branchly

get_active_sessions_over_time

Read-only

Time series of active sessions, bucketed by day or week (auto-chosen from the time window). Each row breaks the total down by interaction type (chat / search / navigation / form_submission / voice). Gaps are zero-filled, so the series is safe to plot directly. Use this to spot spikes, dips, or trends.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`embed_types`	No	Restrict to sessions initiated through these embed type(s). Omit to include all embeds.
`time_filter`	No	Time 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

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior5/5

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

Even though readOnlyHint=true already signals safety, the description adds valuable behavior: auto-selection of day/week buckets, zero-filling of gaps, and direct plot safety. These details go well beyond the annotations and help the agent understand what to expect from the returned series.

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 concise sentences front-load the core purpose, then add bucket granularity, interaction breakdown, and a practical usage cue. Every sentence earns its place with no redundancy or filler.

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 simple optional-parameter surface, a complete output schema, and read-only annotations, the description covers everything an agent needs: what the series contains, how gaps are handled, and when to use it. Nothing critical is missing.

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 description coverage is 100%, with both parameters (embed_types and time_filter) fully described in the input schema. The description does not need to repeat parameter details, so the baseline of 3 applies.

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 opens with a specific resource ('active sessions') and a clear temporal dimension ('Time series... bucketed by day or week'), and states the breakdown by interaction type. This makes the tool's purpose unambiguous and easily distinguishable from sibling analytics tools like get_active_sessions_by_embed.

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 final sentence gives an explicit use case: 'Use this to spot spikes, dips, or trends.' It provides clear context for when to select this tool, though it does not mention when not to use it or explicitly contrast it with alternatives.

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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TDQS

A3.6/5.0

Disambiguation5/5

Each tool targets a distinct resource or metric, and even the closely related analytics tools (e.g. get_top_languages vs get_top_locales, get_top_interaction_sources vs get_top_clicked_urls) are explicitly differentiated in their descriptions. There is no real overlap that would cause an agent to misselect.

Naming Consistency4/5

The verb prefixes create_, get_, list_, read_, and update_ are used predictably, and there is no mixing of camelCase or other conventions. The main inconsistency is that read_sessions is actually a list operation while list_nodes is the equivalent pattern for nodes, and read_session_detail is the singular read.

Tool Count2/5

At 33 tools, this set is well beyond the 16-25 'heavy' range and far above the typical well-scoped 3-15 range. Many of the get_top_* analytics endpoints are individually distinct but could likely be consolidated into fewer parameterized tools to reduce agent selection overhead.

Completeness3/5

The read and analytics side is comprehensive, but the management lifecycle has notable gaps: knowledge nodes support create/read/update but no delete, and data sources/tools lack create/delete operations. Agents can work around some gaps, but content deletion is a clear dead end for a knowledge-base management surface.

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