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OpsMCP

by tanmays0
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Github Create Issue

github_create_issue

Creates a GitHub issue with owner, repo, title, body, and labels. Defaults to dry-run validation; set dry_run=false only after explicit user confirmation to write remotely.

Instructions

Create a GitHub issue. Defaults to dry_run=True (no remote write).

Set dry_run=False only when the user explicitly confirms creation. Never returns credentials.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "owner": "<owner>",
      "repo": "<repo>",
      "title": "<title>"
    },
    "name": "github_create_issue"
  }
}

Input Schema

NameRequiredDescriptionDefault
bodyNo
repoYes
ownerYes
titleYes
labelsNo
dry_runNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries full behavioral disclosure. It reveals the dry_run default (no remote write) and the guarantee that credentials are never returned, which are important behavioral traits. However, it does not explain what happens on failure, whether the operation is idempotent, or what the successful response contains, leaving gaps for an agent to infer.

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 four short sentences, front-loaded with the core purpose, and every sentence adds meaningful content: the action, the dry_run default, the confirmation requirement, and the credential guarantee. There is no fluff or redundancy, making it highly concise and well-structured.

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 description is sufficient for a simple create operation. The output schema (present but not shown) likely covers return details, so the description need not explain them. It covers the critical safety behavior (dry_run) and credential handling. Missing error handling and edge cases, but given the existence of an output schema and the simplicity of the tool, it is reasonably complete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for parameter meaning. It adds value for the dry_run parameter by explaining its default and the condition for setting it false, but it says nothing about owner, repo, title, body, or labels. While some names are self-explanatory, the description does not fully cover the parameter semantics expected at 0% coverage.

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 action ('Create') and resource ('GitHub issue'), making it unambiguous and distinct from sibling tools like github_list_issues, which focuses on listing. The statement is direct and sufficient for an agent to understand what the tool does.

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 provides explicit usage guidance, particularly around the dry_run parameter: it defaults to true and instructs to set it false only upon explicit user confirmation. This is a clear when-to-use rule. While it does not explicitly name alternatives or when not to use this tool, the distinction from listing issues is obvious from the purpose statement.

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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