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gabrielbelli

pelando-mcp

by gabrielbelli

search_stores

Find a merchant's live activity counts and top coupons by searching its name. Results are ranked by match closeness, but verify the store name before using the first result.

Instructions

Look up a merchant: its live activity counts and its top coupons.

Matching is loose and substring-based upstream — "amazon" also returns "Amaro" — so results are ranked here by how closely the name matches, but the caller should still check the name before acting on the first row.

promotion_count tells you how many active promotions a store has, but there is no endpoint anywhere that lists them. To see a store's deals, search a product term and filter by store.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
include_couponsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.1

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses loose substring matching, that results are re-ranked by name similarity, and the important caveat that promotion_count has no listing endpoint anywhere. It omits auth/permission needs, rate limits, and pagination behavior, keeping it short of 5.

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?

Three tight paragraphs, front-loaded with purpose and then escalating to caveats; every sentence carries information. The bold/backtick formatting is slightly heavy but does not waste space.

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?

With no output schema and no annotations, the description compensates by naming returned fields (promotion_count, top coupons) and warning about match accuracy and the promotions dead-end. The main gap is the undocumented limit and include_coupons parameters, which leaves an agent guessing on pagination and coupon inclusion.

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

Parameters2/5

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

Schema description coverage is 0% across three parameters, so the description must compensate and largely does not: query matching is loosely described, but limit and include_coupons are never explained, including what include_coupons=false suppresses. Only the query parameter gets meaningful elucidation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a concrete verb and resource ("Look up a merchant") and states what comes back ("live activity counts and its top coupons"), which is more than a restatement of the name. It partially differentiates from siblings by pointing to the alternate path ("search a product term and filter by store") for deals, though it never names search_deals or get_store_coupons directly.

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?

Provides actionable usage guidance: verify the returned name before acting on the first row, and use product-term search when the goal is deals. It lacks an explicit "when not to use this / use X instead" routing statement naming the sibling tools, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.