EchoRelay

list_audit_events

Read-only

List project audit-log entries, newest first. Captures who changed what — lines, endpoints, targets, API keys. Outbound-target auth.token / auth.password are redacted in the diff per the same policy used for endpoint reads. Retention is the auditRetentionDays advertised on get_project (default 365 days); rows older than that are purged by the cleanup job. Returns {total, limit, offset, retentionDays, rows[]} where each row has id, createdAt, actor (email or null), action, entityType, entityId, entityLabel, diff.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Page size. Default 50.
`since`	No	Lower bound on createdAt (unix ms). Inclusive.
`until`	No	Upper bound on createdAt (unix ms). Inclusive.
`action`	No	Exact action match, e.g. "endpoint.updated", "target.created", "key.revoked".
`offset`	No	Page offset. Default 0.
`entityId`	No	Exact entity id (UUID or stream id depending on entityType).
`entityType`	No	Exact entity type, e.g. "endpoint", "target", "line", "key".

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnlyHint and destructiveHint annotations, the description discloses key behaviors: newest-first ordering, redaction of outbound-target auth fields, retention policy (auditRetentionDays from get_project, default 365 days, purged by cleanup), and the exact return shape. This is substantial behavioral context.

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 concise and well-structured: four sentences each add distinct information (purpose, scope, redaction, retention, response shape). It's front-loaded with the primary action and contains no fluff.

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?

With no output schema, the description fully describes the return object ({total, limit, offset, retentionDays, rows[]}) and row fields. It also covers ordering, redaction, and retention, making it complete for a read-only list tool with 7 optional parameters.

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?

All 7 parameters have descriptions in the input schema (100% coverage). The tool description adds minimal additional parameter meaning—it echoes limit/offset in the return shape but doesn't explain parameter syntax or format beyond the schema. Baseline 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 clearly states the tool's function: 'List project audit-log entries, newest first.' It names a specific resource (audit-log entries) and provides scope ('Captures who changed what — lines, endpoints, targets, API keys'), distinguishing it from sibling list tools like list_api_keys or list_endpoints.

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 implies the tool's use case (inspecting project audit history) and gives context about ordering and content. It doesn't explicitly exclude alternatives or mention when not to use, but no sibling tool covers audit logs, so the context is sufficient.

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

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