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KrystalView

KrystalView MCP Server

Official
by KrystalView

get_sessions

List and filter visitor sessions by device, country, friction score, duration, and rage clicks to identify frustrated users or specific behavior patterns.

Instructions

List recent visitor sessions with filtering.

Each session includes: duration, page count, entry/exit URLs, device info, screen size, IP address, location (country/city), friction score, and rage click count.

Use this to find sessions matching specific criteria — e.g. frustrated mobile users, visitors from a specific country, or high-friction sessions.

Args: query: Search entry/exit URLs (e.g. "/pricing", "/checkout") limit: Max results (1-100, default 20) offset: Pagination offset country: Filter by country name device_type: "mobile" or "desktop" min_friction: Minimum friction score (0-10) min_duration: Minimum session duration in seconds has_rage_clicks: Only sessions with rage clicks

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
limitNo
offsetNo
countryNo
device_typeNo
min_frictionNo
min_durationNo
has_rage_clicksNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries full burden. It discloses the returned fields but does not mention read-only nature, rate limits, authentication requirements, or pagination behavior beyond offset/limit. Adequate for a simple list tool but could be more transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a front-loaded purpose statement, a field list, a usage example, and parameter definitions. It is concise but could potentially omit the field list if the output schema is rich, though the output schema is not shown.

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 8 parameters (none required) and an output schema, the description covers parameter semantics and usage context well. It lacks information on sorting, performance, or rate limits, but overall it is complete enough for an agent to select and invoke correctly.

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?

Schema description coverage is 0%, so the description must compensate. It does so effectively with an Args section explaining each parameter's purpose, valid values, and defaults. This adds significant meaning beyond the schema's basic titles.

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 'List recent visitor sessions with filtering' and enumerates the fields included in each session. This distinctively differentiates it from siblings like get_session_detail (single session) and get_anomalies/funnels (different analyses).

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 explicit usage examples ('frustrated mobile users, visitors from a specific country, or high-friction sessions') and implies when to use this tool for listing vs get_session_detail for detail. It could improve by explicitly stating when not to use it, but the examples offer strong 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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