This is a specialized cosmology/science MCP server with 24 tools across data loading, analysis, MCMC inference, visualization, and arXiv integration. The server shows SIGNIFICANT QUALITY GAPS across naming clarity, parameter descriptions, schema completeness, and error handling. While tool names are mostly action-oriented (e.g., compute_power_spectrum, load_observational_data), many parameter descriptions are minimal or missing context. Input schemas are present but often lack proper typing (parameters use bare 'string' types with dataset_name references rather than enumerated acceptable values). Output schemas are largely undocumented. Several tools accept positional dataset names instead of typed references, increasing hallucination risk. Error handling is minimal, tools do not guide LLM recovery or categorize failures. The design conflates data-passing patterns (agents expected to share dataset names) with stateful session management, which is fragile for multi-turn agent workflows. This server lands in the 'C-to-D' grade range, acceptable for a specialized research tool but well below production baseline for general-purpose agent use.

• Add enum or discovery pattern for dataset_name parameters. Replace 'dataset_name: string' with either (a) an enum of valid dataset names (if static), or (b) a list_datasets() call dependency hint in the description, or (c) a union type allowing either string or a reference object. Validate at tool entry and return actionable errors if dataset not found.
• Expand parameter descriptions for cosmology constants. For each parameter (A_s, n_s, h, Omega_*, etc.), add typical ranges, default values if not provided, and guidance on when an LLM should override the default. Example: 'Hubble parameter h = H0/100 km/s/Mpc. Typical range: 0.6 - 0.74 (Planck 2018: 0.674±0.005). Leave as default unless fitting observational data.'
• Document return types and output schemas for all tools. Add a 'Returns:' section to each tool's docstring explaining the structure of stored datasets or returned objects. Example: 'Returns: dataset_name (str) pointing to a stored dict with keys: k_theory (ndarray), Pk_theory (ndarray), metadata (dict with params h, Omega_b, etc.)'
• Add error recovery guidance. When tools fail due to missing datasets, return: 'Dataset "k_values_xyz" not found. Available datasets: [list]. Use create_theory_k_grid() to create a k-grid, or list_datasets() to see all options.' This guides the LLM's next step without requiring a separate lookup call.
• Implement and document idempotency for expensive operations. For run_mcmc_cosmology and compute_power_spectrum, either: (a) make them idempotent by caching results keyed by (params_hash) and returning cached results if called again, or (b) document that repeated calls with identical params will execute again, and the LLM should check if a result already exists via list_datasets() before calling. Add idempotentHint annotation if (a) is implemented.