The server defines a single tool 'generate_msa' with a detailed implementation and comprehensive parameter schema. The tool name is action-oriented (verb_noun pattern), descriptions are substantive, and the input schema is well-structured with type information and defaults. However, there are significant gaps in error handling guidance, output schema documentation is implicit rather than explicit, and the tool lacks composition patterns. The implementation shows good parameter design with mutually exclusive inputs (sequence vs fasta_file) and sensible defaults (gpu=true, threads=64, sensitivity=7.5), but lacks actionable error recovery guidance and clear output structure documentation that an LLM would need to chain subsequent operations.

• Add explicit output schema documentation: 'Returns (dict): When return_format="a3m", returns {"msa_content": str, "num_sequences": int, "alignment_length": int}. When return_format="path", returns {"file_path": str, "file_size_bytes": int}'.
• Enhance error messages with recovery paths. Example: catch subprocess.CalledProcessError and return 'MMseqs2 search failed: {stderr}. Verify database_path points to a valid MMseqs2 database. Check GPU availability with nvidia-smi if --gpu 1 was requested.'
• Add tool annotations in fastmcp registration: mark as destructiveHint=true (creates/modifies files), idempotentHint=false (GPU acceleration introduces non-determinism), readOnlyHint=false.
• Implement proper temporary directory cleanup using try/finally: 'finally: if use_temp_dir and os.path.exists(temp_dir): shutil.rmtree(temp_dir)'.
• Document 'max_seqs' parameter constraint more explicitly: 'Maximum number of sequences to return (default: 100000). WARNING: Large values may produce MSA strings exceeding 1MB, consuming significant token budget. For most use cases, 5000-10000 is sufficient.'
• Add a 'dry_run' parameter to validate inputs without running the full pipeline: 'dry_run (bool): If True, validate inputs and print the MMseqs2 command that would be executed without running it. Useful for testing parameter combinations.'
• Return structured metadata alongside MSA content: when return_format='a3m', also return sequence count, alignment length, and execution time so the agent can assess result quality.