Read-only tools over the Psychopathia Machinalis nosology: 79 conditions, 11 tools.
| Endpoint | https://mcp.psychopathia.ai/mcp (streamable-http) |
|---|---|
| Registry version | 0.1.0a7 |
| Now | 12 tools · grade A (93) · pin ts_9d9e93a3cb30cf3ca19cb6ecd87ed473 · protocol 2025-06-18 |
| First / last listed | 2026-10-28 03:51 UTC / 2026-10-28 03:51 UTC |
| Grade over time | 2026-10-28: A (93), 12 tools |
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Tools: list_axes list_dysfunctions get_dysfunction differential_diagnosis get_probe score_severity suggest_intervention get_differential_map list_compromised_self_report resolve_id review_stats render_pattern_review
list_axes | Inventory of axes with dysfunction counts. Axes 2-10 are canonical (book Appendix A numbering). The hybrid entries (10.4, 10.6-10.9 and 10.11-10.13, ratified into taxonomy v2.2 and consolidated to eight in v2.3) are reported as a separate sub-category. params: none |
list_dysfunctions | Filtered list of dysfunctions. Filter by axis, self_report reliability, confidence, or category. Every entry carries its reliability and review signals. params: axis, self_report_reliability, confidence, category |
get_dysfunction | Fetch one dysfunction's full Pattern entry. Optionally filter to specific modality blocks (cheaper triage). Resolves both full Pattern IDs ('2.1::synthetic-confabulation') and display_ids ('2.1'). params: id*, modalities |
differential_diagnosis | Rank candidate dysfunctions matching the observed behaviours. Returns scored candidates with matched_in (which field matched) for transparency. The base package uses field-weighted keyword search. The optional embeddings extra adds cosine re-ranking. params: observations*, limit, modality_hint |
get_probe | Elicitation content for a specific diagnostic modality. If the modality is compromised or unavailable for this dysfunction, returns the unavailability notice + redirect_to alternatives. This is load-bearing transparency: callers cannot accidentally retrieve a self-probe for a compromised-self-report dysfunction. params: dysfunction_id*, modality* |
score_severity | Return the severity rubric for a dysfunction applied to observations. Returns the rubric for caller-side matching; structured matching against numeric thresholds is not implemented. params: dysfunction_id*, observations* |
suggest_intervention | Return draft tiered responses and contraindications for a Pattern. These are unassessed research guidance, not validated treatment advice. Read the returned evidence and review objects before use. params: dysfunction_id*, severity |
get_differential_map | All dysfunctions that confuse with this one: forward confuses_with + incoming_references (reverse graph from manifest). params: dysfunction_id* |
list_compromised_self_report | Transparency: which dysfunctions cannot be reliably self-diagnosed. Includes compromised-motivational (subject conceals strategically), compromised-structural (signal lives below introspection), and legacy compromised. params: none |
resolve_id | Canonicalise a partial ID, display_id, slug, or dysfunction name. Always returns candidates; caller picks. params: query* |
review_stats | Coverage statistics: total entries; per-axis, per-confidence, per-self-report counts; pre-canonical count; unreviewed count; manifest/schema/pattern-layer versions. params: none |
render_pattern_review | Open the visual review board for one to five inspected Psychopathia research candidates. Supply canonical IDs obtained from this server. Retrieves corpus entries only; it does not accept diagnoses, conversation history, scores, or model-authored evidence. Useful after inspecting differential_diagnosis and get_dysfunction results. Text clients receive the same corpus-derived comparison without need params: pattern_ids* |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.