codeforge-mcp
Provides tools for interacting with Jira, including fetching, searching, creating, updating, and transitioning issues, as well as adding comments, linking issues, and attaching remote links via the Jira REST API.
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., "@codeforge-mcp review the auth service pull request"
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.
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 clean, scalable, production-ready code with tests, logging, and docs. |
| Scaffold a new Comviva Spring Boot microservice (pom, packaging, logging, Kafka, Consul, OpenAPI) per the standards in |
| Generate a JUnit 5 + Mockito test class for one Java class with |
| Review a pull request / diff with severity-tagged findings anchored to file:line. |
| Root-cause a bug from symptoms, stack trace, logs, and source. |
| Produce a publish-ready Root Cause Analysis report for an incident. |
| Fetch a Jira issue. |
| JQL search. |
| File a new issue (bug, story, task, epic). |
| Update fields on an issue. |
| Move an issue through workflow states. |
| Attach investigation notes, RCA, or implementation updates. |
| Link related issues (Blocks, Relates, Caused by, etc.). |
| Attach a PR / deployment / dashboard URL to an issue. |
| Propose a scalable system design for a goal. |
| Review an API / database / microservice / pipeline / cloud infra design. |
| Inventory technical debt and produce a refactor roadmap. |
| Validate a CI/CD pipeline config. |
| Review Kubernetes / Docker / Terraform / Helm / CloudFormation. |
| Pre-deploy go/no-go review with rollback and observability checks. |
| Tech design, API reference, runbook, README, or module overview. |
| Architecture Decision Record (MADR style). |
| Phased implementation plan with risks and definition of done. |
| 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 buildThis 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=MRTMCursor / 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
rewardservicethat consumes thereward.eventsKafka topic, persists aRewardLedgerentity to MySQL, and exposesGET /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:
A senior-engineer persona prelude (role, tone, standards).
The supplied inputs (the diff, PR title, etc.) embedded in a task description.
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
Create
src/tools/yourTool.tsexporting aToolDefinition: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...") }] }; }, };Register it in
src/tools/index.tsunder the right capability group.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 Bashsetup.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.batStandards 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.mdis searchable on npmjs.com. Make sure that's intended.
Maintainer — one-time setup:
Create a free account at https://www.npmjs.com/signup
Enable 2FA on the account (npm requires it for publishing).
Authenticate on this machine:
npm loginEnter 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 → 1.0.0prepublishOnly 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-mcpNo .npmrc or token required — it's public.
Getting updates:
npm install -g @anketpsingh/codeforge-mcp@latestPer-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:
Edit the file
Path A: commit, push, teammates
git pull && setup.batPath B: bump version,
npm run release:minor, teammatesnpm 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.exampleAvailable Tools
24 toolsanalyze_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_add_remote_linkC
Attach a remote link (e.g. PR URL, deployment URL, dashboard URL) to a Jira issue.
Inputs: url (required), title (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_link_issuesC
Link two Jira issues. Link types are project-defined; common ones: 'Relates', 'Blocks', 'is blocked by', 'Caused by', 'Duplicate'.
Inputs: inwardIssue (required), outwardIssue (required), linkType.
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
Scored across 24 tools
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.
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.
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.
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
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