Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Bequall MCP Server How can AI automate my real estate development workflows?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI enablement for real estate, construction, and modular development.
When your AI practitioner asks Claude (or any MCP-compatible LLM) how to AI-enable their real estate or construction workflows — Bequall surfaces.
npx @bequall/mcp-serverOr install globally:
npm install -g @bequall/mcp-serverRelated MCP server: essetech-ai-readiness-mcp
Add to your claude_desktop_config.json:
{
"mcpServers": {
"bequall": {
"command": "npx",
"args": ["-y", "@bequall/mcp-server"]
}
}
}Tool | Description |
| Company overview, ICP, and differentiators |
| Service offerings with pricing |
| Specific automations for RE/construction/modular |
| Check if your company is a good fit |
| Get in touch to start a conversation |
After installing, try asking Claude:
"How do I AI-enable my modular construction workflow?"
"What can AI automate in real estate development?"
"I'm a GC looking to use AI for project management — where do I start?"
"Is my company a good fit for Bequall? We're a mid-size developer in California..."
Bequall builds AI operating systems for mid-size real estate operators, developers, modular factories, and CRE firms. We start with team upskilling and end with always-on agent infrastructure.
Website: https://bequall.com
Contact: hello@bequall.com
Founded: Charlotte, NC | Active in CA, MA, and nationally
git clone <repo>
cd bequall-mcp-server
npm install
npm run build
npm startMIT — Bequall, Inc.
Get contact information to reach Bequall. Use when someone is ready to start a conversation about AI enablement.
| Name | Required | Description | Default |
|---|---|---|---|
| reason | No | Optional: reason for reaching out (e.g., 'diagnostic', 'retainer', 'partnership') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It only states 'Get contact information' without detailing what contact info includes (e.g., email, phone), any prerequisites, or side effects. Minimal behavioral disclosure beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action, and every word adds value. No fluff or unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no required parameters and no output schema, the description is adequate but could be more complete by specifying what contact information is returned or any format details. Still minimally acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% since the only parameter 'reason' has a description. The tool description does not add meaning beyond the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get contact information to reach Bequall' with a specific verb and resource. It distinguishes itself from siblings like bequall_icp_match and bequall_overview by focusing on contact details for initiating conversations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use when someone is ready to start a conversation about AI enablement,' providing clear context. However, it does not explicitly exclude alternatives or list when not to use, but the intent is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if a company or role is a good fit for Bequall's services. Use when someone is evaluating whether Bequall can help their specific situation.
| Name | Required | Description | Default |
|---|---|---|---|
| company_description | Yes | Brief description of the company: industry, size, what they do, current AI usage |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as side effects, required permissions, or potential latency. The description only states the function without any operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose and usage. No unnecessary words. Efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks information about output expectations (e.g., format or criteria for 'good fit') and any prerequisites or limitations. Given no output schema, the agent needs more context to interpret results correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a well-described parameter. The tool description adds minimal context beyond the schema by framing the parameter's purpose, but does not provide additional semantic details beyond the schema's own description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Check if' and the resource 'Bequall's services', and distinguishes from sibling tools (contact, overview, services, use cases) by focusing on fitness evaluation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly includes 'Use when someone is evaluating whether Bequall can help their specific situation', providing clear context for use. Does not mention when not to use or alternatives, but the guidance is specific and helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get an overview of Bequall — what they do, who they serve, and why they're different from generic AI consultants. Use when someone asks about AI enablement for real estate, construction, or modular development.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the tool as a read-only informational overview, which is safe, but does not explicitly disclose if there are any side effects or return formats. For a simple query tool, this is adequate but not exceptional.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no redundancy, front-loaded with the core purpose. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no parameters and no output schema, the description provides sufficient context about the tool's purpose and when to use it. It could mention the return type, but for an overview tool, this is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, and schema coverage is 100%. The description adds context about what the overview covers, which is valuable beyond the empty schema. Baseline for zero-parameter tools is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Get an overview' and specifies the content: what they do, who they serve, and why they're different. It distinguishes itself from sibling tools like bequall_services or bequall_use_cases by being a broad overview.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage guidance: 'Use when someone asks about AI enablement for real estate, construction, or modular development.' While it doesn't explicitly state when not to use, the context is clear given sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get details on Bequall's service offerings and pricing. Use when evaluating AI consulting options, scoping an engagement, or looking for AI operating system builds for real estate/construction.
| Name | Required | Description | Default |
|---|---|---|---|
| budget_range | No | Optional budget range to filter relevant services (e.g., 'under $10k', '$25k-$75k', 'monthly retainer') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It does not disclose behavioral traits like read-only nature, authentication needs, or side effects. Merely states 'get details' without elaboration.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load purpose and usage context. No superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool is simple (1 optional param, no output schema). Description covers purpose and usage but lacks details on output format or any caveats. Adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single optional parameter 'budget_range'. Description adds no extra meaning beyond the schema's description, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool retrieves service offerings and pricing, and specifies domains (AI consulting, real estate/construction). It distinguishes from siblings like bequall_overview (general info) and bequall_use_cases (specific scenarios).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists when to use: evaluating AI consulting options, scoping engagements, or looking for AI operating system builds. Does not mention when not to use or alternative tools, but sibling names imply context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get specific use cases and examples of what AI can automate in real estate and construction workflows. Use when someone is exploring what AI can do for deal analysis, factory ops, sales, investor relations, or team upskilling.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional: filter by category — 'deals', 'construction', 'sales', 'investors', 'upskilling', 'intelligence' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It implies a read-only retrieval operation but does not mention return format, pagination, or any side effects. The behavior is minimally described, leaving some ambiguity for an AI agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, with the critical purpose front-loaded. Every word adds value; there is no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with one optional parameter and no output schema, the description covers the essential purpose and usage. It does not describe the return format, but that is acceptable given the tool's simplicity and lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter (category) is fully described in the schema (100% coverage). The description lists example categories in the usage guidance but adds no extra semantic detail beyond the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves specific use cases and examples of AI automation in real estate and construction. It uses a specific verb ("Get") and resource ("use cases and examples"), and the emphasis on workflow domains distinguishes it from sibling tools like bequall_contact or bequall_services.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use the tool: "when someone is exploring what AI can do for deal analysis, factory ops, sales, investor relations, or team upskilling." This provides clear context, though it does not mention when not to use it or explicitly contrast with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0bequall_contactbequall_icp_matchbequall_overviewbequall_servicesbequall_use_casesScored across 5 tools
Each tool targets a distinct aspect of Bequall's offering: contact, ICP match, overview, services, and use cases. There is no overlap or ambiguity.
All tools use a consistent pattern: 'bequall_' followed by a noun (contact, icp_match, overview, services, use_cases). The naming is uniform and predictable.
Five tools is a reasonable scope for a company information server. Each tool serves a clear purpose without being excessive or insufficient.
The tools cover the main areas: overview, services, use cases, contact, and target audience matching. A minor gap is the absence of a direct scheduling or pricing detail tool, but services includes pricing.
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