EchoRelay

list_requests

Read-only

List inbound API requests the relay has processed for this project, newest first. Hot-tier window is the project's requestLogRetentionDays (see get_project). Optional filters narrow the result. Returns {total, limit, offset, rows[]} where each row has ts (unix ms), requestId, method, path, status, durationMs, targets, creditsDeducted, authType, errorMessage, bytesSent, configVersion (the published config that authorised the request; 0 means unstamped), and closeReason (for a streaming request: complete / abortedOrError / byteCap / noValue; empty for non-stream requests).

Input Schema

TableJSON Schema

Name	Required	Description
`path`	No	Substring of the request path (e.g. "/webhook"). LIKE-matched.
`limit`	No	Page size. Default 50.
`since`	No	Lower bound on ts (unix ms). Inclusive.
`until`	No	Upper bound on ts (unix ms). Inclusive.
`method`	No	HTTP method (POST, GET, …). Case-insensitive.
`offset`	No	Page offset. Default 0.
`status`	No	Exact HTTP status code (e.g. 404).
`targetOutcome`	No	Narrow to requests that produced at least one per-target attempt with this outcome. Use "dlq" to find requests with at least one target that ended up dead-lettered.

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnlyHint=true annotation, the description discloses ordering ('newest first'), a retention constraint (hot-tier window), and the full return shape including field-level semantics for configVersion, closeReason, and targets. This is substantial behavioral detail not derivable from annotations.

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 well-structured and front-loaded: first sentence states the core action and ordering, then retention, then filters, then the return shape. Every sentence earns its place, and the detailed field list is necessary because 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?

The tool has 8 parameters and no output schema, yet the description covers all essential context: what it lists, ordering, retention window, filter behavior, and a complete enumeration of return fields including edge cases like '0 means unstamped' and closeReason values. This is fully adequate for an agent to invoke the tool correctly.

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?

Schema coverage is 100%, with each parameter already having a description. The description adds only a general statement that filters narrow the result, and ties the hot-tier window to the project setting. It does not add meaningful new parameter semantics beyond the schema, so baseline 3 applies.

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 states a specific verb and resource: 'List inbound API requests the relay has processed for this project'. It also differentiates from siblings by indicating 'newest first' and the plural 'requests', making it clear this is a batch listing tool distinct from get_request.

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 clear context: 'Hot-tier window is the project's requestLogRetentionDays' and 'Optional filters narrow the result', implying when the tool is appropriate. However, it does not explicitly name alternatives or state when not to use it, so it lacks explicit exclusions.

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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