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knowledge-base

by com.tunnelpowered

search_businesses

Find businesses, merchants and websites in the tunnel knowledge base by name or topic. Start here: every other tool needs a slug, and this is where a slug comes from. Returns an array of summaries, each with slug, kind, name, description and a verification object. Read verification.level rather than assuming: "human" means a tunnel employee checked the business, "automated" means machines proved only that the business controls its own channels, and null means neither. Zero matches is a normal answer, not an error — it comes back with completeness "empty". Authentication: none. This tool works with no credentials.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional. Return only records of this kind.
limitNoOptional. Maximum results, 1 to 50. Defaults to 20.
queryYesName, topic or place. Words are matched independently against the name, location, description, offerings and FAQ, and most of them have to appear somewhere in a record, so "cakes in Chisinau" works. Accents are ignored, so the unaccented spelling is fine. This matches words, not meanings: a business calling itself a "cofetăria" will not be found by searching "bakery". If nothing comes back, try the words the business would use about itself.

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavior: authentication is none, it works without credentials, returns an array with specific fields, explains the verification object and its levels, and describes the empty result response. No contradictions.

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 a single paragraph of 6 sentences, front-loaded with purpose and key advice. Every sentence adds unique information (purpose, entry-point, return format, verification, error handling, authentication). No wasted words.

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?

Given 3 parameters, no output schema, and no annotations, the description covers all critical aspects: purpose, usage context, return structure with verification, behavior on empty results, authentication, and parameter tips. It is fully complete for agent decision-making.

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 description coverage is 100%, so baseline is 3. The description adds significant value beyond the schema: explains query matching logic (words independently, accents ignored, examples), default limit, and optional type. This enhances usability.

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 explicitly states the tool's action ('find'), resource ('businesses, merchants and websites'), and scope ('by name or topic'). It also differentiates this tool from siblings by noting that every other tool needs a slug from this one.

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 explicitly positions this tool as the entry point ('Start here: every other tool needs a slug, and this is where a slug comes from'). It also explains that zero matches is a normal answer, not an error. While it doesn't explicitly list when not to use it, the context is clear.

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

A4.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but ask_business and ask_business_freeform both serve the core function of asking a question about a listing, which could cause misselection. The detailed descriptions mitigate the overlap, but the similar intent remains a minor ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (ask_, check_, contact_, get_, search_, send_), with only ask_business_freeform adding a modifier that still fits the convention. Naming is predictable and readable.

Tool Count5/5

Nine tools is well-scoped for a knowledge base server, covering discovery (search, get), questioning (ask), asynchronous communication (contact, followup, replies, escalation), and verification. Each tool earns its place without unnecessary bloat.

Completeness5/5

The tool set provides full coverage of the domain: finding businesses, reading profiles, asking structured and freeform questions, escalating unresolved queries, opening and continuing conversations, and checking verification. Poll-based tools handle dead ends explicitly, so there are no obvious gaps.

Resources