Three well-named, read-only tools with clear descriptions and complete input schemas. Tool names follow verb_noun pattern (list_, dataflow_, get_). Descriptions are detailed (150 - 250 chars) and explain prerequisites and usage order. All parameters have types and descriptions. However, output schemas are not documented, LLMs cannot predict response structure for planning. Error handling is absent; no guidance on recovery or invalid inputs. No parameter validation rules stated (e.g., dataflowId format, dataKey syntax). Missing enum constraints for structured inputs.

• Document output schemas for all three tools. Example for list_dataflows: {total: number, matched: number, returned: number, dataflows: [{id, agencyID, version, name}]}. This enables LLMs to extract dataflow IDs for chaining.
• Add error handling guidance. E.g., 'If dataflowId is invalid, returns error with available IDs. Try list_dataflows() to discover valid IDs.' Pair with actionable error messages in implementation.
• Formalize dataKey syntax as a regex pattern in the schema or add an enum example. Current prose description ('dot-separated', 'codes', 'wildcards') is ambiguous. Consider: pattern: '^([A-Z0-9]+|[A-Z0-9+]+|)(\.([A-Z0-9]+|[A-Z0-9+]+|))*$|^all$'
• Add explicit min/max constraints to parameter descriptions. E.g., 'limit: 1 - 500 (default 50)', 'maxCodesPerDimension: 1 - 10000 (default 50)'. Update schema with minimum/maximum fields.
• Document response pagination. If list_dataflows returns 50 results but 200 exist, add a 'hasMore' flag or 'nextOffset' field. Describe how to fetch subsequent pages.