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read_chat_request_documents

Read-only

Read the full document chunks retrieved for a single chat request (QA or SA). Returns chunk_id, vertex_id, title, full text, score, source, data source type, page metadata, and whether the chunk was cited in the final answer. Use this after read_session_detail to inspect the exact context the assistant grounded its answer in. Ordered by score descending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chat_request_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

Read-only behavior is already covered by readOnlyHint=true; the description adds useful behavioral details such as returning full text, indicating whether a chunk was cited, and ordering results by score descending. No contradictions 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?

Two compact sentences with a front-loaded purpose, a precise return-field list, workflow guidance, and ordering information. Every clause earns its place.

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 one-parameter read-only tool with an output schema, this covers purpose, workflow position, return content, and ordering. An agent has enough information to select and invoke the tool correctly.

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 description clarifies that the tool operates 'for a single chat request' and tells the agent to call it after read_session_detail, indicating where the chat_request_id comes from. Although it doesn't discuss the parameter by name, the single self-describing UUID parameter is sufficiently contextualized.

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?

States a specific verb and resource: 'Read the full document chunks retrieved for a single chat request.' Lists the exact returned fields and names the QA/SA context, making it clearly distinct from siblings like read_chat_request_tool_calls.

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?

Explicitly says to use this after read_session_detail, establishing the correct workflow. It does not enumerate alternatives or when-not conditions, but the intended context is clear and useful.

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

A3.7/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness3/5

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.

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