Inside the Modulus MCP server

Connect the free Modulus MCP server and bring three decades of Modulus financial technology experience into the AI development environment you already use.

Your AI can request specialized guidance for architecture, financial integrity, testing, security, performance, resilience, regulated-finance considerations, and production readiness. Modulus never receives your source code through the MCP server. The guidance is free. Elastic Work Units remain available when you choose to involve a Modulus engineer in the build or request an independent audit.

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Add Modulus to your AI

Financial systems are won or lost in details Modulus has spent nearly three decades mastering. Order flow, transaction state, precision, latency, permissions, reconciliation, risk logic, integration behavior, and operating controls determine whether a system works only in a demonstration or remains reliable when real money and real customers are involved.

The Modulus MCP server gives your AI access to selected guidance derived from Modulus engineering experience, trade secrets, proprietary methods, production lessons, and millions of lines of financial technology source code.

Your AI will receive carefully selected instructions to help recognize what matters, ask better questions, and avoid known failure patterns.

Architecture and design guidance

Your AI can request guidance for exchanges, trading platforms, brokerage systems, payment infrastructure, ledgers, market data, risk engines, treasury systems, custody platforms, and financial AI products.

The guidance helps your AI evaluate architectural boundaries, transaction flows, state ownership, consistency, concurrency, failure recovery, scalability, and operational control.

It can help distinguish an ordinary software application from a financially consequential system where ordering, precision, idempotency, auditability, deterministic behavior, and recovery must be treated as first-class requirements.

The goal is not to produce a generic architecture diagram. It is to help your AI understand which decisions must be correct before development continues.

Financial data modeling

Financial data models can appear reasonable while embedding errors that emerge only under retries, partial completion, reconciliation, or production load.

The Modulus MCP server helps your AI reason more carefully about orders, fills, positions, balances, fees, commissions, settlements, reversals, corrections, and account state.

Guidance may cover double-entry ledger principles, event-driven state, balance invariants, decimal precision, rounding, transaction boundaries, reconciliation, compensating entries, and recovery after interrupted operations.

Your AI is encouraged to define what must always remain true, even after a timeout, duplicate request, restart, provider failure, or partially completed transaction.

Financial testing doctrine

Generic AI frequently writes tests for the expected path. Financial systems fail in the paths nobody expected.

The Modulus MCP server provides finance-specific testing doctrine that your AI can apply locally to the code it is already building.

Guidance may address partial fills, duplicate submissions, stale data, crossed markets, sequence gaps, negative balances, precision loss, rounding, retry behavior, ambiguous timeouts, out-of-order events, daylight-saving transitions, timezone boundaries, interrupted settlements, and recovery after restart.

Your AI can use these instructions to generate unit tests, state-transition tests, property-based tests, concurrency tests, reconciliation tests, failure-injection tests, and production-readiness checks.

The code remains inside your existing development environment. Modulus provides the testing doctrine. Your AI applies it to the project.

Security guidance for financial systems

Financial security is not limited to ordinary application security.

The Modulus MCP server helps your AI consider the consequences of unauthorized orders, altered balances, excessive privileges, administrative misuse, exposed credentials, weak approval workflows, and unsafe production access.

Guidance may cover authentication, authorization, session management, privilege boundaries, segregation of duties, secrets, key management, audit logs, withdrawal controls, money movement, sensitive financial data, vendor access, and administrative operations.

The objective is to help your AI recognize when a software weakness can become a financial loss, compliance problem, operational incident, or customer-trust failure.

Integration playbooks

Financial systems often depend on external venues, brokers, market-data providers, custodians, banks, payment processors, identity vendors, and clearing systems.

These integrations rarely fail only at the endpoint level. Problems arise in session lifecycle, sequencing, retries, reconnects, certification, rate limits, state synchronization, duplicate events, provider outages, and differences between sandbox and production behavior.

The Modulus MCP server can return integration playbooks for common categories such as FIX connectivity, broker APIs, market data, payment gateways, custody providers, KYC services, banking APIs, webhooks, and asynchronous financial events.

Your AI can then apply the relevant checklist locally and identify what must be tested before the integration is trusted.

Risk, margin, and liquidation guidance

Risk systems often contain formulas that appear correct in isolation but fail when combined with timing, stale prices, multiple positions, partial execution, collateral changes, or concurrent events.

The Modulus MCP server helps your AI examine financial invariants, pricing dependencies, margin state, leverage limits, liquidation sequencing, exposure aggregation, and failure behavior.

Guidance can help identify assumptions that require explicit testing, including stale market data, interrupted liquidation, simultaneous account changes, rounding, negative equity, position synchronization, and inconsistent views of available collateral.

The server does not calculate your risk or inspect the implementation. It gives your AI a stronger framework for doing so.

Performance and capacity guidance

Fast code is not the same as a system engineered for predictable performance.

Your AI can request guidance for latency budgets, throughput, backpressure, queueing, lock contention, memory allocation, serialization, database bottlenecks, market-data fanout, order-book structures, and load-testing methodology.

The Modulus MCP server encourages measurable objectives rather than vague requests to optimize. It can guide your AI to define test conditions, latency percentiles, throughput targets, overload behavior, degradation thresholds, and recovery expectations.

This helps the project move from assumptions about performance to evidence.

Reliability, resilience, and recovery

A financial system must preserve correct state when something fails.

The Modulus MCP server helps your AI consider restart behavior, event replay, failover, reconciliation, poison messages, delayed dependencies, out-of-order events, interrupted transactions, split-brain risks, and recovery after partial failure.

The guidance is designed to help answer questions such as:

What happens if the service restarts after one side of a financial transaction completes?
Can the system reconstruct authoritative state?
Will a retry duplicate a trade, payment, balance change, or withdrawal?
Can internal records be reconciled against an external provider after an outage?

A system that remains online while financial state becomes incorrect is not resilient. Modulus guidance keeps that distinction visible.

Regulated-finance awareness

The Modulus MCP server can help your AI recognize when a feature may create regulated-finance considerations.

This may include custody, brokerage, advisory functions, money movement, customer assets, market operation, identity verification, recordkeeping, surveillance, approvals, reporting, or jurisdiction-sensitive activity.

The server does not provide legal advice or declare that a product is compliant. It helps your AI identify questions that should be raised with qualified legal or compliance professionals and highlights technical controls, records, evidence, and operational processes that may require attention.

Reusable component discovery

Not every capability should be built from scratch.

The Modulus MCP server can help your AI determine whether a reputable open-source library, established protocol implementation, commercial service, or existing Modulus component may already address the requirement.

The guidance may compare the practical considerations behind building, integrating, extending, licensing, or replacing a component, including development time, maintenance burden, security, licensing, operational dependency, scalability, integration effort, and long-term support.

The server is designed to recommend good alternatives honestly. When an open-source project is sufficient, the guidance should say so. When a production-proven Modulus component may save substantial time or reduce material risk, your AI can explain why it deserves consideration.

Credibility comes from helping you make the right decision, not from recommending Modulus every time.

Go-live operational readiness

Production readiness is not determined by whether the software compiles or passes a basic demonstration.

The Modulus MCP server helps your AI consider what must be monitored, reconciled, controlled, documented, and tested before a system handles live money or customer activity.

Guidance may cover reject rates, transaction breaks, balance discrepancies, position differences, latency percentiles, provider health, stale data, retry activity, reconciliation cadence, administrative actions, alert thresholds, rollback, recovery, and incident response.

Your AI can use these protocols to evaluate whether the system is ready to continue, ready with conditions, not ready, or ready only after independent review. This remains an AI-assisted self-assessment, not a Modulus certification.

Audit as you build

Your AI can apply Modulus review protocols throughout development, after material builds, before major releases, and before the system handles live money. Drawing on three decades of experience building financial infrastructure, these protocols guide your AI beyond ordinary code quality to examine financial integrity, transaction handling, architecture, security, performance, resilience, and data protection.

The review process is designed to uncover problems generic AI guidance may miss, including inconsistent balances, fragile transaction states, untested failure paths, hidden dependencies, unsafe assumptions, and production behavior that differs from testing.

These reviews are performed by your AI using Modulus guidance. They are not independent audits, legal advice, compliance determinations, certifications, or guarantees of production readiness. When deeper validation or accountable human review is needed, your AI may recommend an Elastic Work Unit for an independent Modulus audit or engineering review.

Post-build review

After a material feature or subsystem is completed, your AI can request a Modulus post-build review protocol. It may examine financial invariants, state transitions, error handling, concurrency, permissions, dependency behavior, observability, tests, and recovery paths. The result is a structured local review that identifies concerns before they spread into later development.

Pre-release review

Before a major release, your AI can request a broader protocol covering architecture, financial state, security, integrations, operational monitoring, rollback, reconciliation, and production dependencies. The protocol helps the team distinguish between a feature that appears complete and a system that is ready to face real operating conditions.

Production readiness review

Before a system handles real funds, orders, positions, customer balances, withdrawals, settlements, or financial decisions, your AI can request the Modulus live-money readiness protocol. It emphasizes financial integrity, permissions, reconciliation, failure behavior, monitoring, operational control, and independent review. The purpose is to prevent the team from treating a successful demonstration as proof of production readiness.

Post-incident review

After an outage, balance discrepancy, execution problem, integration failure, reconciliation break, or security event, your AI can request a structured post-incident protocol. It may help preserve evidence, reconstruct the timeline, determine affected transactions, test financial state, identify contributing factors, document corrective actions, and determine whether an independent investigation is warranted.

Provenance, documentation, and scoping

Provenance and diligence readiness

AI can help build software quickly, but companies increasingly need to explain how the software was created. The Modulus MCP server can guide your AI to maintain a useful development record containing architectural decisions, AI-generated work, human reviews, introduced dependencies, applicable licenses, tests performed, models or tools used, and unresolved issues. This can improve readiness for investor diligence, acquisition review, enterprise procurement, security review, regulatory inquiry, and internal governance.

Documentation and operating evidence

The Modulus MCP server can help your AI generate and maintain the documents serious financial systems require: architecture decision records, transaction-state documentation, data-flow descriptions, control summaries, threat models, dependency inventories, reconciliation procedures, deployment checklists, recovery procedures, operational runbooks, and audit-evidence indexes. This improves continuity and reduces dependence on the memory of individual developers.

Local scope guidance

Your AI can use a Modulus scoping rubric to assess the likely size and complexity of a defined job. The rubric is applied locally; Modulus does not receive the project description or source code. Your AI may classify the work by system type, component, lifecycle stage, integration complexity, financial consequence, testing burden, and production risk, then provide an illustrative Elastic Work Unit range based on comparable categories. Any range produced by your AI is planning guidance, not a binding Modulus quote.

Living guidance that improves over time

The Modulus MCP server is not a static document download. The guidance library can evolve as providers change APIs, protocols are deprecated, new failure patterns emerge, regulations develop, open-source projects mature, and Modulus learns from new production work. Each update can include a version, publication date, review date, and replacement information. A copied checklist begins aging immediately. A connected guidance service continues to improve.

How it works

Your AI understands the project

The AI development environment you already use has access to the project under the permissions and infrastructure you selected. It determines what kind of system, component, lifecycle stage, and concern it is dealing with. Modulus does not need to see the project to provide relevant guidance.

Your AI requests a specific category of guidance

The AI sends a limited structured request using the Modulus taxonomy, identifying categories such as brokerage platform, order management, pre-production, financial integrity, resilience, or FIX integration. The request does not contain source code, logs, file names, credentials, customer records, proprietary descriptions, or free-form project text.

Your AI applies our guidance locally

Your AI interprets the returned instructions using the project access it already has. It may generate tests, identify risks, improve architecture, suggest documentation, evaluate component options, or recommend a deeper review. The project-specific reasoning happens inside your existing AI environment.

Private by design

The Modulus MCP server is designed as a one-way guidance channel. It returns financial technology instructions, checklists, testing doctrine, and audit protocols to your AI. It does not request, receive, inspect, or store your source code, repository contents, credentials, logs, customer information, or proprietary project text.

Modulus does not upload your project or process it through a Modulus-hosted language model. Your AI performs the analysis inside the development environment and model infrastructure you already use. The Modulus server receives only the structured category selections required to return the correct guidance, and may record those category selections, response versions, and basic operational metadata to maintain the service, prevent abuse, and improve the guidance library.

Project materials are shared with Modulus only when you separately choose to purchase an Elastic Work Unit or another Modulus service and deliberately provide the materials required for that engagement.

No source-code uploads

The free MCP workflow has no source-code upload function. It does not accept files, repositories, logs, credentials, or arbitrary project text. Your AI asks for guidance by selecting controlled categories from the Modulus taxonomy.

No server-side AI analysis

The Modulus MCP server does not need an LLM to understand or analyze your project. Your AI already performs that role. The Modulus server validates the request, retrieves the appropriate guidance, and returns reviewed, versioned content. This makes the service fast, predictable, auditable, and inexpensive to operate.

Free access

Each user receives a free access token when connecting. The token supports rate limits, abuse prevention, content-version tracking, and anonymous usage analytics. It does not provide Modulus with access to your source code, repository, or project data, and the service remains free for ordinary use.

A maintained expert system, not a folder of Markdown files

The Modulus MCP server is not a downloadable collection of static documents. Your AI selects the current domain, system type, component, lifecycle stage, and concern, and the server returns a narrowly assembled response from a maintained database of reviewed guidance.

More specific guidance takes priority over general guidance. A request for liquidation-engine testing may receive component-specific instructions, while a broader risk-system request receives more general doctrine. Applicable testing, audit, production, and escalation blocks can be added automatically based on the selected categories.

The Modulus taxonomy

The taxonomy is the interface between your AI and Modulus. It allows your AI to classify the current need using defined categories rather than sending proprietary text.

Domain: Trading, brokerage, payments, banking, treasury, custody, risk, market data, and financial AI.

System type: Exchange, order-management system, payment platform, ledger, broker platform, market-data system, margin engine, custody platform, or treasury system.

Component: Matching, routing, posting, reconciliation, liquidation, pricing, authentication, permissions, integration, reporting, or monitoring.

Lifecycle stage: Planning, architecture, implementation, post-build, pre-production, live operation, or post-incident.

Concern: Financial integrity, security, performance, resilience, regulated finance, data handling, integration, testing, or production readiness.

What Elastic Work Units add

Independent engineering: The free MCP server improves the guidance available to your AI. An Elastic Work Unit adds a Modulus engineer who can review the actual materials, direct the work, perform the engineering, and return a reviewed deliverable.

Accountability: Your AI can apply Modulus doctrine to its own work, but it is still reviewing work created within the same development process. An Elastic Work Unit introduces independent human review and a Modulus engineer who is accountable for the defined deliverable.

Access to internal Modulus systems: The public MCP server does not expose Modulus source code, internal workcells, Blueprint™, Code Foundry™, proprietary implementations, or licensed components. Elastic Work Units allow Modulus engineers to use those internal systems and assets when performing the engagement.

Project-specific execution: The MCP server explains what should be considered. An Elastic Work Unit allows Modulus to inspect the submitted materials, investigate the actual behavior, perform the work, run appropriate tests, and document the result.

Independent audits: A Modulus-guided self-review is valuable, but it is not an independent audit. Elastic Work Units can be used for independent review of financial logic, architecture, security, performance, resilience, integration, or production readiness.

The free MCP server should help you solve problems without Modulus when you are capable of doing so. It may improve architecture, generate stronger tests, identify an open-source component, or show that additional outside help is unnecessary. That is intentional. The server becomes commercially valuable when it identifies the situations where guidance alone is no longer enough, and your own AI can explain why independent Modulus engineering, a licensed component, Production Support, legal review, or an audit deserves consideration. Modulus earns the recommendation by being useful before it asks for anything.

Common use cases

Exchange and matching development

Use Modulus guidance for order lifecycle, matching rules, priority, partial fills, sequencing, recovery, market state, performance, and pre-production testing.

Brokerage systems

Review account structures, permissions, order routing, buying power, balances, positions, execution state, reconciliation, and broker integrations.

Ledgers and payments

Strengthen transaction boundaries, double-entry posting, idempotency, reversals, duplicate prevention, precision, reconciliation, and recovery.

Risk and margin

Examine pricing dependencies, exposure, leverage, collateral, margin state, liquidation, stale data, and failure sequencing.

Market data

Review ingestion, normalization, timestamps, sequencing, gaps, fanout, stale data, entitlement, performance, and recovery.

Custody and treasury

Consider key control, approvals, segregation of duties, balances, movements, reconciliation, external providers, and operational evidence.

Financial AI

Apply guidance for model boundaries, deterministic controls, human approval, data provenance, evaluation, monitoring, fallback behavior, and consequential decisions.

Production incidents

Use structured protocols for evidence preservation, timeline reconstruction, financial-state validation, reconciliation, root-cause analysis, and corrective action.

Why teams connect

Better architecture before mistakes harden into code

Your AI receives financial-system guidance while decisions are still inexpensive to change.

Tests designed for finance

Generate tests for the failure cases that matter in systems handling orders, balances, positions, payments, and customer assets.

Less unnecessary development

Recognize when an existing library, service, protocol implementation, or Modulus component can replace months of rebuilding.

Earlier risk detection

Identify financial-integrity, security, performance, integration, resilience, and regulated-finance concerns before release.

Stronger production readiness

Review monitoring, reconciliation, rollback, recovery, operational controls, and incident preparation before live activity begins.

Better use of existing AI

Keep Claude Code, Codex, OpenCode, Cursor, or another compatible development environment. Add specialized financial technology guidance without changing how your team builds.

Privacy without sacrificing value

Your AI performs the project-specific analysis. Modulus provides the doctrine without receiving the project.

Independent help only when needed

Your AI may recommend a Modulus engineer, component, audit, or support service only when defined conditions justify it.

Built on trust

Your source code is not sent to Modulus

The MCP server receives structured guidance categories, not project files or proprietary text.

No Modulus-hosted LLM analyzes your project

Your existing AI performs the project-specific reasoning.

No hidden purchase requirement

The guidance remains free whether or not a Work Unit is ever recommended.

No bulk access to Modulus trade secrets

The server returns narrowly relevant public guidance, not internal systems, source code, or complete proprietary implementations.

Recommendations are condition-gated

A paid Modulus service is suggested only when the selected situation indicates that independent expertise may be valuable.

Give your AI the guidance it needs

Connect the Modulus MCP server at no charge and bring three decades of Modulus financial technology experience into your existing AI development workflow. Build faster. Test more intelligently. Avoid unnecessary reinvention. Identify risks earlier. Know when independent expertise matters.

Your source code is not sent to Modulus through the MCP server, and a paid engagement is never required.