Read the full tool calls executed for a single chat request. Returns tool_call_id, tool_id, tool_name, tool_type, full arguments (JSON), and full content/response (JSON). Use this to inspect what the assistant invoked and how the tool responded. Ordered by timestamp ascending.
| Name | Required | Description | Default |
|---|---|---|---|
| chat_request_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the operation as read-only, and the description aligns. It adds useful behavioral detail beyond the schema by noting results are 'Ordered by timestamp ascending' and that arguments/content are returned in full, which clarifies what the agent will observe.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three compact sentences with no filler: action/resource, return fields, intended use, and ordering. Each sentence adds a distinct piece of information and the key purpose is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter, read-only lookup with an output schema, the description covers what it returns, why it is used, and the ordering guarantee. Nothing essential for invoking the tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and there is one required parameter, so the description must carry the semantics. It does so by stating the operation is scoped to 'a single chat request,' mapping naturally to chat_request_id. It does not elaborate on the UUID format, but the schema supplies that.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Read' with a precise resource: 'full tool calls executed for a single chat request.' It also names the returned entities (tool_call_id, tool_name, arguments, response), which makes it unmistakable among sibling tools like read_chat_request_documents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Use this to inspect what the assistant invoked and how the tool responded,' giving a clear intended scenario. It does not mention alternatives or exclusions, but the context is specific enough for an agent to select it.
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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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.
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.
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.
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.