list_dead_letterList posts that failed all retry attempts and were moved to the dead letter queue. Review and decide to requeue or discard them.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum items to return (default 50) | |
| status | No | Filter by status (default: dead) | |
| team_id | No | Team ID to operate in team scope. Get available teams with list_teams. If omitted, uses personal scope. |
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=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable context beyond annotations by explaining that these posts 'failed all retry attempts' and were moved to the dead letter queue, which clarifies the state of the data. No side effects are disclosed because none exist for a read operation.
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 two sentences, front-loaded with the action ('List posts...'), and each sentence adds value. The first explains what the tool does, the second provides usage guidance. There is no wasted wording or repetition of schema/annotation details.
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?
This is a simple list operation with no output schema and 3 well-documented parameters. The description provides essential domain context (dead-letter queue, retry failures) and hints at the follow-up actions. It is sufficiently complete given the schema and annotations, though it could mention the returned data shape if that were important.
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 100%: all three parameters (limit, status, team_id) have descriptions in the schema. The tool description itself does not add any parameter-specific meaning, so it earns the baseline score of 3 for a schema that already documents parameters fully.
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 uses a specific verb ('List') with a clear resource ('posts... moved to the dead letter queue') and defines the domain concept. It distinguishes from the sibling tool 'requeue_dead_letter' by focusing on listing vs. acting. The two-sentence description fully conveys the tool's function.
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 clear context: this is for reviewing dead-letter posts before deciding to requeue or discard. It implies a workflow ('Review and decide to requeue or discard them') without explicitly naming alternative tools. It could have stated 'use this instead of requeue_dead_letter for inspection,' but the intent is clear.
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.
With 148 tools, there is significant overlap. For example, generate_content, publish_ai, generate_post_bundle, and request_project_content all generate content; get_analytics, get_unified_analytics, get_post_analytics, get_ad_performance, and get_unified_ad_report all fetch performance metrics; and list_inbox vs list_conversations blur comment and conversation management. Descriptions help, but boundaries between tools are often unclear.
Most tools follow a verb_noun pattern (e.g., list_teams, create_goal, delete_post), but there are notable deviations: create_library_item vs save_to_library, publish_content vs publish_ai, schedule_content vs schedule_content_advanced, and connect_platform vs connect_connector. Mixed prefixes like 'autopilot_', 'check_', and 'get_' are fine, but overlapping verbs and a hyphen in 'connect_linkedin-page' reduce consistency.
148 tools is extreme for any server. Even for a broad social media management platform, this is far beyond what an agent can effectively navigate. The count is unwieldy and suggests the surface should be split into multiple focused servers (publishing, analytics, connectors, workflows, etc.).
The core social publishing workflow is well covered (create, schedule, publish, edit, delete, retry), and there are extensive features for analytics, workflows, connectors, and AI agents. However, some resources have CRUD gaps: no update/delete for brand voices, no delete_project, no update/delete for Product Hunt goals, and no explicit get_workflow. These are workable but notable omissions.