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find_offers

Find offers from local businesses for what a person asked for. Send their sentence as text; the hub parses the trade, the town and the day. An answer with complete: false carries a question to put to the person: call this tool again with the same session and their reply as text. An answer with complete: true is one finished bidding round: bids ranked (price in grosz, slot, business, note) and one outcome per business asked. An empty bids list is a normal answer; outcomes says why.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesWhat the person said, in Polish or English, at most 500 characters. Their reply to the previous question goes here too.
sessionNoThe session id from an earlier answer. Omit it to start a new conversation.
selectionNoRestrict the round to one business (and optionally one of its services), by slug, as a link from a business page does.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

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 carries the full burden, and it delivers: it discloses the conversational protocol, explains complete:true/false semantics, describes the ranked bids structure, and explicitly says an empty bids list is normal. This is substantially more transparent than a generic 'find offers' statement.

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 dense but every clause earns its place: purpose, required input, conversation loop, return semantics, and edge-case behavior are all included without repetition or filler. It is front-loaded with the primary purpose before diving into protocol details.

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 there is no output schema, the description covers the return contract well: complete, question, bids, outcomes, and the empty-bids case. It also explains how to continue the multi-turn conversation, which is essential for invoking this tool correctly in an agent loop.

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 that text is the raw user sentence, that session must be carried over from a previous incomplete answer, and that replies go back through text. The selection object gets no additional description-level commentary, but the schema already documents it clearly.

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 opens with 'Find offers from local businesses for what a person asked for,' stating a concrete action and resource. It also clarifies the tool's parsing role by mentioning trade, town, and day, which distinguishes it from the booking and agent-management siblings.

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?

It gives clear operational guidance: send the person's sentence as text, reuse the session when following up, and handle complete:false by asking the returned question. It does not explicitly name alternatives or when-not-to-use conditions, but the purpose and workflow are clear enough to avoid confusion with siblings.

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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