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codeforge-mcp

CodeForge — an MCP (Model Context Protocol) server that exposes Senior Software Engineer / Technical Lead capabilities as individual tools. Any MCP-compatible client — Claude Desktop, Cursor, VS Code, Claude Code — can call these tools to delegate engineering tasks to a model playing a senior-engineer persona.

The server is prompt-mediated for reasoning tools: code generation, code review, RCA, etc. each return a structured prompt that locks the host LLM into the senior-engineer persona and a strict output schema. The reasoning happens in the client's LLM, so no second LLM API key is needed.

Jira tools call the Jira REST API directly.


Tools exposed

Tool

Purpose

generate_code

Generate clean, scalable, production-ready code with tests, logging, and docs.

scaffold_service

Scaffold a new Comviva Spring Boot microservice (pom, packaging, logging, Kafka, Consul, OpenAPI) per the standards in standards/comviva-springboot.md.

generate_test_cases

Generate a JUnit 5 + Mockito test class for one Java class with @DisplayName on every test, @Nested groups per method, AssertJ assertions, and Comviva copyright header. For project-wide tests, the host LLM iterates over source files and calls this per class.

review_code

Review a pull request / diff with severity-tagged findings anchored to file:line.

analyze_bug

Root-cause a bug from symptoms, stack trace, logs, and source.

generate_rca

Produce a publish-ready Root Cause Analysis report for an incident.

jira_get_issue

Fetch a Jira issue.

jira_search_issues

JQL search.

jira_create_issue

File a new issue (bug, story, task, epic).

jira_update_issue

Update fields on an issue.

jira_transition_issue

Move an issue through workflow states.

jira_add_comment

Attach investigation notes, RCA, or implementation updates.

jira_link_issues

Link related issues (Blocks, Relates, Caused by, etc.).

jira_add_remote_link

Attach a PR / deployment / dashboard URL to an issue.

recommend_architecture

Propose a scalable system design for a goal.

review_architecture

Review an API / database / microservice / pipeline / cloud infra design.

identify_tech_debt

Inventory technical debt and produce a refactor roadmap.

review_cicd_pipeline

Validate a CI/CD pipeline config.

review_infra_config

Review Kubernetes / Docker / Terraform / Helm / CloudFormation.

deployment_readiness_check

Pre-deploy go/no-go review with rollback and observability checks.

generate_documentation

Tech design, API reference, runbook, README, or module overview.

generate_adr

Architecture Decision Record (MADR style).

generate_implementation_plan

Phased implementation plan with risks and definition of done.

generate_migration_strategy

Migration strategy with stages, cutover, and rollback.


Related MCP server: Vibe Coder MCP Server

Install & build

cd D:/eclipse-workspace/MRTM_7.12/mrtm/codeforge-mcp
npm install
npm run build

This produces dist/index.js.


Configure environment

Jira tools require credentials. Copy .env.example to .env and fill in:

JIRA_HOST=https://your-company.atlassian.net
JIRA_EMAIL=you@company.com
JIRA_API_TOKEN=<API token from id.atlassian.com/manage-profile/security/api-tokens>
JIRA_DEFAULT_PROJECT=MRTM

.env is not loaded automatically — pass these as real environment variables when the MCP client launches the server (see the next section). Tools that don't touch Jira work without these variables.


Register with an MCP client

Claude Desktop

Edit %APPDATA%\Claude\claude_desktop_config.json (Windows) and add:

{
  "mcpServers": {
    "codeforge": {
      "command": "node",
      "args": ["D:/eclipse-workspace/MRTM_7.12/mrtm/codeforge-mcp/dist/index.js"],
      "env": {
        "JIRA_HOST": "https://your-company.atlassian.net",
        "JIRA_EMAIL": "you@company.com",
        "JIRA_API_TOKEN": "<token>",
        "JIRA_DEFAULT_PROJECT": "MRTM"
      }
    }
  }
}

Restart Claude Desktop. The tools will appear in the tool picker.

Claude Code (CLI)

claude mcp add codeforge node "D:/eclipse-workspace/MRTM_7.12/mrtm/codeforge-mcp/dist/index.js" \
  -e JIRA_HOST=https://your-company.atlassian.net \
  -e JIRA_EMAIL=you@company.com \
  -e JIRA_API_TOKEN=... \
  -e JIRA_DEFAULT_PROJECT=MRTM

Cursor / VS Code (with MCP extension)

Add an entry in the client's MCP settings pointing to the same node dist/index.js command with the env vars above.


Company standards (used by scaffold_service)

The conventions for new microservices live in standards/comviva-springboot.md — packaging, copyright header, constructor injection, @Slf4j parameterized logging, Spring Kafka config pattern, Consul config layout, error handling, JPA, scheduling, Dockerfile. Edit that file to refine conventions; scaffold_service re-reads it on every call (no rebuild needed).

Example invocation from Claude

Scaffold a new service called rewardservice that consumes the reward.events Kafka topic, persists a RewardLedger entity to MySQL, and exposes GET /rewards/{customerId} to read balances.

The MCP client picks scaffold_service, fills in the args, and the host LLM produces the full project — pom.xml, RewardserviceApplication.java, config/KafkaConfig.java, service/RewardEventConsumer.java, dao/RewardLedgerRepository.java, model/RewardLedger.java, controller/RewardController.java, application.yml, bootstrap.yml, consul-prop.yml, logback-spring.xml, Dockerfile, and tests — all carrying the Comviva copyright header and following every rule in the standards file.


How the prompt-only tools work

When you call review_code, the server doesn't itself invoke an LLM. It returns a single text block containing:

  1. A senior-engineer persona prelude (role, tone, standards).

  2. The supplied inputs (the diff, PR title, etc.) embedded in a task description.

  3. A strict output contract — the exact sections, severity scale, table columns, and rules the LLM must follow.

The host LLM (Claude Desktop, Cursor, etc.) then executes that prompt as its next reasoning step. Because the contract is strict and the persona is consistent across tools, you get reproducible, high-quality output without standing up a second LLM endpoint.

If you later want the server to call an LLM itself (e.g. for batch jobs without a host UI), wire an Anthropic client into each tool's handler and post the existing prompt body to it. The current architecture is designed to make that swap trivial.


Adding a new tool

  1. Create src/tools/yourTool.ts exporting a ToolDefinition:

    import { z } from "zod";
    import { withPersona } from "../prompts/persona.js";
    import type { ToolDefinition } from "./types.js";
    
    const Schema = z.object({ /* inputs */ });
    
    export const yourTool: ToolDefinition = {
      name: "your_tool",
      description: "What it does, in one sentence.",
      inputSchema: Schema,
      handler: async (raw) => {
        const input = Schema.parse(raw);
        return { content: [{ type: "text", text: withPersona("...prompt body...") }] };
      },
    };
  2. Register it in src/tools/index.ts under the right capability group.

  3. npm run build. The client picks up the new tool on next restart.


Sharing with the team

Two distribution paths — start with A, promote to B once the standards stabilise.

A. Git clone

Maintainer (one-time): push this folder to GitHub.

Each teammate (one-time):

git clone https://github.com/ThakurAnketPratapSingh/codeforge-mcp.git
cd codeforge-mcp
setup.bat      # Windows
./setup.sh     # macOS / Linux / Git Bash

setup.bat / setup.sh installs deps, builds, and runs claude mcp add codeforge ... automatically. Teammates add their own Jira creds afterwards (re-register with -e JIRA_* flags — see the setup script output).

Getting updates:

git pull && setup.bat

Standards live in standards/comviva-springboot.md, versioned with the repo, so git pull is how new conventions reach every teammate.

B. Public npm (npmjs.com)

The package is published to the public npm registry as @anketpsingh/codeforge-mcp. Anyone with Node.js installed can grab it — no token needed.

⚠️ Public publication means standards/comviva-springboot.md is searchable on npmjs.com. Make sure that's intended.

Maintainer — one-time setup:

  1. Create a free account at https://www.npmjs.com/signup

  2. Enable 2FA on the account (npm requires it for publishing).

  3. Authenticate on this machine:

    npm login

    Enter username, password, email, and the OTP from your authenticator.

Maintainer — each release:

npm run release:patch   # 0.1.0 → 0.1.1, builds, publishes
npm run release:minor   # 0.1.0 → 0.2.0
npm run release:major   # 0.1.0 → 1.0.0

prepublishOnly rebuilds from clean automatically. Only dist/, standards/, README.md, and .env.example ship.

Each teammate — one-time:

npm install -g @anketpsingh/codeforge-mcp
claude mcp add codeforge codeforge-mcp

No .npmrc or token required — it's public.

Getting updates:

npm install -g @anketpsingh/codeforge-mcp@latest

Per-user secrets (both paths)

Jira credentials are per-user. Each teammate adds their own via claude mcp add ... -e JIRA_EMAIL=... -e JIRA_API_TOKEN=... after the base install. Shared values (JIRA_HOST, JIRA_DEFAULT_PROJECT) go in the README so everyone copies them verbatim.

Evolving the standards

standards/comviva-springboot.md is the single source of truth. To change a convention:

  1. Edit the file

  2. Path A: commit, push, teammates git pull && setup.bat

  3. Path B: bump version, npm run release:minor, teammates npm install -g @anketpsingh/codeforge-mcp@latest

Standards changes are visible in git log standards/ — useful when investigating "why does the generator do X now".


Project layout

codeforge-mcp/
├── src/
│   ├── index.ts              # stdio entry point
│   ├── server.ts             # MCP server wiring
│   ├── prompts/persona.ts    # shared senior-engineer persona
│   ├── jira/client.ts        # Jira REST client (Cloud v3 / ADF)
│   └── tools/
│       ├── types.ts          # ToolDefinition shape, error helper
│       ├── index.ts          # tool registry
│       ├── codeGeneration.ts
│       ├── codeReview.ts
│       ├── bugAnalysis.ts
│       ├── rca.ts
│       ├── jira.ts
│       ├── architecture.ts
│       ├── devops.ts
│       └── documentation.ts
├── package.json
├── tsconfig.json
└── .env.example

Available Tools

24 tools
analyze_bugB

Analyze symptoms, stack traces, logs, and source to find the root cause of a bug. Returns a structured prompt that yields a hypothesis with evidence, severity, priority, recommended fix, and prevention actions.

Inputs: symptom (required), logs, stackTrace, environment, +1 more.

deployment_readiness_checkA

Perform a pre-deployment readiness check. Returns a structured prompt assessing risk, validating rollback, observability, and go/no-go gates.

Inputs: changeSummary (required), riskLevel, blastRadius, observability, +1 more.

generate_adrA

Generate an Architecture Decision Record (ADR) in the MADR-style format. Returns a structured prompt the host LLM completes into a fully-formed ADR.

Inputs: title (required), context (required), options (required), decision (required), +2 more.

generate_codeB

Generate clean, scalable, production-ready code for a feature. Returns a structured prompt the host LLM executes to produce code, tests, logging, error handling, and docs following industry best practices.

Inputs: feature (required), language (required), context, constraints, +2 more.

generate_documentationA

Generate technical documentation — design doc, API reference, runbook, README, or module overview — from supplied source material. Returns a structured prompt the host LLM completes into the document.

Inputs: docType (required), subject (required), sourceMaterial (required), audience.

generate_implementation_planB

Generate a phased implementation plan for a feature or change. Returns a structured prompt yielding milestones, dependencies, risks, and a definition of done.

Inputs: feature (required), context, constraints, knownUnknowns.

generate_migration_strategyB

Generate a migration strategy from one technology / version / pattern to another. Returns a structured prompt yielding stages, dual-write/dual-read plans, validation, cutover, and rollback.

Inputs: to (required), from (required), scope (required), constraints.

generate_rcaA

Generate a detailed Root Cause Analysis report for a production incident. Returns a structured prompt the host LLM completes into a publish-ready RCA document with summary, timeline, root cause, impact, resolution, corrective actions, preventive measures, and lessons learned.

Inputs: incidentTitle (required), impact, timeline, rootCause, +5 more.

generate_test_casesA

Generate a complete JUnit 5 + Mockito test class for one Java class, with @DisplayName annotations on every test class, nested group, and test method for readable test reports. Follows Comviva standards (copyright header, package layout). Call once per class; for 'tests for all classes in the project', the host LLM should glob the sources and invoke this tool per file.

Inputs: className (required), sourceCode (required), useAssertJ, coverageTargets, +2 more.

identify_tech_debtB

Identify technical debt in a codebase and propose a refactoring strategy. Returns a structured prompt covering debt inventory, business impact, refactor roadmap, and quick wins.

Inputs: codebaseSummary (required), hotspots, symptoms.

jira_add_commentB

Add a comment to a Jira issue. Use to attach investigation notes, RCA summaries, or implementation updates.

Inputs: comment (required), issueKey (required).

jira_create_issueB

Create a Jira issue. Useful for filing bugs after analysis, opening tracking tickets for RCA action items, or creating stories from implementation plans.

Inputs: summary (required), labels, priority, issueType, +4 more.

jira_get_issueC

Fetch a Jira issue by key.

Inputs: issueKey (required), fields.

jira_search_issuesC

Search Jira issues with a JQL expression.

Inputs: jql (required), fields, maxResults.

jira_transition_issueB

Transition a Jira issue to a new status (e.g. 'In Progress', 'Done'). Optionally posts a comment with the transition.

Inputs: issueKey (required), transitionName (required), comment.

jira_update_issueC

Update a Jira issue's fields.

Inputs: issueKey (required), labels, summary, priority, +3 more.

recommend_architectureA

Recommend a scalable system design for a stated goal. Returns a structured prompt covering architecture decisions, component diagram-as-text, data flow, scaling strategy, failure modes, and an explicit decision log.

Inputs: goal (required), cloud, currentState, nonFunctional, +1 more.

review_architectureB

Review an existing architecture (API, database, microservice, event pipeline, or cloud infra) for scalability, reliability, security, and operability. Returns a structured prompt that yields graded findings and prioritized remediation.

Inputs: description (required), componentType (required), concerns.

review_cicd_pipelineA

Validate a CI/CD pipeline configuration for correctness, security, efficiency, and best practices. Returns a structured prompt yielding severity-tagged findings and recommended fixes.

Inputs: pipelineType (required), pipelineConfig (required), context.

review_codeA

Review a pull request or diff. Returns a structured prompt that yields severity-tagged, file:line-anchored findings across correctness, security, performance, maintainability, and tests.

Inputs: diffOrCode (required), prTitle, language, focusAreas, +1 more.

review_infra_configA

Review Kubernetes, Docker, Terraform, Helm, or CloudFormation configuration for correctness, security, reliability, and cost. Returns a structured prompt with anchored findings and remediation.

Inputs: config (required), configType (required), targetEnv.

scaffold_serviceB

Scaffold a new Comviva Spring Boot microservice following company standards (packaging, logging, Kafka, Consul config, error handling, OpenAPI). Returns a structured prompt that yields a complete file tree with full source for pom.xml, the application class, config beans, controllers, producers/consumers, entities, application.yml/bootstrap.yml, logback-spring.xml, Dockerfile, and tests.

Inputs: description (required), serviceName (required), capabilities (required), port, +5 more.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    B3.1/5.0

    Scored across 24 tools

    Disambiguation4/5

    Most tools are clearly distinct (generate_code vs review_code vs generate_rca). Minor overlap exists between review_architecture and recommend_architecture, and between analyze_bug and generate_rca, but descriptions help differentiate. Overall, an agent can select the right tool with minimal confusion.

    Naming Consistency4/5

    Majority of tools follow a verb_noun pattern (generate_code, review_code, jira_*). The jira_ prefix adds consistency for that subgroup. Minor deviations like jira_add_remote_link (verb_noun_noun) are acceptable. No chaotic mixing; overall predictable.

    Tool Count3/5

    24 tools is on the heavy side for a single server, especially since many are wrappers that return structured prompts rather than direct actions. While each tool has a clear purpose, the count feels excessive; consolidation or grouping (e.g., separate jira tools) would improve usability.

    Completeness3/5

    The toolset covers a wide range of development lifecycle activities (code generation, review, architecture, bug analysis, Jira integration, CI/CD, infra, documentation). However, it lacks tools for direct code manipulation (e.g., apply_patch, create_file) and for some domains like database schema generation or performance profiling. Notable gaps exist but core workflows are present.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

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