Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GauntletCI-MCP analyze the current commit for behavioral risk"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Model Context Protocol (MCP) server that exposes GauntletCI behavioral change risk analysis as tools for AI coding assistants (Claude, Copilot, Cursor, etc.).
Tool | Description |
| Run GauntletCI on HEAD, return findings as readable text |
| Run GauntletCI, return raw JSON for programmatic use |
| Run GauntletCI, return SARIF 2.1.0 report |
Related MCP server: biascope
Node.js 20+
GauntletCI CLI: dotnet tool install -g GauntletCI
npm install
npm run buildAdd to claude_desktop_config.json:
{
"mcpServers": {
"gauntletci": {
"command": "node",
"args": ["/path/to/GauntletCI-MCP/dist/index.js"]
}
}
}Add to .vscode/mcp.json:
{
"servers": {
"gauntletci": {
"type": "stdio",
"command": "node",
"args": ["${workspaceFolder}/path/to/GauntletCI-MCP/dist/index.js"]
}
}
}Once connected, ask your AI assistant:
"Run GauntletCI on this repo at /home/user/myapp and summarize the findings."
"Are there any block-level risks in the current commit?"
"Get the SARIF report for this commit."
Run GauntletCI behavioral change risk analysis on the current HEAD commit in a .NET repository. Returns findings as structured text.
| Name | Required | Description | Default |
|---|---|---|---|
| workingDirectory | Yes | Absolute path to the .NET repository root. | |
| sensitivity | No | Risk sensitivity filter. Default: balanced. | balanced |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It mentions the tool runs an analysis and returns structured text, but lacks details on side effects (e.g., whether it modifies the repository), required permissions, rate limits, or execution time. The return format 'structured text' is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences, front-loading the action and purpose. Every sentence adds value with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 parameters and no output schema, the description provides a high-level purpose but does not specify the structure of the returned text. Given the availability of sibling tools that return JSON/SARIF, the agent may need more detail on the output format to parse it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add any parameter-specific guidance beyond what the schema already provides for 'workingDirectory' and 'sensitivity'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action ('Run GauntletCI behavioral change risk analysis'), the scope ('on current HEAD commit in a .NET repository'), and the output ('structured text'). It distinguishes from siblings like get_findings_json and get_sarif which likely retrieve results, while this tool performs the analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies it is the first step before using sibling tools (get_findings_json, get_sarif) to retrieve specific formats, but it does not explicitly state when to use this tool versus alternatives or any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run GauntletCI and return the raw JSON result for programmatic processing.
| Name | Required | Description | Default |
|---|---|---|---|
| workingDirectory | Yes | Absolute path to the .NET repository root. | |
| sensitivity | No | balanced |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavioral traits. It states 'Run GauntletCI' but does not mention side effects, required permissions, rate limits, or that it may execute external commands.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of 11 words is concise. However, it lacks detail, bordering on under-specification. Front-loaded with action, but could be more informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and two parameters, the description should explain return format, installation requirements, or usage context. It only states 'raw JSON result' without specifics, making it incomplete for programmatic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50%; description adds no further parameter meaning. The workingDirectory is adequately described in schema, but sensitivity enum values are not explained. Description repeats minimal info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool runs GauntletCI and returns raw JSON for programmatic processing. The sibling tools analyze_commit and get_sarif imply different purposes, providing implicit differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. No mention of prerequisites, when not to use, or which scenarios are appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run GauntletCI and return a SARIF 2.1.0 report. Useful for feeding into tools that consume SARIF (GitHub Advanced Security, VS Code SARIF viewer, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| workingDirectory | Yes | Absolute path to the .NET repository root. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral traits. It discloses the action (running GauntletCI) and output format, but omits important details such as destructive potential, authorization needs, rate limits, or side effects. The agent is left uninformed about operational risks.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, highly concise, with the primary action and a practical use case front-loaded. No extraneous text or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (single required parameter, no output schema), the description adequately explains what it does and a common use case. However, missing behavioral context prevents a perfect score, as some agents may need hints about whether the tool is safe or requires permissions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the description does not add meaning beyond the input schema's definition of 'workingDirectory'. While the schema already describes the parameter, the description repeats no additional context, so it meets the baseline but does not exceed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the specific action: running GauntletCI and returning a SARIF 2.1.0 report. The verb 'run' and output format 'SARIF 2.1.0 report' provide a precise purpose. It distinguishes from sibling tools like 'analyze_commit' and 'get_findings_json' by focusing on SARIF output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Mentions usefulness for tools consuming SARIF (e.g., GitHub Advanced Security), which gives context but does not explicitly specify when to use this tool versus alternatives. No exclusions or when-not-to-use guidance are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The three tools all trigger the same GauntletCI analysis but return different outputs (structured text, JSON, SARIF). The descriptions clearly differentiate the output formats, making it easy for an agent to choose based on the desired result.
Naming is inconsistent: 'analyze_commit' uses a verb_noun pattern while 'get_findings_json' and 'get_sarif' use 'get_'. This mixed convention could cause confusion about the action of each tool.
Three tools is a lean but appropriate set for a focused CI analysis server. Each output format covers a distinct use case without unnecessary bloat.
The tool surface is incomplete: it only analyzes the current HEAD commit and lacks options for specifying a different commit, configuring analysis parameters, or retrieving previous analyses. This leaves significant gaps for users needing more control.
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