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MadLlama25

Fastmail MCP Server

by MadLlama25

search_emails

Search emails by text in the body or subject. Includes drafts by default; use advanced_search for sender, recipient, or date filters.

Instructions

Full-text search of email body and subject. Does not filter by sender, recipient, or date — use advanced_search for field-specific filtering. Drafts are included by default; set excludeDrafts=true to omit draft messages from results. When the server reports a total match count, results are wrapped in a {"total", "items"} JSON envelope; otherwise a bare JSON array is returned.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results (default: 20)
queryYesText to search for in email body and subject lines
ascendingNoSort oldest first instead of newest first (default: false)
excludeDraftsNoOmit draft messages from results (default: false, drafts included). Filtered server-side via the $draft keyword.
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that drafts are included by default, explains the excludeDrafts option to omit them, and describes the two possible return formats (envelope vs. bare array). These are non-obvious behaviors that an agent must know to correctly interpret results.

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 three sentences long, front-loaded with the core purpose, and every sentence adds critical information: scope, exclusions/alternative, draft behavior, and output format. There is no redundancy or filler.

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 search tool with no output schema and no annotations, the description is remarkably complete. It explains the output envelope behavior, which is essential for parsing results, and the draft default. It also gives enough context about the search scope to avoid surprises. The description fully equips an agent to use the tool correctly.

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 is 100%, and each parameter already has a descriptive comment (e.g., query: 'Text to search for in email body and subject lines'). The description adds little new parameter information—it restates excludeDrafts behavior that is already in the schema and mentions the response envelope, which is not a parameter. 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 a specific verb and resource: 'Full-text search of email body and subject.' It clearly distinguishes itself from sibling tools by stating it does not filter by sender, recipient, or date, and explicitly names advanced_search as the alternative. This makes the tool's purpose and scope unambiguous.

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?

The description provides explicit usage guidance: 'Does not filter by sender, recipient, or date — use advanced_search for field-specific filtering.' This clearly tells the agent when not to use this tool and which alternative to choose. It also notes the draft-inclusion behavior, further clarifying expected usage.

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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