message_outbound_dumpRetrieve the raw MIME source (full email dump) for a sent outbound message by messageId; useful for debugging encoding or header issues.
| Name | Required | Description | Default |
|---|---|---|---|
| messageId | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| Body | No | Raw MIME message content |
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 declare readOnlyHint and idempotentHint, so the description does not need to cover safety traits. It adds value by specifying that the output is the raw MIME source/full email dump, a key behavioral detail not evident from annotations. It does not mention potential response size or error conditions, but for a read-only getter this is sufficient.
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?
The description is a single sentence with the key action and intent front-loaded. Every phrase contributes value: 'raw MIME source', 'full email dump', 'debugging encoding or header issues'. No filler or 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?
For a simple read-only tool with robust annotations and an output schema, the description is complete. It conveys the tool's purpose, the identifying parameter, and the primary use case. There are no significant gaps given the tool's complexity.
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?
With 0% schema description coverage, the description compensates by explicitly referencing 'by messageId' and clarifying that it applies to sent outbound messages. The single parameter is self-explanatory, and the schema example shows a GUID format. A bit more detail on ID format or type constraints would push this to 5.
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 states a specific verb ('Retrieve'), resource ('raw MIME source'), and scope ('sent outbound message by messageId'). It clearly distinguishes this from sibling tools like message_outbound_detail by emphasizing the raw MIME dump and debugging use case.
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 provides a clear use context: 'useful for debugging encoding or header issues.' However, it does not explicitly mention alternatives or when not to use this tool, so it falls slightly short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set mixes multiple domains (Postmark email, Pipeworx data queries, Polymarket betting, memory utilities) with several overlapping tools. ask_pipeworx and ask_pipeworx_beta are essentially identical, send/send_batch and bounces/bounce are similar, and multiple polymarket analysis tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker) could be confused. Despite detailed descriptions, the sheer number of query and analysis tools increases the chance of misselection.
All tool names use lowercase_with_underscores, so the casing is consistent. However, there is no uniform verb_noun pattern: some start with verbs (ask, send, bounce, resolve, validate), while others are noun phrases (server, bounces, recent_alerts, entity_profile). This mixed semantic structure makes it less predictable, but the names are still readable.
41 tools is far above the typical well-scoped range of 3-15. The server combines multiple unrelated domains—email, data lookup, prediction markets, memory, and subscriptions—resulting in a heavyweight and unfocused surface. Most of the tools would be better split into separate, purpose-specific servers.
For a server named Postmark, the email side is incomplete: there is no update server configuration, message stream management, or inbound email handling. The Pipeworx data tools provide good read coverage but lack write/management operations for entities. The inclusion of unrelated tools makes the surface feel arbitrary rather than complete for any single domain.