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Sessy — Amazon SES observability

by io.github.marckohlbrugge
OverviewDiscussionsAdmin

Email stats

email_stats
Read-onlyIdempotent

Aggregate email stats for one source or the whole account over a date range: counts by event type, unique opens/clicks, bounce/complaint/open/click rates, bounce breakdown by subtype, and an optional daily time series. Rates are percentages of sends.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "email_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
to_dateNoISO 8601 date or timestamp; only with date_range custom
from_dateNoISO 8601 date or timestamp; only with date_range custom
source_idNoLimit to one source (id from list_sources); omit for the whole account
date_rangeNoDate window preset (default last_30_days); use custom with from_date/to_date
include_daily_seriesNoInclude a per-day sent/delivered/bounced series (ranges up to 120 days); default false

Output Schema

NameRequiredDescriptionDefault
hintNoPresent when there were no sends in the applied window
scopeYes
countsYes
daily_seriesNo
rates_percentYes
bounce_breakdownYesCounts keyed by bounce subtype (Permanent, Transient, Undetermined, Unknown)
applied_date_rangeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is established. The description adds valuable behavioral context beyond annotations: rates are defined as percentages of sends, the optional daily series is noted, and the metric list clarifies exactly what the tool returns. No contradiction exists, and this extra detail helps interpret the output.

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 fronts the main purpose and then lists supporting details. Every clause adds value—scope, date range, metric types, and the send-percentage clarification. It avoids fluff and is easy to scan.

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?

For a tool with 0 required parameters, an output schema, and rich annotations, the description covers all essential usage aspects: aggregation behavior, source scoping, date range, metric types, and the optional time series. Edge cases like custom date range constraints are handled by the schema, so the description is complete for the intended complexity.

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 coverage for all 5 parameters is 100%, so the schema carries full semantic weight for parameter details. The description adds minimal parameter-specific meaning (e.g., 'one source or whole account' maps to source_id, 'date range' relates to date_range), but these are restatements of what the schema already documents. Thus the baseline of 3 is appropriate.

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 opens with the specific verb 'Aggregate' and the resource 'email stats', then clarifies scope ('one source or the whole account') and date range. It enumerates the exact metrics (counts, opens/clicks, rates, breakdowns, optional series), making it immediately distinct from sibling tools like search_events or get_message.

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 clearly implies when to use this tool: when the user needs aggregate metrics (counts/rates) across a date range. It gives context about source scope and date presets, but it does not explicitly mention alternatives or exclusions (e.g., 'for individual events use search_events'). This meets 'clear context, no exclusions' rather than the explicit always/never guidance required for a 5.

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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