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get_api_test_runs

Page through an API test's recent run history — each run's outcome (SUCCESS, FAILURE, ERROR, MISSED), timing, any assertion failures and its traceId: the W3C trace id the probe sent to the target as a traceparent header, so the target's own logs and traces for that run can be found by trace_id. Use this to investigate why an API test is unhealthy. Optionally filter by time window and status. The redacted request/response capture of each run is available on the HTTP API only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd of the window, ISO-8601 instant (inclusive)
fromNoStart of the window, ISO-8601 instant e.g. '2026-07-01T00:00:00Z' (inclusive)
pageNoZero-based page number (default 0)
sizeNoRuns per page — default and max 200; values outside 1-200 are clamped
statusNoFilter to one outcome: SUCCESS, FAILURE, ERROR or MISSED
apiTestIdYesId of the API test whose runs to fetch

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added
  2. Removed
  3. Added

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden and does so well: it explains traceId as the traceparent sent to the target, useful for correlating target logs, and notes that redacted request/response captures are available only via the HTTP API. It lacks details like sort order or auth requirements, but the core behavior is transparent.

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?

Each sentence earns its place: purpose and returned fields first, usage context second, filtering third, and the HTTP-only capture limitation last. The traceId clause is long but directly useful for downstream correlation, not padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description compensates by naming the key returned fields enough for an agent to understand what it will get, and adds the important redacted-capture caveat. It omits secondary details like sort order and pagination metadata, but not enough to impair a correct call.

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?

The schema already documents all six parameters at 100% coverage, so the baseline applies. The description adds only the high-level 'filter by time window and status' grouping and confirms the page-through behavior, without adding new parameter-level meaning.

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?

Description opens with a specific verb-resource pair, 'Page through an API test's recent run history,' and enumerates the fields returned (outcome, timing, assertion failures, traceId). This clearly separates it from sibling tools like get_api_test, which targets test configuration, by framing the resource as run history.

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?

It explicitly says 'Use this to investigate why an API test is unhealthy' and mentions optional time/status filtering, giving a clear trigger context. It does not name alternatives or state when not to use it, so it stops short of full when/when-not guidance.

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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