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LangChain Chroma Vector DB Server with Ollama Embeddings and Multi-Format Document Ingestion. Provides custom implementations for Ollama and OpenAI embeddings using direct API calls, and supports multiple document formats.
This RAG server has 5 tools with basic definitions but significant quality gaps. Tool names are verb-forward and clear (add_documents, query_documents, list_documents, delete_document, clear_database), which is good. However, parameter descriptions are sparse or missing entirely, several tools lack required/optional clarity, and output schemas are not documented. The `add_documents` tool has no required parameters despite accepting files or text, this is dangerous and ambiguous. The `delete_document` and `clear_database` tools perform irreversible operations but lack confirmation/dry-run patterns and error guidance. Most descriptions are generic and under 100 characters, missing context on WHEN to use each tool and what happens on failure. No per-parameter descriptions for most inputs. This is typical of community servers that focus on functional correctness but omit agent-facing polish.
Adds documents to the vector database. Accepts file paths or raw text content.
Clears all documents from the vector database.
Deletes a document from the vector database by its ID.
Lists all documents currently in the vector database.
Searches the vector database for documents similar to the query using semantic search.
Destructive operations (delete_document, clear_database) lack confirmation/dry-run patterns and permission gates. An LLM can irreversibly wipe a database in one call with no undo. Violates pattern:confirmation-request and pattern:permission-gate.
Output schemas completely missing for all 5 tools. LLMs cannot determine what fields to expect in responses, forcing them to guess at downstream tool chains. Violates pattern:tool and blocks chaining.
add_documents has no required parameters (required=[]). Both 'files' and 'text_content' are optional, LLMs will call without either, or call with both confusingly. Ambiguous schema invites misuse.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 46 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 0 | - | v1 |
No pagination/limit parameters on list_documents. With no cap, this tool can return thousands of documents, exhausting context windows. Violates pattern:paginated-result.
Parameter descriptions are minimal or absent. num_results is described as 'Number of results to return (default: 5)' but lacks range constraints (e.g., 1-100). Most params lack context on format, constraints, or valid values.
Tool descriptions are generic and under 100 chars. They state WHAT tools do but not WHEN to use them or what happens on error. e.g., delete_document: 'Deletes a document... by its ID' gives no guidance if ID is not found.
No error handling or recovery guidance documented. Tools do not specify retryable vs fatal errors, invalid values, or how to correct failed operations. Violates pattern:recovery-guide.
No field-naming consistency between tools. If query_documents returns a 'document_id' field, delete_document must accept 'document_id' parameter, not 'id' or 'doc_id'. Mismatched naming forces LLM field-mapping reasoning.