Missing output schema documentation. No tools document what they return, preventing agents from planning downstream calls or extracting structured data.
Exposes raw API objects as parameters and returns. Tools accept 'index' (FAISS object), 'metadata' (raw list), and return numpy arrays, not agent-friendly types. Should wrap in JSON-serializable structures with clear field names.
Parameter 'index' in search_index lacks description. Agents cannot infer it requires a FAISS index object, this is an unsafe implicit contract.
search_index
HIGH
Recommendations
Document the output schema for every tool. For example, search_index should return: { results: [ { chunk: string, doc_name: string, chunk_id: string, score: number } ], total_found: integer, top_k_used: integer }. This lets agents understand what data is available.
Replace raw FAISS/numpy objects with JSON-safe structures. Instead of accepting 'index' (FAISS object), have load_index and load_existing_index return a dictionary with { index_path: string, dimension: integer, num_vectors: integer } that agents can store and pass to search_index via paths, not raw objects.
Consolidate load_index + load_metadata into a single load_index_with_metadata tool, or clearly document when to use each. Add a use_case parameter like 'load' vs 'validate' if they serve different purposes.
Add min/max bounds to numeric parameters. Chunk_text: size 1 - 500, overlap 0 - 100. Search_index: top_k 1 - 100. Include these in both the schema and description.
Expand descriptions to 100 - 200 characters. E.g., 'Searches a FAISS vector index for documents semantically similar to a query. Returns the top_k most relevant chunks with similarity scores. Use this after loading an index to find relevant context for answering user questions.' This guides LLM selection.
Add error handling to every tool description. E.g., process_document: 'May fail if file does not exist (try with a valid path), embedding service is unavailable (check Ollama is running on localhost:11434), or file is too large (split into smaller documents).' This lets agents recover.
Parameter 'metadata' in search_index has minimal description ('The metadata associated with the index') and no type clarity. Should explain it expects a list of dicts with 'chunk', 'doc_name', 'chunk_id' keys.
No error handling guidance. Tools will fail silently (e.g. file not found, embedding service down, invalid FAISS index path) with no recovery hints for the agent.
Descriptions are too short or generic. 'Gets the embedding for a given text using the Ollama API' (42 chars) and 'Loads the FAISS index from the specified path' (45 chars) lack context about when/why to call them. Minimum is 10 chars but targets 50 - 200 chars for LLM optimization.
No schema constraints on numeric parameters. 'top_k' defaults to 5 but has no min/max bounds, agent could pass 10000 and exhaust memory or context. Should specify 1 - 100 range.
No idempotency guidance. Tools like process_document modify external state (appends to FAISS index, writes metadata JSON). If an agent retries after a partial failure, it will double-add chunks. No mention of transaction semantics or duplicate prevention.
Overlapping tool concerns. Both load_index+load_metadata and load_existing_index do the same thing. Agents will waste reasoning cycles deciding between them. Should consolidate or clearly distinguish by use case.
No pagination or result limits. search_index returns top_k results but makes no guarantee about output size, memory footprint, or token count. If an agent sets top_k=1000, the response could exhaust context window.
Parameter descriptions missing for critical inputs. 'file_path' in process_document has a description, but 'size' and 'overlap' in chunk_text are underdescribed, agents cannot infer optimal values or constraints.
chunk_text
Document idempotency. process_document should note: 'Appends chunks to the FAISS index. Calling twice with the same file will duplicate chunks. Use load_existing_index to verify current state before calling.' This prevents duplicate insertions on retry.
Add natural-language parameter aliases. Instead of requiring 'top_k', accept 'limit' or 'num_results' as synonyms. Document in the description that these are equivalent. Agents often reason in natural terms.
Return all necessary downstream references. search_index currently returns chunks with doc_name, chunk_id, and chunk text. If agents will call process_document next to re-index, also return the original file_path so they don't need to lookup.
Implement result capping. search_index should cap top_k at 50 and document this in the description: 'top_k is capped at 50 to avoid exhausting context. Larger limits are automatically reduced.' This prevents runaway calls.
Add a batch variant. If agents often process multiple documents, offer process_documents (plural) accepting a file_path array and returning [ { file_path, chunks_added, metadata_ids } ]. This reduces token waste and latency.
Validate file paths against directory restrictions. process_document should reject paths outside an allowed directory (e.g., only ~/documents/rag_docs) to prevent path traversal attacks.
Add a dry-run mode to process_document. E.g., 'dry_run=true' returns chunks and embeddings without writing to disk. This lets agents preview changes before committing.