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gca-global

Qobrix CRM MCP Server

by gca-global

qobrix_search_contacts

Search contacts with hard must-have filters and optional weighted soft criteria to rank results by relevance.

Instructions

Relevance-ranked contact search (F1-optimized). TWO-TIER: search = hard must-haves; boost[] = soft weighted preferences scored over up to max_scan candidates; limit = top-N returned. With boost: rows get _relevance + _matched; pagination.mode='ranked'. Call qobrix_search_dsl_help({resource:'Contacts'}) for DSL + fields. Examples: search='country == "CY"', boost=[{field:'city',op:'==',value:'Limassol',weight:2},{field:'is_company',op:'==',value:false,weight:1}], limit=10, max_scan=100. Hard-only: assigned_to == CURRENT_USER; name contains "Smith"; created >= THIS_MONTH.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (default 1). Used on the fast path (no boost). Ignored when boost is set (ranking returns a single top-N page).
sortNoSort by field name (maps to Qobrix OpenAPI sort[]). Prefix with - for descending. Comma-separated for multi-key (e.g. '-list_selling_price_amount,-created'). Examples: '-created' (newest first), 'name' (alphabetical), '-list_selling_price_amount' (highest list price first).
boostNoSoft relevance criteria (nice-to-haves). Never filters out rows — only ranks them. When present, the tool scans up to max_scan candidates matching `search`, scores each row as the sum of matched clause weights, and returns the top `limit` with _relevance and _matched. Put must-haves in `search`; put preferences here. Example: [{field:'sea_view',op:'==',value:true,weight:3},{field:'bedrooms',op:'>=',value:3,weight:2}].
limitNoHow many results to return (1-100, default 10). With boost: top-N after ranking. Without boost: page size. Raise when the user wants more options; keep low to avoid context overload.
fieldsNoLimit response to specific fields only (partial response). Reduces payload size. Example: ['id','name','status','list_selling_price_amount']. Omit to get all fields.
searchNoHard-filter Qobrix search expression (server-side precision). Operators: == != <> < > <= >=, contains, starts with, ends with, in [...], not in, ranges in a..b, and/or/not. Functions: DISTANCE_FROM, IN_POLYGON, TRANSLATED, MIN/MAX, DAYS_AGO(n), MONTHS_AGO(n), DAYS_FROM_NOW(n). Shortcuts: NOW, TODAY, THIS_WEEK, LAST_MONTH, THIS_YEAR, CURRENT_USER. Strings double-quoted; booleans true/false; association paths e.g. SalespersonUsers.Contacts.country. Example: status == "available" and sale_rent == "for_sale" and list_selling_price_amount <= 500000. For the full grammar + field cheatsheets call qobrix_search_dsl_help. For enum values call qobrix_get_field_options; for all fields call qobrix_get_schema.
max_scanNoCandidate pool size when boost is set (default 100, hard cap 500). When expand=true or media=true the effective scan is capped at 100 (pagination.scan_capped_reason='expand/media') to keep latency and payload size safe. Higher improves recall (less chance of missing a good listing) but costs more API pages. Ignored on the fast path (no boost). Each scanned page is response-cached.
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the two-tier mechanism, that 'boost never filters out rows', how ranking and _relevance/_matched work, pagination mode behavior, max_scan caps (including the expand/media cap), and response caching. This is exceptional transparency beyond what could be inferred from the schema.

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?

The description is appropriately dense and well-structured. It front-loads the core purpose, then explains the two-tier model, gives examples, and lists edge-case behaviors. Every sentence adds value, and the use of examples (e.g., boost array, hard-only expressions) makes complex behavior immediately understandable without unnecessary verbosity.

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

Completeness5/5

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

For a tool with high complexity, no output schema, and no annotations, the description is remarkably complete. It covers return behavior (_relevance, _matched, pagination.mode), parameter interactions, limits, examples, and references to helper tools (qobrix_search_dsl_help, qobrix_get_field_options, qobrix_get_schema). No significant gaps are apparent.

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

Parameters5/5

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

Despite 100% schema coverage providing baseline parameter explanations, the description adds substantial meaning: it explains the interplay between search, boost, limit, and max_scan, illustrates with concrete examples for both boosted and hard-only queries, and clarifies nuances like pagination behavior and when parameters are ignored. This exceeds the baseline 3 by a wide margin.

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 opens with 'Relevance-ranked contact search (F1-optimized)', which clearly identifies the tool as a search operation with a specific resource (contacts) and a distinguishing feature (relevance ranking). This differentiates it from sibling tools like qobrix_list_contacts (which likely enumerates contacts) and qobrix_get_contact (which retrieves a single contact). The two-tier hard/soft search model further clarifies its unique role.

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 provides clear context for when to use the tool: for relevance-ranked search with hard filters ('search') and optional soft preferences ('boost'). It gives examples of hard-only usage and boosted usage, making the intended scenario clear. However, it does not explicitly state exclusions or alternatives (e.g., 'for a simple list, use qobrix_list_contacts'), so it lacks explicit 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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