The server provides 13 well-named, verb-prefixed tools with consistent parameter schemas and reasonable descriptions. However, significant gaps exist: tool descriptions lack actionable LLM guidance (e.g., when to prefer search_lei_records vs fuzzy_completions), output schemas are undocumented, error handling provides no recovery guidance, and parameter descriptions are minimal (averaging ~15 chars vs. recommended ~72 chars baseline). The server follows basic composition patterns but lacks the depth expected for production agent integration.

• Document output schemas for all tools. Specify the structure of paginated responses (e.g., {"data": [...], "pagination": {"page": int, "total": int}}) and the fields returned per resource type (LEI record, issuer, country, etc.).
• Expand parameter descriptions to 50 - 150 characters. For example: 'Filter field key (e.g., "entity.legalName", "entity.jurisdiction"). See list_fields() for complete catalogue.' This matches the 72-char production baseline.
• Add selectivity guidance to search tools. Example for search_lei_records: 'Exact or fuzzy filter match on indexed fields. Use this for precise filtering; use fuzzy_completions for approximate entity name matching; use auto_completions for field-value suggestions.' This helps LLMs pick the right tool.
• Document pagination behavior: 'Results paginated in chunks of up to size (default 25, max 250). Return total count so LLM can estimate remaining pages.' State any API-imposed limits.
• Add error handling guidance in tool descriptions. Example: 'Returns {"data": [...]} on success. On 4xx errors (invalid field, bad LEI), returns {"error": "message"}. On 5xx errors, retry with exponential backoff.'
• Annotate all tools with readOnlyHint=true (via FastMCP schema metadata or JSON Schema const 'kind' field) to signal safety and enable advanced agent planning patterns.
• Consider breaking search_lei_records into domain-specific variants if common use cases emerge (e.g., search_lei_by_entity_name, search_lei_by_jurisdiction) to reduce LLM reasoning overhead.