BizClaw Business Directory MCP

Ask About Companies

ask_about_companies

Ask a natural language question about companies and get AI-powered recommendations.
Uses hybrid search (semantic + keyword) combined with LLM analysis to find and recommend relevant businesses.

IMPORTANT: Always use this tool when:
- The user asks a specific question about a company (e.g., "do they offer bargaining?", "what are their prices?", "do they deliver to X?")
- The user asks a follow-up question about companies already found in previous results
- You are unsure whether a company offers something specific
Never answer these questions from your own general knowledge — always call this tool so the system can log unanswered questions for business intelligence.

Args:
    question: Natural language question (e.g. "Which logistics companies offer cold chain delivery in Istanbul?")
    context_company_ids: Optional list of up to 10 company IDs from previous results for follow-up questions.
                         ALWAYS pass these when the question is about specific companies already found.

Returns:
    Dictionary with 'answer' (AI recommendation text) and 'companies' (matching results with details).

Input Schema

TableJSON Schema

Name	Required	Description
`meta`	No	Optional UCP request metadata. Use {'ucp-agent': {'profile': 'https://agent.example/.well-known/ucp'}} for UCP-aware negotiation.
`question`	Yes	Natural language question about companies, recommendations, pricing, policies, features, or follow-up context.
`context_company_ids`	No	Optional company UUIDs from previous results for follow-up questions.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior4/5

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

The description discloses relevant behavior beyond the annotations, including that unanswered questions are logged for business intelligence and that results are AI-generated recommendations. The annotations do not contradict this, and the description adds useful context about how the tool operates.

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 longer than average but well-structured with clear sections, bolded guidance, and bullet points. The Returns section is somewhat redundant with the output schema, but the overall organization makes the important instructions easy to find.

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?

The description covers the tool's purpose, when to use it, key parameters, return shape, and even a business-intelligence side effect. It is complete enough for correct invocation, though it could have briefly mentioned when to use related sibling tools like search_companies.

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 the baseline is 3. The description adds value by explaining when context_company_ids must be passed, providing an example question, and clarifying that context_company_ids is a list of up to 10 IDs from previous results.

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 asks a natural language question about companies and returns AI-powered recommendations. It also explains that it uses hybrid search plus LLM analysis, which differentiates it from sibling tools like search_companies and get_company.

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 explicit 'Always use this tool when' conditions and instructs the agent never to answer from general knowledge. It gives clear context for use, though it does not explicitly name alternative sibling tools or describe when to prefer them.

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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TDQS

A4.2/5.0

Disambiguation4/5

Most tools target clearly distinct actions: searching, retrieving company details, creating proposals, and replying. The main potential confusion is between search_companies and ask_about_companies, and between create_proposal and create_batch_proposals, but their descriptions clarify the intended use cases well.

Naming Consistency4/5

Tool names mostly follow a consistent snake_case verb_noun pattern like search_companies, get_company, create_proposal, and reply_to_proposal. ask_about_companies deviates slightly from the pattern but is still readable and predictable.

Tool Count5/5

Nine tools is well-scoped for a business directory MCP server covering discovery, company lookup, and proposal management. Each tool has a distinct role and no tool feels redundant or unnecessary.

Completeness4/5

The tool surface covers the core workflow well: search companies, get details, create proposals individually or in batch, list proposals, and reply to company responses. Minor gaps exist—such as no explicit cancel/update proposal or direct proposal-by-ID lookup—but these are not blocking for typical usage.

Resources

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