Loading ToolBench
The server defines 17 tools with explicit schemas and descriptions visible in the source. Naming follows verb_noun convention consistently (create_, open_, get_, insert_, delete_, etc.). Most tools have descriptions ranging 50-150 chars, which is acceptable. However, several critical gaps reduce quality: (1) Many parameter descriptions are minimal or missing context; (2) Output schemas are not documented, tools return strings (markdown/json formatted) rather than structured objects, forcing the LLM to parse unstructured text; (3) No pagination support on list-like resources despite potentially large result sets; (4) Error handling is basic and does not guide recovery; (5) Batch operation tools (insert_documents, upsert_documents, update_documents) lack per-item success/failure feedback; (6) Some parameters like 'filter' (in vector_query) lack format/syntax documentation.
Create a new Zvec collection and open it for use. This tool creates a new vector database collection at the specified path with the given schema definition. The collection is automatically opened and cached for subsequent operations. Use this when you need to initialize a new vector database.
Create an index on a collection field.
Delete documents from a collection by ID.
Destroy and delete a collection from disk.
Drop an index from a collection field.
Search using embeddings generated from text.
Insert documents with embeddings generated from text fields.
Output schemas not documented for any tool. Tools return markdown or JSON strings, forcing LLMs to parse unstructured text. This violates the pattern:response-shaper requirement.
Batch operation tools (insert_documents, upsert_documents, update_documents, delete_documents) lack per-item success/failure reporting. If one document fails, the LLM does not know which items succeeded vs failed.
No pagination support on resource endpoints (zvec://collections, zvec://collection/{collection_name}) or on fetch_documents. Large collections could overwhelm context window. Missing pattern:paginated-result.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 59 | 2026-07-28+ | v2 |
Fetch documents by ID from a collection.
Generate dense embedding from text using OpenAI.
Get detailed information about a collection.
Insert documents into a collection.
Perform multi-vector similarity search on a collection.
Open an existing Zvec collection from disk. This tool opens a previously created collection and caches it for subsequent operations. The collection must have been created with zvec_create_
Optimize a collection for better performance.
Update existing documents in a collection.
Upsert documents into a collection (insert or update).
Perform vector similarity search on a collection.
Destructive operations (destroy_collection, delete_documents) lack confirmation/dry-run support. No pattern:confirmation-request implementation.
Parameter 'filter' in vector_query has no format documentation. What syntax does it accept? Regex, SQL WHERE, JSON? LLM cannot construct valid filters.
Batch document operations (insert_documents, etc.) have underspecified documents array schemas. Nested object definitions lack property-level descriptions. LLM cannot determine required vs optional fields or expected data types.
Error handling is implicit and provides no recovery guidance. Functions catch exceptions and call handle_error(), but error messages are not documented. LLM does not know if a failure is retryable or what the next step should be.
Tool descriptions are very brief (many under 50 chars). Several lack WHEN to use the tool or dependencies on other tools (e.g., create_and_open_collection should note it caches the collection for later use).