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Glama

Check seat availability

check_seat_availability

Check live seat availability for an Indian Railways train by train number, stations, class, and quota, returning AVAILABLE, RAC, WL, or REGRET status with confirmation estimates.

Instructions

Seat or berth availability for one train, class and quota on a date. Returns the railway status string (AVAILABLE / RAC / WL / REGRET) and, where known, a confirmation estimate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesAlighting station: either the station code (e.g. "NDLS") or the name (e.g. "New Delhi").
dateNoJourney date as YYYY-MM-DD, or "today" / "tomorrow". Defaults to today in IST.
fromYesBoarding station: either the station code (e.g. "NDLS") or the name (e.g. "New Delhi").
quotaNoBooking quota (default GN = General).
trainNumberYesFive digit train number.
travelClassYesClass to check, e.g. "3A".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It does well by enumerating the possible status values (AVAILABLE / RAC / WL / REGRET) and qualifying the confirmation estimate with 'where known'. This gives an agent realistic expectations about output and uncertainty, though it does not cover rate limits or error behavior.

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 two sentences with no padding. The primary scope is front-loaded, and the output contract is communicated compactly. Every sentence adds value.

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?

Given no output schema, the description adequately explains the return value (status string plus confirmation estimate). The schema covers all parameters, so an agent can call the tool correctly. A minor gap is that the format of the 'confirmation estimate' is not described, but this does not block selection or invocation.

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?

The input schema has 100% description coverage, so the description does not need to repeat parameter details. The prose adds minor context by grouping train, class, and quota as the core selection criteria, but it does not add semantic meaning beyond what the schema already provides.

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 clearly specifies the resource (seat/berth availability), the scope (one train, class, quota, and date), and the key output (status string and confirmation estimate). This distinguishes it from sibling tools like get_fare, get_pnr_status, and find_trains_between_stations even without naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The scope wording implies that this tool is for checking availability on a specific already-identified train, rather than for searching trains or faresable. However, it does not explicitly state when to use it instead of siblings, nor does it mention any exclusions or alternative tools.

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