List Messages
list_messagesAuthenticated tool to list messages.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| appId | Yes | ||
| limit | No |
list_messagesAuthenticated tool to list messages.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | ||
| appId | Yes | ||
| limit | No |
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 indicate read-only, idempotent, non-destructive behavior. The description adds that it requires authentication, which is useful but minimal. It does not detail side effects, rate limits, or output behavior beyond the schema.
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 short and not verbose, but it is under-specified. It earns its place but adds little value, leaving important information absent.
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 output schema and 0% parameter coverage, the description fails to explain return format, filtering, or pagination. Annotations cover safety but not usage context, making it incomplete for agent decision-making.
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 description coverage is 0% and the description provides no explanation of parameters. The meaning of 'q' and 'limit' is entirely dependent on naming, which may confuse an AI agent.
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 clearly states the verb 'list' and resource 'messages', distinguishing it from siblings like 'send_message' or 'get_message'. However, it lacks specificity about scope (e.g., per app or time range).
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 mentions 'Authenticated', implying a prerequisite, but provides no guidance on when to use this tool vs alternatives (e.g., search messages) or when not to use it.
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.
Most tools have distinct purposes, but there is some overlap between `list_messages`, `list_recent_events`, and `list_message_attempts`, and between `replay_event` and `replay_events`. However, the descriptions generally clarify differences.
The naming mostly follows a verb_noun pattern (create_, list_, get_, update_, delete_), with consistent snake_case. A few tools like `rotate_endpoint_secret` and `upsert_event_type` deviate but still use verb_noun structure.
With 52 tools, the set is extremely large and may overwhelm agents. While the domain is complex, this many tools reduce coherence and make selection more difficult.
The tool surface is very comprehensive, covering CRUD for most resources, replay, retries, anomaly management, and webhook setup. Minor gaps exist (no delete for destinations, no update for sources), but core workflows are well-supported.