Free-form string parameters without enum constraints. 'dataset_type' accepts 'kv, llm, or chat' as a description but no enum schema constraint. 'data_type' filter similarly unconstrained. LLMs cannot validate options and may hallucinate invalid values.
No documented output schemas. Tools like list_datasets, list_examples, get_dataset do not specify return structure, field types, pagination mechanism (offset/limit vs cursor), or total count format. LLMs cannot plan downstream tool calls without knowing what fields are available.
Parameters accepting JSON strings ('metadata', 'filter_', 'inputs', 'outputs') lack format specifications or examples. E.g., list_examples has 'metadata' described as 'Filter by metadata (JSON string)' with no indication of required structure. LLMs will guess at JSON structure and produce malformed input.
Recommendations
Add enum constraints to all finite-set parameters: dataset_type → enum[kv, llm, chat], data_type → enum[kv, llm, chat]. Update schema definitions to include 'enum' field for each constrained parameter.
Document output schemas for all tools. Use a structured format (e.g., docstring sections or separate schema definitions) to specify: (a) return type (object, array), (b) required fields and their types, (c) pagination mechanism (limit, offset, total; or cursor, next_cursor), (d) example response structure.
Replace free-form 'metadata' and 'filter_' parameters with explicit structured definitions. For metadata, document expected JSON structure (e.g., 'object with string keys and string/number values'). For filter_, provide grammar or examples (e.g., 'LangSmith query filter syntax, see docs link').
Add 'dry_run' parameter to delete_dataset and delete_examples to preview impact before irreversible deletion. Update descriptions to explain: 'Pass dry_run=true to preview what will be deleted without executing.'
Separate 'name_or_id' into two parameters: 'dataset_id' and 'dataset_name', each with clear description of which to use. If server logic requires only one, choose the more common identifier and document how the other is derived.
Specify pagination bounds in list_datasets and list_examples descriptions: 'limit: max 100, default 20' and 'offset: starts at 0'. Document whether response includes 'total_count' field.
Expand all parameter descriptions to include format/range constraints: 'Example ID: UUID v4 format (36 chars)' or 'Limit: integer 1 - 100, default 20'. Avoid bare names; add context for LLM understanding.
Destructive operations (delete_dataset, delete_examples) accept a 'confirm' boolean parameter to skip confirmation but no dry-run or preview capability. LLMs may bypass the confirmation and delete unintended resources. No recovery guidance if deletion is mistaken.
Parameters named 'name_or_id' (delete_dataset) are ambiguous. LLMs cannot determine whether to pass a dataset name or ID without additional guidance. Should provide separate name and id parameters with clear descriptions of when each applies, or mandate one canonical form.
Pagination parameters ('limit', 'offset') present in list_datasets and list_examples but no indication of maximum allowed values, default behavior, or total count in response. Without this, LLMs may request excessive result sets or fail to understand pagination bounds.
Parameter descriptions are inconsistent in depth. Some are single phrases ('Dataset ID', 'Example ID') offering minimal context. Others lack any actionable guidance on valid formats or ranges. A parameter 'dataset' is described as 'Dataset ID or Name' but no guidance on how the tool disambiguates when both are valid.
Error recovery guidance is missing from tool descriptions. Descriptions do not indicate what the agent should do if a dataset is not found, if credentials are invalid, or if a push_dataset fails partway through. Error handling logic exists in main.py but is not exposed to the LLM via tool documentation.
No idempotency documentation. Tools like create_dataset, create_example, push_dataset do not indicate whether repeated calls with identical inputs are safe (idempotent) or will cause side effects (duplicates, conflicts). Agents need this to retry safely on transient failures.
Tool descriptions do not indicate required dependencies or prerequisites. E.g., create_example requires a valid dataset but the description does not state this. Example shows no guidance that the agent should call list_datasets or get_dataset first to validate the dataset exists.
create_exampleexample_from_run
Add error recovery guidance to each tool description: 'If dataset not found, call list_datasets() to discover available datasets.' Include common failure modes (auth, not found, conflict) and recovery steps.
Document idempotency status for each write operation: 'create_dataset is idempotent, calling twice with the same name and type returns the existing dataset without error.' State whether the tool upserts or fails on duplicate.
Add dependency hints to tools that require prior setup: 'create_example requires a valid dataset_id. Call list_datasets() or create_dataset() first to obtain one.' This guides the LLM to multi-step workflows.
Implement tool annotations in MCP (if available): mark delete_dataset and delete_examples with destructiveHint=true, list_* tools with readOnlyHint=true. This enables clients to display warnings or require extra confirmation.
For 'fields' and 'output' parameters present in many tools, define what valid values are. E.g., 'fields: comma-separated list (id, name, description, created_at, ...) or * for all.' Without this, LLMs cannot construct valid calls.