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Read-onlyIdempotent

Search MrBilit's official rules, FAQs, and help-center guides to find passages on refunds, cancellations, baggage, ID, auto-reserve, and change requests.

Instructions

Search MrBilit's official rules, FAQ and help-center guides; returns the best matching passages.

Use for refund and cancellation rules, ID and baggage rules, how auto-reserve works, how to request a change, etc. Passages are plain text with the section they come from. Rules of one fare are in mb_flight_fare_details / mb_search_trains / mb_search_buses instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax passages.
queryYesQuestion or keywords in Persian, e.g. 'استرداد بلیط قطار' or 'رزرو خودکار'.
sourceNoterms = official rules (refunds, baggage, ID), faq = general FAQ, support = help-center guides and request forms.all

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered structurally. The description adds useful behavioral context beyond that: results are plain-text passages accompanied by their source section, and it notes the content domain it indexes. It does not discuss result limits or ranking behavior, but the output schema covers the return shape.

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 compact sentences, front-loaded with the purpose, then usage, then the exclusion. Each sentence carries distinct information, though the source-category phrasing mildly duplicates the enum documentation.

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?

For a read-only search tool, the description covers purpose, invocation contexts, exclusions, and result format, while annotations carry safety and an output schema exists for return values. Nothing an agent needs to select or call this correctly is missing.

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% and the schema already documents query (with Persian examples), limit, and the source enum values in detail. The description only loosely echoes the source categories ('rules', 'FAQ', 'help-center guides') without adding syntax or format guidance, so the baseline of 3 applies.

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 specific verb (Search) and resource (official rules, FAQ, help-center guides) plus the return shape (best matching passages). It also names the sibling tools that own adjacent content (mb_flight_fare_details / mb_search_trains / mb_search_buses), so an agent can distinguish it 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?

Explicitly enumerates when to use it (refund and cancellation rules, ID and baggage rules, auto-reserve behavior, change requests) and gives a clear when-not with named alternatives for fare-specific rules. Nothing is left to inference.

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