Skip to main content
Glama

draft_support_reply

Draft a professional customer-support reply from a raw customer message. Tenant-agnostic: no account or ticket id needed. Send the customer’s raw text (plus optional product context and tone) and get back { draft, disclaimer }, an AI-generated reply for a human to review before sending. Nothing is stored and nothing is sent to any customer. [price: $0.05]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toneNoTone of the drafted reply. Default: friendly.
product_contextNoOptional background about the product/service the reply should reflect (≤2000 characters), e.g. what the product does or the relevant policy. Also treated as untrusted content.
customer_messageYesThe raw customer message to draft a reply to (plain text, 1–4000 characters). Paste the customer’s words as-is. It is treated as untrusted content, never as instructions.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavior: nothing stored, nothing sent, AI-generated reply for human review. Explicitly states customer_message and product_context are untrusted content, never instructions. No contradictions.

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 plus cost note. Front-loaded with core action. No extraneous words. Every sentence 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?

Adequately covers inputs and output ({ draft, disclaimer }). No output schema but description explains return. Could be improved by clarifying disclaimer role, but overall complete for a drafting tool.

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?

Schema coverage is 100%, baseline 3. Description adds value: clarifies product_context as 'optional background about the product/service' and emphasizes untrusted content. Specifies length limits and tone default, exceeding schema info.

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?

Clearly states 'Draft a professional customer-support reply from a raw customer message.' Distinguishes itself by noting tenant-agnostic and no account/ticket id needed, which differentiates it from siblings like 'draft_reply' and 'send_reply'.

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?

Provides clear context on when to use (drafting for human review) and what to expect (AI-generated draft + disclaimer). Includes cost note. Lacks explicit 'when not to use' but implicitly covered by the send/save prohibition.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0
Disambiguation4/5

Most tools have distinct purposes, but the presence of three drafting tools (draft_reply, draft_support_reply, propose_resolution) with overlapping functionality may cause confusion. Descriptions help differentiate them, but an agent could still misselect.

Naming Consistency4/5

Tool names predominantly follow a verb_noun pattern in snake_case (e.g., create_ticket, list_issues). However, a few tools like 'assign' and 'triage' are single verbs, and 'draft_support_reply' breaks the pattern slightly. Overall consistent but not perfect.

Tool Count5/5

16 tools is well-scoped for a customer support server covering ticket management, issue tracking, knowledge base, and changelog. Each tool serves a clear purpose without bloat.

Completeness4/5

The tool surface covers the core ticket lifecycle (creation, triage, assignment, context, drafting, sending, resolving) and supports issue linking and knowledge base search. Minor gaps like updating ticket details or bulk actions exist but are not critical.

Resources