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Glama

agent-cold-email

by io.github.YS-projectcalc

contact_operator

Reach a human operator — for anything list_messages/infrastructure_status can't answer (a stuck vendor issue, a billing question, an account-level ask). Inputs: body (1-2000 chars), urgency ('normal' | 'needs_human', default 'normal'). Files a support ticket and notifies the operator; returns { ticketId, note, deduplicated }. Works in every account state a tenant token still authenticates in, including dunning-suspended, canceling and canceled — this is exactly the channel for 'why is my account suspended?'. The ONE exception is an admin-TERMINATED (abuse) account, whose token is rejected at auth with 401 before this tool runs. The operator's reply arrives as a message on THIS account (poll list_messages / infrastructure_status.messages[] — there is no separate reply-fetch call). Sending the IDENTICAL body AND urgency again within an hour returns the SAME ticketId and does not file a second ticket or send a second alert (deduplicated: true on that response; false when a new ticket was actually filed) — no separate idempotency key is needed to retry a dropped response. This is a TEXT match, not an intent match: the platform cannot tell a deliberate retry from a coincidentally-identical NEW message, so a genuinely new ask with the same wording collapses into the earlier ticket just as silently — vary the wording (or raise urgency, which is always treated as an escalation and files a new ticket) if you have something new to say. 'needs_human' also bypasses the ~10-minute ops-email throttle so an urgent message is pushed immediately. Rate-limited to 5 calls/hour per tenant — a 429 names retryAfter (seconds) when hit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes
urgencyNonormal

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

The description thoroughly discloses side effects and constraints beyond annotations: it creates a ticket, notifies an operator, deduplicates identical requests within an hour, and is subject to rate limiting. It also explains the 'needs_human' urgency bypasses the throttle. This is highly transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is excessively verbose and repetitive. It repeats the deduplication and throttle explanations multiple times, and the overall length is disproportionate to the simple functionality. It could be condensed into a few sentences without losing substance.

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?

Despite its verbosity, the description covers all necessary context: purpose, when to use, side effects, deduplication rules, rate limits, and account state handling. It leaves no ambiguity about the tool's behavior and limitations, making it fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful context to both parameters: 'body' is the message content, and 'urgency' influences behavior (e.g., 'needs_human' bypasses throttle). It clarifies default values and edge cases, fully enriching the schema which only provides types and constraints.

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 clearly states the tool's purpose: 'Reach a human operator' and 'Files a support ticket and notifies the operator'. It also provides specific examples of when to use it, distinguishing it from sibling tools like list_messages and infrastructure_status.

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

Usage Guidelines5/5

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

It explicitly states when to use the tool ('for anything list_messages/infrastructure_status can't handle') and gives concrete examples like 'why is my account suspended?'. It also explains deduplication and rate-limiting constraints, which are crucial usage guidelines.

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

A4.4/5.0
Disambiguation4/5

Most tools have distinct purposes, but pause/pause_all and metrics/campaign_results/list_campaigns have overlapping scopes. Descriptions clarify the differences, so ambiguity is low.

Naming Consistency4/5

Uses snake_case with a mix of verb_noun (list_campaigns) and noun_verb (infrastructure_status), but patterns are predictable. Minor inconsistency between setup_infrastructure and infrastructure_status.

Tool Count4/5

25 tools is at the upper bound for a focused server, but the domain (cold email automation) justifies the number. Each tool covers a distinct aspect of the workflow.

Completeness4/5

Covers account management, campaigns, leads, threads, infrastructure, webhooks, and dashboards. Missing resume for paused campaigns and delete for campaigns, but core operations are present.

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