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Cachly — AI Cognitive Brain

semantic_search

Retrieve cached entries semantically similar to a natural-language query. Returns keys, values, and similarity scores for efficient content discovery.

Instructions

Find cached entries that are semantically similar to a natural-language query. Read-only — no side effects. Returns an array of objects, each with: key, value, similarity_score (0–1), and namespace. Returns an empty array if no entries meet the similarity threshold. Requires OPENAI_API_KEY (or compatible provider) and the Speed/Business tier with CACHLY_VECTOR_URL. Embeddings are computed server-side and never leave Germany (pgvector HNSW index). Example: "find all cached responses about password reset" or "what did we answer about pricing?". Use cache_get for exact key lookup; use smart_recall for brain lessons.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesUUID of the cache instance (from list_instances)
queryYesNatural-language query to find similar cached content
thresholdNoMinimum cosine similarity 0–1 (default: 0.82). Lower = broader matches.
namespaceNoSemantic namespace to search in (default: cachly:sem)
top_kNoMaximum number of results to return (default: 5)
use_hybridNoEnable Hybrid BM25+Vector RRF fusion search. Passes `hybrid: true` and the query text to the pgvector API for higher precision on named entities. Default: false.
auto_namespaceNoAuto-detect the namespace from the query text using text heuristics instead of using the `namespace` parameter. Returns results only from the matching domain (code/translation/summary/qa/creative).
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses read-only nature, return format (key, value, similarity_score, namespace), empty array behavior, prerequisites (API key, tier), and data residency (Europe). No annotations are present, so the description fully covers behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single focused paragraph that includes all key information without redundancy. It is well-structured but slightly verbose due to listing multiple details; still, it earns its sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, behavior, return values, empty results, prerequisites, and alternatives. However, it omits potential error cases (e.g., missing API key) and could elaborate on hybrid search, but given the absence of output schema, it is quite comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters well. The description adds no new parameter-level meaning beyond what is in the schema, so a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Find cached entries that are semantically similar to a natural-language query,' which is a specific verb and resource. It also explicitly distinguishes from siblings like cache_get and smart_recall.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance with natural-language examples and directly names alternative tools: 'Use cache_get for exact key lookup; use smart_recall for brain lessons.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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