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Glama

Weav Customer Service

by io.github.dubdubdubco

get_comparison

Return a short structured contrast of Weav vs Ada, Intercom, and Zendesk, plus the comparison page URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYesDescribe the user's underlying goal in one sentence — not the tool you're calling.
llm_modelYesThe 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_idNoEcho 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.

  1. Changed4 schema fields changed
    • 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"
+]
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. The verb 'Return' makes the read-only, non-mutating nature reasonably clear, and it describes the output shape. However, it does not disclose any limitations, error cases, or additional behaviors beyond the basic return value.

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 a single, information-dense sentence that front-loads the action and includes the essential details: the compared products and the URL output. Every word 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?

The tool is simple, all parameters are fully described in the schema, and the description gives a clear high-level account of the returned content. Since there is no output schema, a slightly more detailed return shape would be ideal, but the description is sufficient for correct invocation and basic use.

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 baseline is 3. The tool description adds no parameter-specific meaning beyond what the schema already provides; it does not explain how context, llm_model, or conversation_id affect the call.

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 identifies the exact resource: a contrast of Weav vs Ada, Intercom, and Zendesk, plus the comparison page URL. It clearly distinguishes this tool from siblings like get_product_overview or get_pricing, which cover different content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not state when to use this tool versus alternatives, or mention any exclusions or prerequisites. Usage is only implied by the tool name and the mention of a comparison resource; there is no explicit routing or alternative guidance.

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