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Semantic Search Contacts

neuron_semantic_search_contacts
Read-only

Search contacts by semantic meaning using natural language queries. Returns ranked results with relevance scores and AI-generated match reasons.

Instructions

Search contacts using natural language AI-powered semantic search. Finds contacts based on the meaning of their notes — skills, services, schedules, preferences, etc. Returns ranked results with relevance scores and AI-generated match reasons.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return (default 10, max 20)
queryYesNatural language search query (e.g. 'who can deliver to Lagos on Monday?')
Behavior4/5

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

Annotations already indicate readOnlyHint=true. The description adds that it returns ranked results with relevance scores and match reasons, but does not mention any limitations (e.g., only searches notes). No contradictions.

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?

Two sentences, concise and front-loaded. The first sentence states the core action, the second adds detail on what is searched and returned. No unnecessary words.

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 no output schema and only 2 params, the description adequately covers input (natural language query) and output (ranked results with scores). It could mention if it searches only notes or other fields, but the emphasis on notes is sufficient.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds valuable context: query is natural language with an example, limit has max 20, and it specifies the kinds of queries (skills, services, etc.). This goes beyond the schema's parameter descriptions.

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 it performs semantic search on contacts using natural language, distinct from basic search by emphasizing 'AI-powered' and 'meaning of their notes'. It specifies the resource (contacts) and the type of search (semantic). The mention of returns (ranked results, relevance scores, match reasons) adds clarity.

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

Usage Guidelines4/5

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

The description implies use for finding contacts by conceptual meaning rather than exact matches. It does not explicitly compare to the sibling tool 'neuron_search_contacts' but the semantic focus distinguishes it. However, it lacks explicit 'when to use' or 'when not to use' guidance.

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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