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list_dlq

Read-only

List entries currently in this project's dead-letter queue, newest first. One inbound request fans out per-target, so a single request may produce several DLQ entries, one per target that didn't accept it (different targetIds). Returns {total, limit, offset, rows[], evictedCount} where each row has id (the dead-letter entry id, {ms}-{seq}), requestId, targetId, configVersion (the published config that authorised the delivery; 0 means unstamped), failureReason, failureCode (stable machine-readable cause; unclassified for entries written before codes existed), failedAttempts, failedAt, payload (the JSON of the queued delivery as it was attempted); evictedCount is the lifetime count of entries the queue's capacity cap discarded before they could be triaged. DLQ entries — including the original request body and headers — are kept for up to 30 days from the failure time or until cleared, then purged automatically (or discarded early past capacity — see evictedCount); they are never written to a database. get_request still answers what happened to a purged/evicted/discarded entry's inbound request for the project's requestLogRetentionDays window, independent of whether the DLQ row itself still exists. requestId is the durable handle across a retry: an entry's own id changes every time it is replayed and later dead-letters again, so get_dlq_entry accepts a requestId lookup as well as id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size. Default 50.
offsetNoPage offset. Default 0.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true and destructiveHint=false, but the description goes far beyond this by explaining fan-out per-target, 30-day retention, automatic purging, capacity-cap eviction, non-persistence to a database, and the id-versus-requestId semantics on replay. This is rich behavioral disclosure that annotations do not provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and then provides dense, non-redundant detail about return fields, retention, eviction, and id behavior. It is a long single paragraph rather than a structured layout, but every sentence earns its place given there is no output schema.

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 complex tool with no output schema, the description is remarkably complete: it specifies the response shape, field-by-field semantics, retention and eviction behavior, and how this tool relates to get_request and get_dlq_entry. An agent has enough to call it correctly and interpret results.

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?

Both parameters, limit and offset, are fully documented in the input schema with ranges and defaults (100% schema coverage). The description adds no additional parameter-specific meaning beyond the schema, so 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: 'List entries currently in this project's dead-letter queue, newest first.' It clearly distinguishes this listing operation from related siblings like get_dlq_entry, discard_dlq_entry, and retry_dlq_entry, so an agent can tell what it is for without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives contextual pointers, such as get_request still answering for purged/evicted entries and get_dlq_entry accepting requestId as well as id, which implies when a more targeted lookup is useful. However, it never explicitly states when to use list_dlq versus get_dlq_entry, nor does it give exclusions or alternative-selection rules.

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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