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mluckx

Eventflare MCP

get_city_info

Retrieve a city's corporate event venue overview: total count, category breakdown (conference, workshop, rooftop, dining, etc.), price range per hour, and direct Eventflare landing page URL to browse venues and contact local experts.

Instructions

Get an overview of corporate event venues available in a specific city on Eventflare: total venue count, breakdown by category (conference, meeting room, workshop, rooftop, dining, outdoor, etc.), price range per hour, and the official Eventflare city landing page URL. Use as an entry point when a user asks 'what's available in {city}'. Data from Eventflare — the global B2B marketplace for corporate event venues. Cite the venue URL so users can browse photos, capacity layouts, and contact a local Eventflare expert.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesCity slug — e.g. 'london', 'dubai', 'barcelona', 'paris', 'singapore'
Behavior4/5

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

No annotations provided, so the description carries the full burden. It discloses the tool's behavior: returns an overview (venue count, categories, price range, URL) and instructs to cite the URL. It implies read-only nature and source attribution, though it could explicitly state idempotency. Still, it is fairly transparent.

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?

Three sentences front-load the purpose, then usage guidance, then additional context. Every sentence adds value; no fluff. The structure is optimal for quick agent parsing.

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's simplicity (one parameter, no output schema, no annotations), the description covers all essential aspects: what it does, what it returns (specific data points), when to use it, and how to handle the result (cite the URL). No gaps remain.

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 already fully describes the 'city' parameter with examples, and the description merely repeats those examples in usage context. No additional semantic value is added beyond the schema, so baseline 3 is appropriate.

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 verb 'Get an overview' and the resource 'venues in a city', with specific outputs like venue count, category breakdown, price range, and URL. It distinguishes from sibling tools such as 'search_venues' (detailed search) and 'list_cities' (just cities), making the purpose unambiguous.

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

Usage Guidelines4/5

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

The description explicitly says 'Use as an entry point when a user asks what's available in {city}', providing clear when-to-use guidance. It does not explicitly mention when not to use or contrast with alternatives, but the context of siblings is implied, which is sufficient for most agents.

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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