agent4.io

list_end_users

List this tenant's end users (the people chatting with your agents) — roster only, never any conversation content.

Each user carries: display name, login method (email / oauth:* / proxy), email (if any),
space/session/document counts, last-24h token usage, and a `console_url` (click through for
detail). q filters by name or external id; sort ∈ recent (default) | tokens24h (descending
24h usage). Pagination: pass the returned next_cursor back as cursor.
Typical: list_end_users(sort="tokens24h") to find the most active / most expensive users.

Input Schema

TableJSON Schema

Name	Required	Default
`q`	No
`sort`	No	recent
`limit`	No
`cursor`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses the roster-only nature, returned user fields, sorting semantics, and pagination ritual (passing next_cursor back). It does not mention rate limits or the exact ordering of 'recent' (ascending vs. descending), but these are minor for a read-only list tool.

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 well-structured and dense: purpose at the top, followed by response highlights, filter/sort semantics, pagination, and a typical call. Every sentence earns its place with no redundancy or fluff.

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 that the output schema exists and the tool is a straightforward list operation, the description covers all essential aspects: result contents, filter/sort options, pagination, and a practical usage example. It provides enough information for an agent to select and invoke the tool correctly without needing to consult additional sources.

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?

The input schema has no parameter descriptions (0% coverage), so the description must add meaning—and it does: 'q filters by name or external id', 'sort ∈ recent | tokens24h', and pagination via cursor. The 'limit' parameter is left to inference, but its default (50) is visible in the schema and its meaning is self-evident.

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 this tenant's end users' and explicitly scopes it as 'roster only, never any conversation content,' distinguishing it from session-related sibling tools. It names the resource (end users) and the action (list), making the purpose unambiguous.

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 a concrete typical use case—'list_end_users(sort="tokens24h") to find the most active / most expensive users'—which helps an agent decide when to use it. However, it does not explicitly name alternative tools (e.g., list_user_sessions) or state when not to use it, stopping short of full when/when-not 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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TDQS

A3.6/5.0

Disambiguation4/5

Tools are mostly distinct by domain and action. A few pairs like add_knowledge_file vs add_knowledge_text or build_knowledge_index vs patch_knowledge_index could cause confusion, but descriptions clearly differentiate them.

Naming Consistency4/5

The vast majority follow a consistent verb_noun snake_case pattern (create_*, list_*, get_*, update_*). A few outliers like tenant_info, usage_stats, and page_context_stats are noun-first, deviating slightly from the otherwise uniform scheme.

Tool Count1/5

With 50 tools, this is a very large surface that exceeds typical well-scoped server sizes. Even for a broad platform management API, the sheer number makes it heavy and potentially overwhelming, matching the '50+' extreme mismatch category.

Completeness2/5

The server covers creation, reading, updating, and listing for most resources but lacks any delete operations (no delete_agent, delete_knowledge_base, delete_skill, delete_share, delete_storyline, delete_page_context). This is a significant gap in lifecycle coverage that will force agents to work around missing functionality.

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