Skip to main content
Glama

search_incident_scenarios

Find production incident scenarios by symptoms, exit codes, or keywords to diagnose Kubernetes, AWS, Terraform, Linux, SRE, and CI/CD issues.

Instructions

Search 1,449+ real-world production incident scenarios across Kubernetes, AWS, Terraform, Linux, SRE, and CI/CD by symptoms, error codes (e.g. 137, 143, 502), or keywords.

Input Schema

NameRequiredDescriptionDefault
limitNoNumber of results to return (default: 5, max: 20)
queryYesThe symptom, exit code, error message, or technology (e.g., "exit code 137", "AWS VPC CNI exhausted", "DynamoDB state lock", "OOMKilled")
categoryNoOptional filter by domain: "Kubernetes", "AWS", "Terraform", "Linux & SRE", "CI/CD & GitOps", "Ansible", "Docker", "Database", "Security"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the broad dataset size and coverage domains, but it does not describe result format, ranking, pagination behavior, or how search results relate to runbook retrieval.

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 definition is a single, front-loaded sentence with no wasted text. The dataset scope comes first, followed by domains and searchable input types, which is exactly the right ordering.

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

Completeness3/5

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

The tool is simple and the schema is fully documented, which lowers the description burden. Still, with no output schema, the description should ideally say what a search result contains or how to follow up with get_scenario_runbook, and that information is missing.

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 input schema already documents query, category, and limit thoroughly. The description reinforces the query use cases and domains but adds little syntax or meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: search incident scenarios. It also names the domains and searchable dimensions (symptoms, error codes, keywords), making the tool's purpose clear, though it does not explicitly distinguish itself from get_scenario_runbook.

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

Usage Guidelines3/5

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

The description implies usage by listing the kinds of inputs to search with, such as symptoms, error codes, and keywords. However, it gives no explicit when-to-use guidance, no exclusions, and no routing advice relative to siblings like get_scenario_runbook or decode_error.

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