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vexo-mcp

by kishanssg

vexo_get_recent_events

Read-only

Retrieve recent raw events for a specific entity with filters. Returns event names, screens, sessions, and metadata sorted newest first.

Instructions

Forensic dive into one entity's most recent raw events (newest first). Use after the aggregate tools point you at a specific entity and you need to read the actual event stream — names, screens, sessions, payloads.

Scans the configured lookback window (default 30 days) unless start_date/ end_date are given. Identify the entity with the "filters" selector.

Inputs: filters: REQUIRED key/value selector identifying the entity, e.g. {"worker_id":"54"} or {"deviceId":"abc-123"}. n: number of most recent events. Default 10, max 50. start_date, end_date: OPTIONAL ISO dates to override the lookback window.

Returns: { filters, window, rows:[{timestamp, event_name, screen, session_id, metadata}], truncated, total_count? }. On failure: { error }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersYesentity selector, e.g. {"worker_id":"54"}
nNo
start_dateNo
end_dateNo
Behavior4/5

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

Annotations provide readOnlyHint=true (read-only) and openWorldHint=true. The description adds important behavioral details beyond annotations: it scans a default 30-day lookback window, allows override via start_date/end_date, limits n to max 50, and describes the return format including truncated and total_count. No contradictions.

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 appropriately concise and well-structured: it starts with the core purpose, then provides usage context, a bulleted list of inputs with defaults, and a return format summary. Every sentence is informative and there is no 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 4 parameters, nested objects, and no output schema, the description is complete. It covers purpose, when to use, parameter details with examples, return structure (including truncated and total_count), and failure case. An agent has all necessary information to select and invoke the tool correctly.

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

Parameters5/5

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

Schema coverage is 25% (only filters has a description). The description compensates fully: explains filters is a required key/value selector with examples, n is number of events (default 10, max 50), start_date/end_date are optional ISO dates. This adds substantial meaning beyond the schema, ensuring an agent can use parameters correctly.

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's purpose: 'Forensic dive into one entity's most recent raw events (newest first).' It uses a specific verb ('get') and resource ('recent events' for an entity). It also distinguishes from siblings by noting it is used after aggregate tools point to a specific entity, differentiating it from tools like vexo_count_events or vexo_overview.

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 provides clear guidance on when to use: 'Use after the aggregate tools point you at a specific entity and you need to read the actual event stream.' It implies not to use it for aggregated views. While it does not explicitly name sibling tools as alternatives, the context is sufficient for an AI agent to understand the appropriate scenario.

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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