branchly

update_chat_request_analytics

Idempotent

Update analytics fields on a chat request: summary, tags, answer_type, sentiment, classification_topic_id, or classification_intent_id. Only provided fields are updated. Use this to annotate or reclassify chat interactions after the fact.

Input Schema

TableJSON Schema

Name	Required	Description
`tags`	No	List of tags to attach to the chat request.
`summary`	No	Short summary or annotation for the chat request.
`sentiment`	No	Sentiment classification: 'positive', 'negative', or 'neutral'.
`answer_type`	No	Answer type classification: 'complete', 'no_knowledge', 'outside_scope', 'small_talk', or 'follow_up_question'.
`chat_request_id`	Yes
`classification_topic_id`	No	UUID of the classification topic to assign to this chat request.
`classification_intent_id`	No	UUID of the classification intent to assign to this chat request.

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 cover non-destructive and idempotent behavior, so the bar is lower. The description adds meaningful partial-update semantics with 'Only provided fields are updated', which tells the agent that omitted fields remain unchanged.

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 concise sentences both earn their place: the first states the action, resource, and fields, and the second states the intended use case. Information is front-loaded with no filler.

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?

The tool has a simple flat schema, an output schema, and annotations covering idempotency and destructive risk. The description sufficiently covers scope, behavior, and use case, leaving no critical gap for an agent to invoke it correctly.

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?

Schema description coverage is high (86%), so the schema already explains each parameter. The description adds value by grouping these as analytics fields and stating partial-update behavior, but it does not provide new per-parameter meaning.

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 uses a specific verb and resource ('Update analytics fields on a chat request') and lists all possible fields. It also distinguishes itself from sibling update_* tools by naming the chat-request resource and its analytics purpose.

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 phrase 'Use this to annotate or reclassify chat interactions after the fact' provides clear intended context. It does not explicitly name alternatives or exclusions, but no direct sibling alternative for this update operation exists.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All branchly tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?