Routebase

Get Request Logs

get_request_logs

Read-only

Get request logs from the mock server showing recent incoming requests and matched rules. Supports filtering by HTTP method, path, status code, and time range. Pass logId to get ONE log with its full request and response bodies and headers instead of the list — that is where you look when a request matched the wrong rule. Requires project context (call set_context first).

Input Schema

TableJSON Schema

Name	Required	Description
`skip`	No	Number of logs to skip (for pagination, default 0)
`take`	No	Number of logs to return (default 50, max 100)
`logId`	No	Public Id (Guid) of a single log. When given, returns that one log in full (bodies and headers) and every filter and pagination parameter is ignored.
`method`	No	Filter by HTTP method (e.g., 'GET', 'POST')
`statusCode`	No	Filter by response status code
`mockServerId`	Yes	The public Id (Guid) of the mock server
`pathContains`	No	Filter by path containing this text

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 7, 2026

TDQS

A5/5.0

Behavior5/5

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

The annotation readOnlyHint is consistent with the description's read-only nature. The description further discloses that when logId is passed, all filters and pagination parameters are ignored, which is a key behavioral detail not fully captured by the 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 three sentences long but packs essential information without redundancy. It is front-loaded with the primary purpose and follows with filtering and logId-specific behavior, maintaining clarity and brevity.

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?

Given there is no output schema, the description sufficiently covers all necessary operational aspects, including how to retrieve logs and the special behavior of logId. No critical information is missing for an agent to use this tool correctly.

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

Parameters5/5

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

All seven parameters are described in the schema with clear meanings, and the description adds extra context for logId explaining it overrides filters. This goes beyond the schema to clarify parameter interactions.

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 retrieves request logs from a mock server, including recent incoming requests and matched rules. It also explicitly distinguishes between listing logs and retrieving a single log with full details via logId, making its purpose unambiguous.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use logId (when a request matched the wrong rule) and notes that project context is required via set_context. It also explains that filters are ignored when logId is provided, giving clear usage instructions.

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

A3.9/5.0

Disambiguation4/5

The tools are mostly distinct with clear descriptions. Some pairs like get_header_policies vs get_resolved_headers or get_environment_verification vs get_monitoring_sync_status could be slightly confusing, but the descriptions clarify scope and purpose.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern (get_, list_, create_, update_, manage_, etc.). Even the few bare verbs like 'search' and 'set_context' are consistent with the naming scheme.

Tool Count1/5

With 165 tools, the server is extremely heavy. This far exceeds the 'too many' threshold of 25+, making it difficult for an agent to navigate and select the right tool efficiently.

Completeness5/5

The tool surface covers a very broad API lifecycle domain: specs, environments, test cases, monitors, mock servers, security, governance, documentation, and team management. Read and write operations are present across most areas, with no obvious missing core functionality.

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