branchly

get_top_clicked_urls

Read-only

URLs users actually clicked from inside the embed (search results, citation links, follow-ups, etc.), ranked by click count. Optionally filter by click event type. Use this to see what users find useful enough to click through to.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of rows to return, ordered by count descending.
`event_types`	No	Restrict to specific click-event type(s): 'search_result', 'link', 'contact', 'tool', 'follow_up_navigation', 'follow_up_question'. Omit to include all.
`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

A4.2/5.0

Behavior4/5

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

The annotations already declare readOnlyHint=true, lowering the burden for safety disclosure. The description adds useful behavioral context beyond annotations by specifying that results are ranked by click count and scoped to clicks 'from inside the embed', including examples like search results and citation links. No contradiction with annotations.

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 compact: two sentences with the core result and ranking front-loaded, followed by a practical use case. The parenthetical examples add clarity without bloat, and no sentence is wasted.

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 an output schema present and full parameter documentation in the schema, the description does not need to explain return values or parameter syntax. It adequately covers the tool's scope and intent. A slightly higher score would require explicit guidance on sibling distinctions like get_top_cited_sources, but that is not essential for correct invocation.

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%, so the schema fully documents limit, event_types, and time_filter. The description only mentions the optional 'click event type' filter, which adds no meaning beyond the schema. Per calibration, baseline 3 is appropriate when the schema carries the parameter semantics.

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 states exactly what the tool returns: 'URLs users actually clicked from inside the embed', and specifies the ranking method ('ranked by click count'). It clearly differentiates this from sibling analytics tools like get_top_searches or get_top_cited_sources by focusing on user click behavior rather than searches, citations, or interaction sources.

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 context for when to use this tool: 'Use this to see what users find useful enough to click through to.' It does not explicitly name alternatives or state when not to use it, but the use case is concrete enough for an agent to select it appropriately among the analytics siblings.

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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