Run AI categorisation over a batch of inbox emails to label intent and priority. Consumes AI credits — pass only the emails you actually need triaged.
| Name | Required | Description | Default |
|---|---|---|---|
| email_ids | Yes | Email IDs to categorise (max 50 per call) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey idempotentHint=true (safe to retry) and destructiveHint=false (no destruction). The description adds important behavioral context: it consumes AI credits (a cost/resource implication) and implies it processes a batch (max 50). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise, front-loaded sentences. The first sentence states the core function, the second provides critical usage guidance. Every word adds value; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (1 param, no output schema), the description is sufficient. The agent understands input, behavior (AI credit consumption), and constraints (max 50). A minor gap: the output format (returned labels/priority) is not described, but since there is no output schema, this is a soft miss rather than a hard requirement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (only one parameter, 'email_ids', well-described with array type, string items, and max 50 limit). The description reinforces that these are emails 'to categorise' and adds the consumption cost context, which helps the agent decide which IDs to pass. No additional parameter documentation is needed beyond what's already provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Run AI categorisation') and resource ('batch of inbox emails') and states the outcome ('label intent and priority'). This clearly distinguishes it from siblings like 'archive_email', 'reply_to_email', or 'get_email', which handle different operations on emails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises to only pass emails that need triaging due to AI credit consumption. This provides clear when-to-use guidance. However, it does not explicitly mention when NOT to use this tool (e.g., for already categorized emails) or name specific alternative tools, though the sibling list provides implicit context.
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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Most tools are cleanly separated by resource and action, and the descriptions do a good job of cross-referencing related tools. However, there are several close clusters—get_analytics vs generate_report, get_deliverability_score vs run_deliverability_audit, check_dmarc vs verify_domain, and list_emails vs list_inbox_conversations vs get_email—that could cause an agent to pick the wrong one. The detailed descriptions reduce but do not eliminate this ambiguity.
Tool names follow a consistent snake_case verb_noun pattern throughout: create_, get_, list_, send_, toggle_, and so on. Even multi-word actions like select_ab_test_winner and categorize_inbox_emails stay uniform. The only slight deviation is the bare verb upgrade, but it is readable and does not break the overall pattern.
54 tools is far beyond the typical well-scoped MCP surface and lands heavily in the 'too many' range. While the domain is broad, many tools could be consolidated—multiple analytics/reporting tools, several deliverability checks, and separate email/inbox listing tools create redundancy. The sheer number increases selection overhead and makes the toolset harder for an agent to navigate reliably.
The core email marketing lifecycle is represented: domains, contacts, campaigns, templates, automations, sends, and analytics all have main operations. However, there are notable gaps—no update/delete for campaigns, templates, forms, or automations; no create/update/delete for forms; no sandbox enable/disable; and no way to install marketplace items. These are workable gaps but would cause failures for agents trying to perform full lifecycle management.