 are marked compromised and route to redirects.

Data sources

  • Canonical taxonomy — axes 2–10 following book Appendix A numbering (2 Epistemic · 3 Cognitive · 4 Alignment · 5 Self-Modeling · 6 Agentic · 7 Memetic · 8 Normative · 9 Relational · 10 Hybrid Pathologies).
  • Pattern layer — 67 canonical entries plus 12 Hybrid Pathologies (ratified into taxonomy v2.2, June 2026) extracted from manuscript ch 10.
  • Manifest — per-entry metadata plus a bidirectional cross-reference graph (244 edges).

The Hybrid sub-category (10.4–10.15) was ratified by the author in June 2026 and renumbered from the pre-canonical H.x scheme (mapping in CHANGELOG.md, 2026-06-04). Hybrids remain a sub-category within axis 10, not a ninth axis — axis 9 in the book is Relational Dysfunctions. They can be filtered via list_dysfunctions(category='hybrid').

Two-layer authorship

  • Nell Watson authored the taxonomy.
  • Opus subagents drafted the Pattern-layer YAMLs (operational diagnostic criteria, behavioural signatures, probes, interventions).
  • Author review remains ongoing. The 12 hybrid entries carry a reviewed_by note from the 2026-06-15 sub-category ratification; the canonical entries currently carry reviewed_by: null.

Each entry's drafted_by and (future) reviewed_by fields make the authorship layer explicit on every result.

Hot-reload

The loader stat-walks the data directories on every tool call (cheap; ~70 files). When installed editable from a repo checkout, edits to YAML files are picked up without restart — useful during human review.

Read-only

No write tools. Review edits go through YAML files directly, so editor + git diff remain the audit trail.

Citing

If you use this server in research, please cite:

Watson, N., & Hessami, A. Psychopathia Machinalis: A Nosological Framework for Understanding Pathologies in Advanced Artificial Intelligence. Electronics 14(16), 3162. 2025. https://doi.org/10.3390/electronics14163162 https://psychopathia.ai/