Skip to main content
Glama

get_active_sessions_by_embed

Read-only

Time series of active sessions broken down by embed type (chat, chat_widget, navigator, search_interface, voice, api). Use this to understand which interfaces are driving usage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
time_filterNoTime window for the analytics query. Accepts either: (a) a preset enum value: 'this_month', 'this_year', 'last_month', 'last_30_days' (default), 'last_6_months', 'last_12_months'; or (b) an explicit ISO date range as 'YYYY-MM-DD,YYYY-MM-DD' (inclusive).last_30_days

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.8/5.0
Behavior3/5

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

The annotations already declare readOnlyHint=true, so the description need not restate read-only behavior. It adds context about the time-series shape and the breakdown by embed type, but does not disclose other behavioral traits like aggregation interval, response size, or how missing embed values are handled.

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 two sentences with no filler; the core output and breakdown are stated first, followed by a direct usage rationale. Every sentence earns its place.

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?

With one optional, fully documented parameter, a read-only annotation, and an output schema, the description is nearly complete for correct invocation. It could be stronger by explicitly contrasting with get_active_sessions_over_time, but the embed-breakdown language already routes an agent reasonably well.

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 only parameter, time_filter, is fully and clearly documented in the schema with presets, a custom-range pattern, defaults, and inclusivity semantics. With 100% schema description coverage, the tool description has no obligation to repeat parameter details, and it adds no extra meaning beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource and dimension: a time series of active sessions broken down by embed type, with the enum of embed types explicitly listed. It differentiates from sibling get_active_sessions_over_time by emphasizing the breakdown, though it does not explicitly name that sibling or state what the tool is not.

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?

'Use this to understand which interfaces are driving usage' gives clear and relevant context for when the tool is the right choice. It does not mention alternatives or exclusion cases, such as when total active sessions without a breakdown would be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation5/5

Each tool targets a distinct resource or metric. The many get_top_* endpoints are differentiated by the specific dimension measured, and read_* / list_* / get_* verbs consistently separate detail retrieval from aggregation and paginated listings. Explicit distinctions like get_top_languages vs get_top_locales and get_top_interaction_sources vs get_top_clicked_urls remove ambiguity.

Naming Consistency5/5

Tool names follow a predictable verb_noun pattern: create_* for mutations that add, update_* for edits, list_* for paginated collections, read_* for detailed record access, and get_* for aggregate analytics. Even with 33 tools the naming convention is uniform and readable.

Tool Count2/5

33 tools exceeds the 25+ threshold for 'too many' and is heavy for a single server surface. While the analytics getters are individually focused, the set is larger than typical for an MCP server and could be consolidated or grouped more tightly.

Completeness3/5

Analytics coverage is thorough, and nodes/prompts have create/read/update lifecycles. However, there are no delete operations anywhere, and data sources and tools support update but not create or delete, leaving notable lifecycle gaps for administrative tasks.

Resources