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gca-global

Qobrix CRM MCP Server

by gca-global

qobrix_search_contracts

Find contracts by mandatory criteria, then rank them with optional weighted preferences (boost[]), returning top-N relevance-scored matches.

Instructions

Relevance-ranked contract search (F1-optimized). TWO-TIER: search = hard must-haves; boost[] = soft weighted preferences; limit/max_scan for top-N and pool. With boost: _relevance + _matched; pagination.mode='ranked'. Call qobrix_search_dsl_help({resource:'Contracts'}) for fields. Example: search='contract_type == "cos" and contract_status == "agreed"', boost=[{field:'final_selling_price_amount',op:'>=',value:300000,weight:2}], limit=10, max_scan=150. Prefer qobrix_deals for common closed-deal questions.

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.
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the two-tier ranking behavior, the `_relevance`/`_matched` output fields, `pagination.mode='ranked'`, and that boost never filters out rows (implied by 'soft weighted preferences'). It also mentions 'F1-optimized' as a recall/precision trait. It doesn't state auth or side-effect status, but as a search tool this is less critical.

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?

Six sentences, each packed with distinct information: purpose, two-tier model, output markers, field help pointer, example, and alternative tool. No fluff, though it is dense.

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 params, no output schema, and no annotations, the description is remarkably complete: it covers the two-tier model, gives an example, directs users to qobrix_search_dsl_help for fields, and names the preferred alternative for closed deals. The main gap is the lack of a full response format, but the field-help pointer mitigates that.

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%, so baseline 3. The description adds significant semantic value by explaining how `search` and `boost` interact, providing a concrete example with all key params, and clarifying that `limit` means top-N with boost and page size without.

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?

Describes itself as 'Relevance-ranked contract search (F1-optimized)' with a specific resource (contracts) and a distinct two-tier search mechanism. Also explicitly steers users to qobrix_deals for common closed-deal questions, distinguishing it from sibling tools.

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?

Explicitly says 'Prefer qobrix_deals for common closed-deal questions,' providing an alternative. It also gives precise guidance on when to use `search` (hard must-haves) vs `boost` (soft preferences), and how `limit`/`max_scan` control top-N and pool.

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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