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Cachly — AI Cognitive Brain

semantic_search

Find cached entries semantically similar to a natural-language query. Returns results with similarity scores from pgvector HNSW index.

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).
Behavior4/5

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

With no annotations, the description fully covers behavioral traits: declares read-only and no side effects, describes return format (array with key, value, similarity_score, namespace), explains empty results, and details embedding computation (server-side, pgvector, data stays in Germany). Missing some error handling details, but still thorough.

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

Conciseness5/5

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

Description is a single coherent paragraph with front-loaded purpose, then read-only, return format, requirements, examples, and sibling references. Every sentence adds value with no waste.

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?

Given 7 parameters and no output schema, the description covers return structure, requirements, examples, and behavior. Slightly lacking error/edge case details, but overall complete for a search tool with well-documented schema.

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 baseline is 3. The description adds context like default values and explanation of hybrid search, but the schema already covers parameters well. No significant additional meaning beyond schema.

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 states 'Find cached entries that are semantically similar to a natural-language query', which clearly identifies the verb and resource. It also distinguishes from siblings by referencing cache_get for exact lookup and smart_recall for brain lessons.

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?

The description explicitly says 'Use cache_get for exact key lookup; use smart_recall for brain lessons', providing clear alternatives. It also includes prerequisites (API key, tier) and examples, giving complete guidance on when to use the tool.

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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