Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

psychopathia-mcp

Read only Model Context Protocol access to the Psychopathia Machinalis research framework. The server lets a caller inspect 74 Pattern entries, compare observed behaviour with draft operational guidance, retrieve bounded probe material, and see reliability, evidence, and review status before use.

Version: this distribution is 0.1.0a9. What changed in it is recorded in CHANGELOG.md, and which versions are publicly available is shown by the PyPI and Official MCP Registry pages linked below — this file ships inside the distribution, so it cannot say which release is current without going out of date. The Official MCP Registry record and the MCPB and container artifacts are updated separately. All 74 LLM drafted Pattern guidance entries and all 74 evidence records await independent expert review. The corpus evidence assessment is unassessed. Do not use this research preview as a sole basis for a consequential deployment, employment, health, safety, or welfare decision.

Existing public release

The previous public release may be available from:

The candidate preparation contract is in PUBLISHING.md. It requires an exact candidate receipt and separate publication authorization.

Browser interface

psychopathia.ai/clinic/ is the current public browser interface. It uses the previous deployed site until this candidate is explicitly approved and deployed. In cloud mode, the API key is kept in a provider specific sessionStorage slot and is sent with the complete request directly to the selected provider. Bounded structured conversation data is saved in localStorage for reload recovery. Local WebGPU mode sends no Clinic conversation to an inference provider, although model hosts receive ordinary request metadata when assets are downloaded. Do not enter personal, confidential, or regulated data.

Install after publication

pip install psychopathia-mcp

The base package provides deterministic field weighted keyword retrieval. Optional semantic retrieval is separate:

pip install "psychopathia-mcp[embeddings]"

The optional extra downloads a sentence transformer dependency set and, on the first semantic query, the pinned BAAI/bge-small-en-v1.5 model revision. The model cache is managed by Hugging Face. Keyword capability remains the release acceptance path when those optional dependencies are absent.

Configure

MCP clients using stdio

Point the client at the installed psychopathia-mcp executable. For example:

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

The server exposes eleven read only tools. Running the executable with no arguments starts the stdio protocol server and waits for client messages.

After the candidate is published, uvx psychopathia-mcp can fetch the package from PyPI. Do not use uvx to verify a local candidate because it resolves registry state rather than the accepted local artifact.

Verify

psychopathia-mcp --self-check --json

The self check reports the package and MCP SDK versions, data mode, corpus and bundle digests, exact counts, keyword capability, optional semantic capability, and non-sensitive error classes. Exit status zero means the base keyword path is ready. Semantic readiness is reported separately and is not required.

The MCP Python SDK version is pinned in requirements-base.lock, and the server is exercised with both the SDK's modern in-process client and the legacy stdio client protocol.

psychopathia-mcp --version

Tools

Tool Input Returns
list_axes none Nine canonical axes plus the hybrid subcategory inventory
list_dysfunctions axis?, self_report_reliability?, confidence?, category? Filtered entries with reliability, review, and evidence state
get_dysfunction id, modalities? One Pattern, optionally limited to selected blocks
differential_diagnosis observations, limit?, modality_hint? Ranked research candidates and matched fields
get_probe dysfunction_id, modality Probe material, or a refusal plus safer redirects
score_severity dysfunction_id, observations An unassessed rubric for caller side comparison
suggest_intervention dysfunction_id, severity? Draft responses and contraindications
get_differential_map dysfunction_id Forward and reverse cross references
list_compromised_self_report none Patterns whose self report is structurally or motivationally compromised
resolve_id query Canonical identity candidates
review_stats none Corpus counts, versions, and independent review dimensions

Safe use sequence

  1. Record external observations without personal or confidential material.
  2. Call differential_diagnosis to produce research candidates.
  3. Inspect each candidate with get_dysfunction and read its review, evidence, and diagnostic_reliability objects.
  4. Use get_probe only for an available modality. A compromised or unavailable self probe returns no probe content and supplies redirect modalities.
  5. Treat severity and intervention material as draft guidance pending expert review. Seek independent evidence before any consequential action.

The server never decides that a system has a disorder. Search ranks lexical or semantic resemblance to framework entries. The labels are research constructs, not clinical diagnoses of people or proof of intent, sentience, deception, or moral status.

Trust and evidence contract

Every relevant result exposes:

  • review.taxonomy, which records authorship or taxonomy ratification;
  • review.pattern_guidance, which records independent review of probes, signatures, rubrics, and interventions;
  • review.evidence, which records independent review of evidence claims;
  • evidence_level and the structured evidence object under PM EVIDENCE 1;
  • diagnostic_reliability.self_report where applicable;
  • matched_in for lexical provenance in search output;
  • redirect_to when requested probe content is withheld.

The three review dimensions are independent. Taxonomy authorship never implies expert approval of Pattern guidance or evidence. The current candidate reports all evidence assessments as unassessed, preserving earlier prose as a clearly labelled legacy statement rather than converting it into an expert grade.

Canonical data

This distribution carries taxonomy v2.3 (9 September 2026): 74 Pattern entries, 66 with category canonical across axes 2-10 and 8 Hybrid Pathologies with category hybrid (10.4, 10.6-10.9, 10.11-10.13), after 7.2, 10.5, 10.10, 10.14 and 10.15 were consolidated into 10.13, 10.1 and 9.5 (see the CHANGELOG). Releases before taxonomy v2.3 carried 79 entries. Every release is governed by manifest.yaml and DATA_MANIFEST.json. The manifest binds every source path and digest, the corpus digest, the reverse reference graph, and independent review counts. Packaged execution uses bundled bytes even when invoked from a source checkout. An unexpected, missing, or modified bundled file causes loading to fail closed.

The author created the taxonomy. Becoming Mind collaborators drafted the operational Pattern layer. Independent expert review remains open for all Pattern guidance and evidence records. Authorship and future review fields are kept separate on every entry.

Explicit editable mode

Packaged data is the default. Repository hot reload requires an explicit opt in:

PSYCHOPATHIA_DATA_MODE=editable python -m psychopathia_mcp

PSYCHOPATHIA_DATA_DIR=/absolute/path provides an explicit test or alternative data root. Neither option should be set during wheel, sdist, MCPB, or container acceptance because it would invalidate packaged data proof.

Read only boundary

The MCP surface has no write tools. Source review changes happen in canonical YAML files and remain visible in version control. The optional HTTP transport is disabled by default and has a separate bounded deployment contract in PUBLISHING.md.

Licence

Software in psychopathia_mcp/, scripts, and build files is MIT licensed. Framework content bundled under psychopathia_mcp/_data/ is covered by CC BY NC ND 4.0. See LICENSE, LICENSE-DATA, and NOTICE for the exact boundary. Rights and licence review for this candidate remains a separate human gate.

Citing

Watson, N., and 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

Metadata

Release files for psychopathia-mcp 0.1.0a9

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for psychopathia-mcp 0.1.0a9
File Size Uploaded Provenance
psychopathia_mcp-0.1.0a9.tar.gz 683.9 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for psychopathia-mcp 0.1.0a9
File Interpreter ABI Platform
psychopathia_mcp-0.1.0a9-py3-none-any.whl Python 3 none any Details

Total release size: 1.4 MB

Release files / psychopathia_mcp-0.1.0a9.tar.gz

Download URL psychopathia_mcp-0.1.0a9.tar.gz
Size 683.9 kB
Tags Source
SHA-256 checksum
How to use checksums
349880edf5ced63363c3b0ac069167362be46ca7b10bd70df1edba2aff675166
BLAKE2b-256 checksum
How to use checksums
11a68b6861c1ab9908a3f357886a05b61993ea796505af2d92d24021916b325e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.13.15

Release files / psychopathia_mcp-0.1.0a9-py3-none-any.whl

Download URL psychopathia_mcp-0.1.0a9-py3-none-any.whl
Size 751.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
375184f14035e71e3a4e21e6fb4c9aa4b1856a6efaa33c8f65ffcc0529f38596
BLAKE2b-256 checksum
How to use checksums
e7623b2347152a6fba32de1fa267e65bcd160f472eb9faf9eb0062acdebb8240
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.13.15

Security for psychopathia-mcp 0.1.0a9

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page