Weav Customer Service

get_product_overview

Read-onlyIdempotent

Return a structured overview of Weav: AI agents, unified inbox, channels, training, actions, and escalation. Links to product and docs.

Input Schema

TableJSON Schema

Name	Required	Description
`context`	Yes	Describe the user's underlying goal in one sentence — not the tool you're calling.
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedSep 2, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "Describe the user's underlying goal in one sentence — not the tool you're calling.",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

addedInput schema / required

Added value: +[
+  "context",
+  "llm_model"
+]

First observedSep 1, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds modest context about the output containing a structured overview and links to product/docs, which is useful but does not disclose details like formatting, link types, or any unusual behavior. With annotations carrying the safety burden, this is adequate but not exceptional.

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?

A single, front-loaded sentence states the core function, then lists the content scope and the presence of links. No filler or repetition exists, and the structure makes the tool's purpose immediately legible.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only informational tool with no output schema, the description conveys the topic coverage and that links are included. It could more explicitly describe the return structure, but the term 'structured overview' plus the enumerated areas is sufficient for an agent to invoke and interpret results reasonably.

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 100%, so the schema fully documents all three parameters, including the important guidance for context, llm_model, and conversation_id. The description adds no parameter-level meaning, but that is not necessary given the rich schema descriptions. Baseline 3 applies.

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 names a specific verb ('Return'), a clear resource ('structured overview of Weav'), and enumerates the covered areas (AI agents, unified inbox, channels, training, actions, escalation). This sufficiently distinguishes it from siblings like get_pricing, get_comparison, and get_demo, which target different intents.

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 makes the intended context clear: when the user wants a structured product overview and links to product/docs. It does not explicitly state when not to use it or name alternatives, but the scope is specific enough that an agent can infer the appropriate call versus siblings like get_signup or get_more_tools.

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

All Weav Customer Service tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?