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EchoRelay

by dev.echorelay

get_dlq_entry

Read-only

Get DLQ entries by Redis stream id ({ms}-{seq}, as returned by list_dlq's id) or by requestId. requestId is the durable handle — stable across a retry, unlike id, which changes every time an entry is replayed and later dead-letters again — and returns every fanned-out target's entry for that inbound request (one request can fan out to N targets, and several may dead-letter); id returns at most one. Each entry includes configVersion (the published config that authorised the delivery; 0 means unstamped). Errors if nothing matches this project — purged, retried, or discarded entries age out the same as any other.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoRedis stream id, e.g. "1717930000000-0" (as returned by list_dlq). Changes on every retry — prefer requestId to track an entry across replays.
requestIdNoThe UUID the relay returned in its 202, or from list_requests / list_dlq rows. Stable across retries. Returns every fanned-out target's DLQ entry for this request. Provide exactly one of id / requestId.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds substantial behavioral context: the durable vs. changing nature of identifiers, fan-out cardinality, the inclusion of configVersion with meaning, and the error condition when no entry matches. This exceeds the baseline expectation for a read-only tool.

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 somewhat long but every sentence earns its place. It front-loads the core purpose, then explains the nuanced differences between parameters and error behavior. No fluff or redundancy; the density is appropriate for the tool's complexity.

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?

There is no output schema, so the description compensates by explaining what entries include (configVersion). It covers both lookup modes, error conditions, and edge cases like purged/retried entries. Given the tool's complexity, the description is fully complete for an agent to select and invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides detailed descriptions for both parameters with 100% coverage. The description adds extra value by explaining the format of id, the stability of requestId, and the return behavior for fan-out scenarios, going beyond just restating the schema.

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 clearly states the tool's purpose: fetching DLQ entries, with two distinct lookup keys explained. It differentiates from siblings by explicitly covering both id and requestId semantics and the fan-out behavior, making it unambiguous what this tool does.

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

Usage Guidelines4/5

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

The description provides strong usage context by explaining when to use requestId vs id and noting the relationship to list_dlq. It does not explicitly name alternatives like discard_dlq_entry or retry_dlq_entry, but the read-only nature and the distinction from mutating siblings is evident.

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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TDQS

A3.6/5.0
Disambiguation3/5

Most tools target distinct resources and the descriptions are unusually explicit, but the billing cluster (change_plan/cancel_subscription and the many add-on actions) plus the preview/diff tools overlap and could cause mis-selection. config_diff, dry_run_endpoint, and preview_line_draft all read as 'preview what will change' at first glance despite different scopes.

Naming Consistency4/5

The overwhelming majority follow a clean snake_case verb_noun pattern: create_*, get_*, list_*, set_*, update_*, delete_*. It is only held back by a few naming outliers such as config_diff and default_endpoint_template, which break the verb-first convention.

Tool Count1/5

78 tools is an extreme mismatch for an MCP server surface, even accounting for the broad management/relay domain. Such a large surface will overwhelm model context and make tool selection materially harder; this would be better split into focused servers for configuration, data-plane operations, and billing.

Completeness4/5

The tool surface is very thorough: projects, lines, endpoints, credentials, keys, configs/drafts, DLQ, requests, metrics, audit, team, and billing are all represented. Only minor gaps exist, such as no direct single-line get/update and the intentional inability to widen the outbound allowlist or lift archive protection via API.

Resources