brd-enhancer-mcp
This server enhances development tasks by searching project documentation and returning enriched prompts with relevant context.
Enhance a task or query: Submit a task description and receive an enriched prompt with relevant background information, requirements, or architecture context from your project's documentation.
Project-specific lookups: Optionally specify a
project_idto target a specific project's docs, or fall back to thePROJECT_IDenvironment variable as the default.Remote API-backed retrieval: Performs a standalone remote lookup against a configured backend API, returning documentation-grounded context directly.
Workflow integration: Designed to reduce manual doc searching — after receiving the enhanced result, you can review it and proceed with further instructions.
Generates and submits Gherkin test cases from Confluence content.
Click on "Deploy 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., "@brd-enhancer-mcpEnhance the task: Implement the user authentication flow"
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.
brd-enhancer-mcp
A package that ships three MCP servers for an AI-assisted SDLC workflow:
Server | Executable | Purpose |
enhance-prompt |
| Enhance dev tasks with project documentation context |
test-workflow |
| Generate & submit Gherkin test cases from Confluence |
pipeline-analyzer |
| Analyze Harness pipeline failures with RAG context from past incidents |
Prerequisites
⚠️ Use Official Python (NOT Microsoft Store Python)
Download Python from https://www.python.org/downloads/
During installation, make sure to check:
✅ "Add Python to PATH"
Verify you have the correct Python:
where pythonOutput | Status |
| ✅ Official Python — Good |
| ❌ Microsoft Store Python — Reinstall from python.org |
Related MCP server: Context7 MCP
Installation
Option A — Global Install (Recommended for most developers)
pip install git+https://github.com/arushsingh17/mcp.gitVerify installation:
pip show brd-enhancer-mcp
where brd-enhancer-mcp # Windows
which brd-enhancer-mcp # Mac/LinuxOption B — Virtual Environment Install
# Step 1: Create and activate a virtual environment
python -m venv venv
venv\Scripts\activate # Windows
source venv/bin/activate # Mac/Linux
# Step 2: Install the package
pip install git+https://github.com/arushsingh17/mcp.git
# Step 3: Get the exact executable path (needed for config)
python -c "import shutil; print(shutil.which('brd-enhancer-mcp'))"Example output:
C:\Users\YourName\Desktop\myproject\venv\Scripts\brd-enhancer-mcp.exeCopy this path — you will need it in the config below.
Configuration
🌍 Global Install Config
Since brd-enhancer-mcp is registered in system PATH, no file path is needed.
Claude Desktop → %APPDATA%\Claude\claude_desktop_config.json (Windows)
Claude Desktop → ~/Library/Application Support/Claude/claude_desktop_config.json (Mac)
Claude Code → ~/.claude.json
{
"mcpServers": {
"brd-enhancer": {
"command": "brd-enhancer-mcp",
"env": {
"API_KEY": "your_api_key",
"PROJECT_ID": "your_project_id",
"API_URL": "https://your-backend.com"
}
}
}
}📦 Virtual Environment Config
Use the full path you got from the command above.
⚠️ On Windows replace every
\with\\in the path
{
"mcpServers": {
"brd-enhancer": {
"command": "C:\\Users\\YourName\\Desktop\\myproject\\venv\\Scripts\\brd-enhancer-mcp.exe",
"env": {
"API_KEY": "your_api_key",
"PROJECT_ID": "your_project_id",
"API_URL": "https://your-backend.com"
}
}
}
}Mac/Linux venv config:
{
"mcpServers": {
"brd-enhancer": {
"command": "/Users/yourname/myproject/venv/bin/brd-enhancer-mcp",
"env": {
"API_KEY": "your_api_key",
"PROJECT_ID": "your_project_id",
"API_URL": "https://your-backend.com"
}
}
}
}Environment Variables
Shared (all servers)
Variable | Required | Default | Description |
| ✅ Yes | — | Your personal backend API key |
| ✅ Yes | — | Your project ID/GUID |
| ❌ No |
| Backend URL |
pipeline-analyzer-mcp only
Variable | Required | Default | Description |
| ✅ Yes | — | Harness personal access token ( |
| ✅ Yes | — | Harness account identifier |
| ✅ Yes | — | Harness project identifier |
| ❌ No |
| Harness organization identifier |
| ❌ No |
| Harness base URL (change for self-hosted) |
pipeline-analyzer example config
{
"mcpServers": {
"pipeline-analyzer": {
"command": "pipeline-analyzer-mcp",
"env": {
"API_URL": "https://your-backend.com",
"API_KEY": "your_backend_api_key",
"PROJECT_ID": "your_project_id",
"HARNESS_API_KEY": "pat.ACCOUNT.TOKEN_ID.SECRET",
"HARNESS_ACCOUNT_ID": "your_harness_account_id",
"HARNESS_ORG_ID": "default",
"HARNESS_PROJECT_ID": "your_harness_project_id",
"HARNESS_BASE_URL": "https://app.harness.io"
}
}
}
}Quick Reference
Scenario | Command in config |
Global install (Official Python) |
|
Global install (Microsoft Store Python) | Full path from |
Virtual environment (any OS) | Full path from |
Available Tools
1 toolenhance_taskA
Search project documentation and return an enhanced prompt with relevant context. Use this to get background info, requirements, or architecture context for a task.
This is a standalone remote lookup — it queries an API and returns results directly.
After receiving the result, display it to the user and wait for their instructions.
Args: task: The task or query to enhance project_id: Optional project ID/GUID. Defaults to PROJECT_ID env var.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| project_id | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the execution model ('standalone remote lookup,' 'queries an API'), but lacks critical safety context such as whether the operation is read-only, idempotent, or requires specific authorization. It omits potential side effects or rate limits.
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 is well-structured with the core purpose front-loaded, followed by usage context, behavioral notes, workflow instructions, and parameter definitions. The Args section is clear and necessary given the schema's lack of descriptions. No extraneous information is present.
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 simple schema (2 string parameters, 1 required) and presence of an output schema, the description is sufficiently complete. It covers parameter semantics inline, explains the API interaction model, and provides post-invocation workflow guidance, adequately compensating for missing annotations.
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?
With 0% schema description coverage, the description compensates effectively by documenting both parameters in the Args section: 'task: The task or query to enhance' and 'project_id: Optional project ID/GUID. Defaults to PROJECT_ID env var.' It adds semantic meaning and default behavior explanations absent from the schema.
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 the tool 'Search[es] project documentation and return[s] an enhanced prompt with relevant context,' providing a specific verb (search), resource (project documentation), and outcome (enhanced prompt). It further clarifies scope with examples like 'background info, requirements, or architecture context.'
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 provides clear contextual guidance ('Use this to get background info... for a task') and explicit workflow instructions ('After receiving the result, display it to the user and wait for their instructions'). While no alternatives are named (no siblings exist), the when-to-use guidance is direct and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.3.0- First observed
enhance_task
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'enhance_task' has a clearly defined and distinct purpose of searching documentation to enhance prompts with context.
A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare against. The name 'enhance_task' follows a clear verb_noun pattern that is appropriate for its function.
One tool is too few for a server named 'brd-enhancer-mcp', which suggests a broader scope of enhancing tasks or documents. A single lookup tool feels thin and incomplete for what could involve multiple enhancement-related operations.
The tool surface is severely incomplete for an enhancement domain. While 'enhance_task' provides context lookup, there are obvious gaps such as creating, updating, or managing enhanced tasks or documents, leaving agents with limited functionality.
Maintenance
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