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Model Context Protocol (MCP) server for AlphaFold3 protein structure prediction. Provides tools for running structure predictions with different modes (default, a3m, msa), batch processing multiple variants, and preparing protein variant configurations.
AlphaFold3 MCP has 5 tools with reasonable naming and documented input schemas, but exhibits significant gaps in description depth, parameter validation, error handling, and output schema documentation. Tool names follow verb_noun patterns (af3_predict_structure, af3_prepare_variants), which is correct. However, descriptions are often generic ('Run AlphaFold3 structure prediction') without actionable guidance on WHEN to use each tool or WHAT they return. Critical parameters like 'mode' and 'device' lack enum constraints and detailed format specs. Output schemas are not visible in the source, and error handling is absent. The tools operate on a highly specialized domain (protein folding) where assumptions about required data formats and failure modes are never stated. STDIO transport caps the protocol readiness hard ceiling at 50.
Run batch AlphaFold3 predictions on multiple inputs
Run AlphaFold3 structure prediction (default, a3m, or msa mode)
Run full pipeline from sequences (protein, DNA, RNA, ligand). Supports protein sequences, DNA sequences, RNA sequences, and small molecules via SMILES or CCD IDs.
Combined preparation and prediction workflow for protein variants
Prepare input configs for multiple protein variants
Output schemas not documented. No visible tool response definitions. Agents cannot plan downstream tool calls or extract required IDs/fields.
Parameter enums missing for constrained inputs. 'mode' (default/a3m/msa) should be enum; 'device' lacks min/max bounds. LLMs will hallucinate invalid values.
Tool descriptions are generic and lack WHEN/WHY guidance. E.g., 'Run AlphaFold3 structure prediction' (62 chars) does not explain when to use af3_predict_structure vs. af3_predict_structure_from_seq, or what output is returned. Baselines: 80 - 200 chars with actionable WHEN context.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 41 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 41 | - | v1 |
No error handling or recovery guidance. Tools lack descriptions of what can go wrong (invalid JSON, GPU OOM, malformed FASTA, missing CCD ID) and what the LLM should do next (retry, ask user, decompose into smaller calls).
af3_prepare_and_predict_variants violates single-responsibility principle by combining prepare and predict. LLMs waste reasoning cycles deciding whether to call this combined tool or compose prepare + predict separately.
Complex array parameter (sequences in af3_predict_structure_from_seq) lacks schema for array items. What fields must each sequence object have? Type, value, format? LLMs cannot infer nested schemas.
Side effects (writes to output directories, GPU allocation) are not stated in tool descriptions. Agents need to know which tools are safe to retry and which have irreversible consequences.