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josemdasilvag-blip

venue-ops-mcp

venue_ops_get_day

Read-onlyIdempotent

Retrieve detailed trading figures for a single venue on a specific day, including sales by channel, covers, average ticket, control block metrics, and labour costs—all expressed as comparable percentages of gross sales.

Instructions

Return the full trading figures for one venue on one day.

Gives sales split by channel, covers, average ticket, the control block (discounts, comps, voids) and labour cost, with every rate expressed against gross so the percentages are comparable across venues.

Args: params (DayInput): Validated parameters containing: - venue_id (str): Venue identifier, e.g. 'MAD-01' - day (str): Trading day, YYYY-MM-DD - response_format (ResponseFormat): 'markdown' or 'json'

Returns: str: The day's figures.

JSON keys include: net_sales, dine_in_sales, delivery_sales, covers,
transactions, average_ticket, spend_per_cover, discounts, comps, voids,
control_total, gross_sales, discount_rate, comp_rate, void_rate,
labour_cost, labour_hours, labour_pct (float rates are fractions of 1).

Error response: "Error: <message>" naming the valid venues or dates.

Examples: - Use when: "How did Gran Via do on 14 August?" - Don't use when: You want to know whether that day was normal (use venue_ops_compare_weekday).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by detailing the exact output format (markdown or json), listing all JSON keys, and describing the error response format. This goes beyond the annotations without contradicting them.

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 well-organized with clear sections (Args, Returns, Examples). Every sentence is informative and earns its place. The main purpose is stated in the first sentence, and the total length is proportional to the tool's complexity.

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?

The tool is simple (single venue, single day), and the description covers input parameter details, output structure (with key names), error handling, and usage examples. It also differentiates from a sibling tool. Given the output schema exists and the tool has good annotations, the description is fully sufficient.

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

Parameters4/5

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

The description includes an 'Args' section that describes venue_id, day, and response_format with examples. The input schema also has descriptions for each parameter, so the tool description adds context like 'Use venue_ops_list_venues to see them all' and the example date format. The return keys are also listed, which helps the agent understand parameter impact.

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 opens with 'Return the full trading figures for one venue on one day,' which is a specific verb+resource combination. It lists the exact data breakdown (sales by channel, covers, average ticket, etc.) and explicitly distinguishes from sibling venue_ops_compare_weekday in the 'Don't use when' example.

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?

The 'Examples' section provides a clear use case ('How did Gran Via do on 14 August?') and a non-use case with an explicit alternative tool ('use venue_ops_compare_weekday'). This gives the agent precise when-to-use and when-not-to-use guidance.

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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