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Search dishes

tf_search_dishes
Read-onlyIdempotent

Search dishes by name near a delivery point, filter discounted items, and sort by price or rating to find meals. Returns dish prices, discounts, and restaurant names.

Instructions

Search dishes by name near a delivery point, with price, discount and restaurant.

Use for "find X near me" or "cheapest / discounted X". Prices are the search index's and can lag the menu: confirm with tf_menu before recommending. Then call tf_vendor_info for fee and minimum order. Tapsi's search lists only restaurants open now: at night total can be 0 (the site shows the same); tf_restaurants / tf_discounted_vendors also list closed vendors.

Input Schema

NameRequiredDescriptionDefault
latYesDelivery point latitude, e.g. 35.7577.
longYesDelivery point longitude, e.g. 51.4095.
pageNoPage number, from 1.
sortNoOrder of the results; cheapest / biggest_discount / top_rated sort the first 100 matches (top_rated = dish rating, then restaurant rating; unrated last). nearest is Tapsi's own order.relevance
limitNoMax dishes (40 keeps the reply under 15 KB).
queryYesDish or restaurant word, e.g. 'پیتزا'.
discounted_onlyNoOnly dishes with a discount.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark this read-only, idempotent and non-destructive, so the description is free to add the non-obvious behavior: indexed prices can lag the live menu, and only currently-open restaurants are listed, so results can be empty at night. These freshness and availability caveats are exactly the context annotations cannot express.

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?

Front-loaded with the core action, then caveats and next steps in short, information-dense sentences. Slightly packed (three concerns in one compact paragraph), but no sentence is filler.

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 7 parameters and no output schema, the description covers the search semantics, result interpretation (index lag, open-only), and downstream tools to call. Return shape and pagination behavior are left implicit, which is the only real gap for a list-returning 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 description coverage is 100%, so lat/long/query/page/limit/sort/discounted_only are already fully documented, including enum meaning and limits. The description only restates that search is name+location based and mentions price/discount, adding no syntax or format detail beyond the schema — the calibrated baseline.

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?

States a precise verb+resource plus scope: search dishes by name near a delivery point, returning price, discount and restaurant. An agent can distinguish this from tf_search_vendors, tf_menu and tf_vendor_info without opening any schema.

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?

Gives explicit trigger phrases ("find X near me", "cheapest / discounted X") and a chained workflow: confirm prices with tf_menu, then fee/minimum with tf_vendor_info. It also names alternatives (tf_restaurants / tf_discounted_vendors) for the closed-vendor case, so when-not-to-use is covered.

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