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 declare readOnlyHint=true, which covers the safety profile. The description adds useful behavioral context beyond that: it returns the full arguments and content/response as JSON, and results are ordered by timestamp ascending. This is meaningful and non-redundant.
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?
Three short sentences, each earning its place: what it reads, what it returns, and why to use it. No filler or repetition of schema details.
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?
The tool is simple (one parameter, one resource) and has an output schema. The description covers the operation scope, return contents, ordering, and usage intent, so an agent has everything needed to select and call it correctly.
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?
The only parameter, chat_request_id, has no schema description (0% coverage), but the description compensates by tying the operation to 'a single chat request.' This makes the purpose of the required uuid parameter clear enough for correct invocation.
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') and resource ('full tool calls executed for a single chat request'), clearly distinguishing it from siblings like read_chat_request_documents or read_session_detail. It also enumerates the exact returned fields, removing ambiguity about what the agent will get.
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 states the intended use: 'Use this to inspect what the assistant invoked and how the tool responded.' This gives clear situational context, though it does not explicitly contrast with sibling tools or mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or metric. The many get_top_* endpoints are differentiated by the specific dimension measured, and read_* / list_* / get_* verbs consistently separate detail retrieval from aggregation and paginated listings. Explicit distinctions like get_top_languages vs get_top_locales and get_top_interaction_sources vs get_top_clicked_urls remove ambiguity.
Tool names follow a predictable verb_noun pattern: create_* for mutations that add, update_* for edits, list_* for paginated collections, read_* for detailed record access, and get_* for aggregate analytics. Even with 33 tools the naming convention is uniform and readable.
33 tools exceeds the 25+ threshold for 'too many' and is heavy for a single server surface. While the analytics getters are individually focused, the set is larger than typical for an MCP server and could be consolidated or grouped more tightly.
Analytics coverage is thorough, and nodes/prompts have create/read/update lifecycles. However, there are no delete operations anywhere, and data sources and tools support update but not create or delete, leaving notable lifecycle gaps for administrative tasks.