Skip to main content
Glama

get_api_test_runs

Page through an API test's recent run history — each run's outcome (SUCCESS, FAILURE, ERROR, MISSED), timing and any assertion failures. Use this to investigate why an API test is unhealthy. Optionally filter by time window and status.

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

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool supports pagination ('Page through'), includes outcome classifications, and provides filter capabilities. It does not cover auth, rate limits, or exact response shape, but for a list/read operation the disclosed behavior is reasonably rich.

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 two sentences, front-loaded with the verb and resource, and every phrase earns its place. It efficiently conveys purpose, content, use case, and optional filters without waste.

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?

The tool has moderate complexity with six parameters and no output schema. The description explains what data is returned (outcomes, timing, assertion failures), the use case, and optional filters. It lacks explicit ordering or pagination defaults, but those are present in the schema. Overall it is sufficient for an agent to invoke 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 description coverage is 100%, so the schema already documents all six parameters. The description adds value by summarizing that time window and status filtering exist, but it doesn't add syntax or details beyond what the schema provides. 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 uses a specific verb ('Page through') and resource ('API test's recent run history') and clearly lists the data returned (outcomes, timing, assertion failures). It distinguishes itself from sibling tools like get_api_test (single test) and list_api_tests (list tests) by focusing on 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?

The description explicitly states when to use the tool: 'Use this to investigate why an API test is unhealthy.' It provides clear context but does not explicitly mention when not to use it or name alternative tools, so it falls slightly short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Several tool pairs are near-duplicates, including three deprecated aliases (add_investigation_alert_channel vs add_alert_channel, list_investigation_alert_channels vs list_alert_channels, remove_investigation_alert_channel vs remove_alert_channel) that muddy the surface. Additionally, suppress_signal and create_ignore_rule both suppress alerting via different mechanisms, which could cause misselection despite detailed descriptions.

Naming Consistency4/5

The vast majority of tools follow a clear verb_noun snake_case pattern (create_api_test, list_issues, set_alert_rule_status). A few bare-noun tools (logs, spans, metrics) and the standalone verb correlate break the pattern slightly, but overall the naming is highly consistent and predictable.

Tool Count1/5

With 52 tools, this is on the extreme end of the calibration scale. Even accounting for the broad scope of an observability platform, the count is excessive and includes several deprecated redundancies that inflate it further.

Completeness5/5

The toolset provides comprehensive CRUD/lifecycle coverage across all major domains: alert rules (create, read, update, delete, status, delivery, preview), API tests (create, read, update, delete, run history, credentials), ignore rules and suppressions, issues with digest config, investigations with claim/read, channels, credentials, and rich query tools (logs, spans, metrics, SQL, traces, correlation). No obvious dead ends or missing core operations.

Resources