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josemdasilvag-blip

venue-ops-mcp

venue_ops_list_venues

Read-onlyIdempotent

Retrieve a complete list of venues and the covered date range to identify available venue IDs before querying detailed daily figures.

Instructions

List every venue in the dataset and the period the data covers.

Call this first when you do not know which venue ids exist. It is cheap and it tells you the date range every other tool will accept.

Args: params (ListVenuesInput): Validated parameters containing: - response_format (ResponseFormat): 'markdown' or 'json'

Returns: str: Venue list and covered period.

JSON schema:
{
    "currency": str,               # e.g. "EUR"
    "period": {"start": str, "end": str},
    "venues": [
        {"venue_id": str, "name": str, "city": str, "seats": int}
    ]
}

Examples: - Use when: "Which sites do we have data for?" - Don't use when: You already have a venue id and want figures (use venue_ops_get_day).

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

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety profile is clear. The description adds value by explaining the tool reveals the date range accepted by other tools, which is behavioral context beyond annotations. However, it does not detail what happens on failure or edge cases (e.g., empty dataset).

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?

Extremely concise and well-structured. Description front-loads the core purpose, follows with usage guidance, then succinctly documents parameters and output schema via Args/Returns/JSON schema. Every sentence adds value without redundancy.

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?

Given the tool has a single parameter, a clear output schema, and rich annotations, the description is complete. It covers purpose, usage context, parameter details, and expected output structure. No gaps remain for agent decision-making.

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 coverage is 0% but there is only 1 parameter (response_format) which is well described in the schema (markdown vs json). The description adds an example of usage context but does not significantly improve schema understanding. Baseline 3 is appropriate as schema already handles this simple parameter adequately.

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 states the tool lists every venue and the period data covers. It differentiates from siblings by explaining when to call this first (when venue ids are unknown) and directs to venue_ops_get_day for known venue ids.

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 advises to call this first when venue ids are unknown, notes it is cheap, and clarifies the date range context. Provides concrete when-to-use and when-not-to-use examples, including which sibling tool to use instead (venue_ops_get_day).

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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