branchly

get_top_cited_sources

Read-only

Knowledge-base nodes most frequently cited in chat answers, ranked by citation count. Each row carries the node UUID (vertex_id), its title, optional source URL, and citation_count. Use this to understand which knowledge is actually load-bearing; pair with read_node(vertex_id) to inspect cited content.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of rows to return, ordered by count descending.
`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

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds useful behavior: results are ranked by citation count, each row contains uuid/title/optional URL/citation_count, and it is designed as a discovery step before read_node. 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 and front-loaded with the key behavior in the first sentence. The second sentence adds practical usage context and a pairing suggestion; no filler or redundant restatement of the tool name.

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?

For a read-only analytics tool with no required parameters, an output schema, and fully documented input parameters, the description is complete. It states what the tool returns, why it matters, and how to follow up with read_node.

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 input schema already provides 100% description coverage for limit, embed_types, and time_filter, including defaults and formats. The description itself does not add parameter-level semantics, so the baseline score of 3 is appropriate.

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 identifies the resource (knowledge-base nodes), the ranking criterion (citation count), and the context (chat answers). It differentiates itself from sibling analytics tools like get_top_searches and get_top_clicked_urls by specifying cited sources and listing the returned fields.

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?

It gives clear context on when to use the tool ('understand which knowledge is actually load-bearing') and suggests pairing with read_node(vertex_id) to inspect content. However, it does not explicitly state when not to use it or name alternative analytics tools for different questions.

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.

Resources

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