Weav Customer Service

get_pricing

Return Weav public pricing: Lite, Plus, Pro, Max monthly prices, annual discount, add-ons, and signup/sales links. Use this instead of scraping weav.com/pricing.

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. Dates show when Glama detected each change.

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

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It signals a read-only operation via 'Return' and states the data is public pricing, implying no special auth or destructive side effects. It also positions the tool as the sanctioned replacement for scraping, which usefully sets expectations about reliability, though it does not mention caching or data freshness.

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?

The description is two sentences with no wasted words. The first sentence front-loads the return contents, and the second sentence gives direct usage guidance. Everything present earns its place.

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?

Although there is no output schema, the description enumerates the key return fields, giving the agent a clear idea of what the response will contain. It omits minor details such as currency or regional applicability, but for the stated scope and low complexity, the description is sufficiently complete.

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%, and each parameter (context, llm_model, conversation_id) already has a detailed description in the input schema. The tool description adds no per-parameter semantics, so the baseline score of 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 uses a specific verb ('Return') and a clear resource ('Weav public pricing'), then enumerates the exact contents: Lite, Plus, Pro, Max monthly prices, annual discount, add-ons, and links. This makes the tool's scope unambiguous and distinguishes it from siblings like get_comparison and get_product_overview.

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 explicitly says to use this tool instead of scraping weav.com/pricing, which provides clear, actionable usage guidance. It does not explicitly address sibling tools like get_comparison or get_demo, but the pricing-specific content list makes the intended use case evident.

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

A4.2/5.0

Disambiguation5/5

Each tool maps to a distinct purpose: comparison, demo, pricing, product overview, signup, and tool discovery. There is no meaningful overlap between them, so an agent should rarely confuse one for another.

Naming Consistency5/5

All tool names follow the same get_<noun> convention, using clear lowercase with underscores. This is consistent and predictable across the entire set.

Tool Count5/5

Six tools is a well-scoped size for a customer-service/sales information server. Each tool covers one necessary action without requiring a large or redundant surface.

Completeness5/5

The set covers the full customer journey from understanding the product and pricing to comparing, booking a demo, and signing up. Product overview and comparison also link to deeper resources, so there are no obvious dead ends.

Resources

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