Alphafold Sovereign MCP vs Aria MCP Server

get_disease_targets

Return top protein targets for a disease with Open Targets evidence scores. Evidence score breakdown (0–1 per data type): - ``genetic_association``: GWAS + rare-variant signals - ``somatic_mutation``: Cancer somatic variant evidence - ``known_drug``: Approved or clinical-stage drugs - ``affected_pathway``: Pathway membership (Reactome, SIGNOR) - ``literature``: Text-mining evidence (Europe PMC) - ``animal_model``: Knockout / model organism phenotypes - ``rna_expression``: Differential expression evidence Example: ``get_disease_targets(disease_id='MONDO:0007254', limit=15)`` returns top 15 targets for breast carcinoma.

get_target_diseases

Return all diseases associated with a protein target via Open Targets. Accepts a UniProt accession and returns the full disease landscape for that target — essential for target-validation and indication-expansion. Example: ``get_target_diseases(uniprot_id='P04637')`` returns all diseases associated with TP53 / p53.

get_common_disease_targets

Profile the top drug targets for a curated set of common diseases in one call. Use this for a fast landscape scan across a whole disease area: given a ``category`` (e.g. 'oncology'), it looks up the curated MONDO diseases in that category and returns each one's top Open Targets evidence-scored targets, in parallel. To profile a single disease you already have a MONDO ID for, use ``get_disease_targets`` instead — this tool is its category-level, multi-disease counterpart and does not accept a raw MONDO ID. Queries Open Targets live. Returns a JSON string with the ``category``, the number of diseases profiled, and a ``profile`` object mapping each disease to its MONDO ID and top targets (per-disease errors are reported inline, not raised). Returns a JSON error object listing the valid values when the category — or a ``disease_name`` filter within it — is not recognised.

triage_variant_3d

Comprehensive clinical triage for a missense variant. Fuses the upstream signals this tool currently wires into a single prioritised report: 1. **Pathogenicity** — ClinVar interpretation + review status. The ``alphamissense_score`` / ``alphamissense_interpretation`` fields are always ``null`` / "Not available" here: AlphaMissense is not wired into this tool. For an AlphaMissense pathogenicity score use ``generate_variant_clinical_report``. 2. **Population genetics** — gnomAD LOEUF / pLI gene-constraint scores. Per-variant allele frequencies and the per-ancestry breakdown are not wired into this tool. 3. **Disease associations** — a placeholder note pointing at ``get_target_diseases()``; the Open Targets / MONDO traversal is a roadmap (Wave-3) item. 4. **Structural context** — a text note pointing at ``analyze_structural_confidence`` (resolve the gene to a UniProt accession first); the AlphaFold pLDDT / PAE join into this report is a roadmap (Wave-3) item. Returns a ``pathogenicity_tier``: HIGH / MEDIUM / LOW / UNKNOWN (derived from ClinVar; the AlphaMissense input is always absent here). Example: ``triage_variant_3d(hgvs='BRCA1:c.181T>G')``

phenotype_to_structures

Map a clinical phenotype to the protein structures of its disease targets. Pipeline: 1. Resolve HPO term → associated diseases 2. For each disease → top protein targets (Open Targets) 3. For each target → UniProt ID (for AlphaFold retrieval) Use the returned UniProt IDs with ``analyze_structural_confidence`` to retrieve AlphaFold structural confidence (pLDDT/PAE). Example: ``phenotype_to_structures(hpo_id='HP:0002621')`` maps Atherosclerosis → disease targets → UniProt IDs.

get_orphan_disease_atlas

Map an Orphanet rare disease to its MONDO record, HPO phenotypes, and protein targets. Rare / orphan diseases are often under-studied because their small patient populations make large trials impractical. This tool aggregates the available structural and clinical intelligence into one report to accelerate research. Returns: - MONDO record with ICD-10 coding - HPO phenotype profile of the disease - Open Targets protein target evidence scores - UniProt IDs for AlphaFold structural retrieval Example: ``get_orphan_disease_atlas(orphanet_id='79318')`` returns the Gaucher disease atlas.

compare_disease_target_overlap

Compare the protein target landscapes of two diseases. Identifies shared and unique targets between two diseases — a key analysis for drug repurposing, identifying shared mechanisms, and understanding comorbidity. Returns: - Shared targets (present in both disease target sets) - Unique to Disease A / Disease B - Jaccard similarity score of target sets Example: ``compare_disease_target_overlap( mondo_id_a='MONDO:0004975', # Alzheimer disease mondo_id_b='MONDO:0005180', # Parkinson disease )``

resolve_icd10_to_mondo

Resolve an ICD-10 clinical code to MONDO disease ontology terms. Enables integration between clinical / EHR data (which uses ICD-10) and the research-grade MONDO ontology used by Open Targets, HPO, and this MCP. Example: ``resolve_icd10_to_mondo(icd10_code='I21.0')`` maps ST-elevation MI (ICD-10) to MONDO coronary disease terms.