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simulate_upstream_ingest

Simulate an upstream system creating an issue. Generate test issues with custom severity, kind, and optional GitHub issue creation.

Instructions

Simulate an upstream system creating an issue (source=upstream).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNocode
titleYes
severityNomedium
descriptionNo
features_jsonNo{}
create_github_issueNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observedv0.1.0

TDQS

C2.3/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It says 'simulate' but does not clarify whether a real issue record is created, whether side effects occur (e.g., via create_github_issue parameter), or whether the operation is reversible. This leaves critical behavior ambiguous for a simulation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise, but it is underspecified for a tool with six parameters. It lacks structure or any breakdown of inputs, outputs, or side effects. It is more under-specified than concise, earning a low score.

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

Completeness1/5

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

The tool has six parameters, one required, and an output schema, yet the description provides no context about expected inputs, return values, or behavioral nuances. It does not explain what the simulated issue represents, how to interpret the output, or when this tool is appropriate. The description is grossly incomplete for the tool's complexity.

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

Parameters1/5

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

Schema description coverage is 0%, and the description mentions none of the six parameters (kind, title, severity, description, features_json, create_github_issue). The description provides no semantic meaning beyond the raw schema, failing to compensate for the lack of schema descriptions.

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 action ('simulate an upstream system creating an issue') and includes 'source=upstream', which distinguishes it from generic issue creation tools. However, it does not explicitly name sibling tools or contrast with create_issue, so it is clear but not fully differentiated.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives like create_issue or simulate_github_issue. It does not mention any prerequisites, exclusions, or conditions that would prompt an agent to select this tool. The context is implied but not stated.

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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