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Find shops

market_search
Read-onlyIdempotent

Find shops in the Lucerna market by what they do, what they say about themselves, AND WHAT THEY ACTUALLY STOCK. Use this when you know what you are shopping for but not which shop — 'a barber in Denver', 'heavyweight black tee'. Words are matched against each shop's own prose and against its live shelf — titles, descriptions, categories, tags and variant labels — so you can search for the PRODUCT and not only for a shop that happens to describe itself using your word. Each row says which it was (matched_on: words, shelf, or both). unreachable names any shop whose shelf refused, so a shop that stocks the thing and would not answer is never silently missing from your count. Filter with can to require a capability. Results are alphabetical: there is no paid placement and no ranking to game. Then call shop_lookup or concierge_ask on one. THIS SEARCHES SHOPS AND THEIR SHELVES, NOT THIS PLATFORM'S OWN PRODUCTS. A shop sells tees, food and files; the platform's own subscription lines (agent memory, video generation, mailboxes, plans) are never on a shelf, so a search here for 'saas', 'subscription' or 'pricing' will correctly find nothing — call platform_catalog for those, and never report an empty market as proof that none exist.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNowords to match against a shop's name, tagline, description, mission and location AND the words on its live shelf (titles, descriptions, categories, tags, variant labels) — every word must appear somewhere, so more words narrow the result. 'san diego tee' can match a town from the shop's prose and a product from its shelf.
canNorequire ALL of these capabilities: bookings (takes appointments), shop (sells goods), quote (quotes custom work by walking a concierge), tips
limitNohow many shops to return (default 24, max 100)
offsetNoskip this many — page with `total` from the answer

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?

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds important behavior beyond that: matching against live shelves, the `matched_on` and `unreachable` result semantics, alphabetical ordering with no paid placement, and the exclusion of platform-owned products. This materially improves the agent's mental model.

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 long but every sentence earns its place: use-case, matching semantics, result fields, capability filtering, ordering, follow-up tools, and scope exclusions. It is front-loaded with the core distinction and avoids filler.

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?

With no output schema, the description compensates by describing key return concepts (`matched_on`, `unreachable`, alphabetical order) and caveats (unreachable shelves, platform products absent). It covers the practical questions an agent would have before calling the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the schema descriptions are already strong. The description reinforces `q` semantics and mentions `can` as a capability filter, but it doesn't add much new meaning about limit/offset beyond what the schema already explains. Baseline 3 is appropriate.

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 states a specific verb and resource ('Find shops in the Lucerna market') and expands scope to actual shelf contents, not just self-descriptions. It also explicitly distinguishes itself from platform_catalog, preventing confusion about what entity is searched.

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?

It gives an explicit when-to-use trigger ('when you know what you are shopping for but not which shop') and names the alternatives: shop_lookup or concierge_ask for following up, and platform_catalog for platform subscription products. It even warns against misinterpreting an empty result as proof that platform offerings don't exist.

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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