FeatureStoreLite has 3 tools with basic action verbs (store_, get_, list_) but suffers from significant quality gaps. Tool descriptions are present but generic and boilerplate-heavy ('Tool: ...' prefix followed by minimal explanation). Parameters are typed but lack rich descriptions, the 'vector' parameter in store_feature only says 'A valid JSON array string' without explaining the domain semantics (dimensions, normalization, expected range). The 'metadata' parameter has no description at all in the schema. No output schemas are documented, forcing LLMs to guess response structure. Error handling is minimal (raw exception strings). The store_feature tool accepts a JSON string parameter instead of structured input, which is unusual for vector storage. No per-tool risk annotations (readOnlyHint, destructiveHint, idempotentHint) are present. Overall, this reads as a minimal working implementation rather than a production-grade tool suite.

• Rewrite tool descriptions to be LLM-optimized: state WHAT the tool does, WHEN to use it over similar tools, and any prerequisites. E.g., 'store_feature' → 'Persist a feature vector and optional metadata. Use this to cache embeddings or ML features for retrieval. Vectors are stored as JSON arrays; metadata is optional and useful for labeling entity type or segment. This operation is idempotent, re-storing the same key overwrites the prior vector.'
• Add rich descriptions to all parameters. For 'vector' in store_feature: 'A valid JSON array of numbers representing the embedding (e.g., [0.1, 0.2, -0.5]). Typically 8-1024 dimensions depending on embedding model.' For 'metadata': 'Optional JSON object to annotate the feature, e.g., {"type": "user", "segment": "premium"}. Useful for filtering and understanding stored vectors later.'
• Convert 'vector' parameter from string to structured input: accept an array[number] type instead of requiring JSON serialization. This eliminates client-side JSON encoding and makes the schema self-documenting.
• Document output schemas explicitly. For store_feature: return {status: string, key: string}. For get_feature: return {key: string, vector: array[number], metadata: object | null, created_at: string}. For list_features: return {keys: array[string], total: integer, limit: integer}.
• Add pagination to list_features: accept 'limit' (default 20, max 100) and 'offset' (default 0) parameters. Return {keys: [...], total: <count>, offset: <offset>, limit: <limit>} so agents can iterate.