Routebase

Test Persona

test_persona

Probe a persona's credentials against an environment before a scan relies on them — one GET to probePath with the persona's resolved auth. A 2xx, 401 or 403 counts as success: the headers reached the target. Requires project context.

Input Schema

TableJSON Schema

Name	Required	Description
`personaId`	Yes	Public Id (Guid) of the persona to probe (from list_personas)
`probePath`	No	Path to probe, default '/'
`environmentId`	Yes	Public Id (Guid) of the environment supplying the base URL (from list_environments)

Schema Changelog

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

First observedSep 7, 2026

TDQS

A4.6/5.0

Behavior4/5

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

The description discloses the exact HTTP method (GET), the target (probePath), how auth is applied (resolved auth), and the success criteria (2xx, 401, or 403). The annotations (readOnlyHint: false, destructiveHint: false) align with this safe, non-destructive behavior, and the description adds useful detail beyond 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 compact and well-structured. The em-dash separates the core action from the result interpretation, and the success criteria are stated in a clear, scannable way. No unnecessary words are used.

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?

Despite lacking an output schema, the description fully explains what the tool does, how it behaves, and what to expect (success statuses). It also notes the project context requirement, giving an agent everything needed to decide when to call it.

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 three parameters are fully described with types, defaults, and source hints (e.g., 'from list_personas'). Schema coverage is 100%, and the description enhances understanding by specifying that probePath defaults to '/' and that IDs are public GUIDs.

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: to probe a persona's credentials against an environment via a single GET request. It uses a specific verb ('Probe') and distinguishes it from related tools like get_resolved_auth or get_environment_auth by focusing on testing rather than retrieval.

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 explains when to use the tool ('before a scan relies on them') and notes a prerequisite ('Requires project context'). It does not explicitly name alternative tools to avoid, but the context makes the usage scenario clear.

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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