Skip to main content
Glama
gca-global

Qobrix CRM MCP Server

by gca-global

qobrix_search_opportunities

Search real-estate opportunities using hard filters and weighted preference boosts. Rank results by relevance to filter pipelines or match supply to demand.

Instructions

Relevance-ranked lead/opportunity search (F1-optimized) — primary pipeline filter AND supply→demand matching. TWO-TIER: search = hard must-haves; boost[] = soft weighted preferences scored over max_scan candidates; limit = top-N. With boost: _relevance + _matched; pagination.mode='ranked'. Call qobrix_search_dsl_help({resource:'Opportunities'}) for DSL + buyer-criteria fields. Pipeline recipes: status == "open"; created >= THIS_WEEK and status == "new"; next_follow_up_date <= NOW and status == "open". Supply→demand (who wants this listing?): search='status in ["new","open"] and buy_rent == "buy"', boost=[{field:'area_of_interest',op:'contains',value:'Limassol',weight:3},{field:'bedrooms_from',op:'<=',value:3,weight:2},{field:'list_selling_price_to',op:'>=',value:400000,weight:2}], limit=15, max_scan=200. Works for projects too (native by-property does not).

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 full responsibility for behavioral disclosure, and it excels. It discloses the two-tier ranking behavior (hard `search` filters vs. soft `boost` preferences), the scoring mechanism over `max_scan` candidates, the inclusion of `_relevance` and `_matched` fields, and pagination mode 'ranked'. It also mentions edge cases like the effective scan cap for expand/media (noted in max_scan schema) and the distinction between fast and ranked paths. No contradictions with annotations (none exist).

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 long but information-dense, with a front-loaded primary purpose and then progressively detailed guidance. Each sentence adds value: the two-tier model, recipes, and example are all useful. No fluff. However, it could be slightly more concise by trimming repeated emphasis on boost behavior already in the schema, but overall structure is logical and scannable.

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 7 parameters and no output schema, the description is remarkably complete. It covers the primary use cases (pipeline filtering, supply→demand), provides recipes, gives a full example, mentions compatibility with projects, and references supplementary tools for deeper DSL and field info. The return values (_relevance, _matched, pagination mode) are disclosed, though a full output schema is absent. The description leaves little ambiguity about how to use this complex tool.

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 is 3. The description adds significant value beyond the schema by explaining the interplay between parameters: how `search` (must-haves) pairs with `boost` (nice-to-haves), how `limit` becomes top-N with boost vs. page size without, and how `max_scan` defines the candidate pool. The detailed supply→demand example demonstrates parameter usage with real field names and values, clarifying semantics beyond the schema's 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 the tool's primary purpose: 'lead/opportunity search' with 'Relevance-ranked' functionality, and identifies it as the 'primary pipeline filter AND supply→demand matching.' It distinguishes from siblings by noting 'Works for projects too (native by-property does not),' which is a direct differentiator. The verb 'search' + resource 'opportunities' is explicit and specific.

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 provides explicit usage guidance: it tells when to use this tool (primary pipeline filter, supply→demand matching) and when not (native by-property search doesn't work for projects). It offers concrete pipeline recipes and a full supply→demand example with search, boost, limit, and max_scan values. It also directs to qobrix_search_dsl_help for DSL and buyer-criteria fields, giving clear alternatives and supplementary resources.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/gca-global/qobrix-crm-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server