Save hours every week. This email summary tool toolkit gives you a battle-tested prompt and operating playbook for restaurant/brand growth. No setup, works in any LLM chatbot.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional user query to scope the analysis. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden and delivers none of it. It says nothing about what input it expects (an email body? a thread? a file?), whether it mutates anything, what permissions or limits apply, or what the response looks like.
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?
It is short but front-loaded with promotional filler ("Save hours every week", "battle-tested", "No setup") that consumes the entire description without conveying operational information. Every sentence is waste from an agent's perspective.
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?
With no annotations, no output schema, and only a vague promo blurb, an agent lacks the essentials: what input to supply, what a summary contains, and how it differs from adjacent summary tools. The schema covers the lone parameter, but the surrounding behavioral context is absent.
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?
There is a single optional parameter with 100% schema description coverage ("Optional user query to scope the analysis"), so the schema already documents it. The description adds no meaning about how a query scopes the summary, which meets the baseline 3 for high coverage but no more.
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 text mostly restates the tool name ("This email summary tool toolkit") and then pivots to unrelated marketing copy about "restaurant/brand growth" and a "battle-tested prompt and operating playbook." It never states the concrete operation (e.g., condense a thread into bullet points or an executive summary), so an agent cannot tell what it actually does versus siblings like email-sort-tool or email-summary-tool variants.
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?
"No setup, works in any LLM chatbot" is a sales claim, not usage guidance. There is no statement of when to choose this tool over the many email-* and *-summary-tool siblings, no prerequisites, and no exclusions.
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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