As enterprises embrace agentic AI architectures, there’s a natural temptation to expose every API as a tool for AI agents via protocols like the Model Context Protocol (MCP). This is a mistake. Not every API is agent-ready. Indiscriminate exposure creates security vulnerabilities, operational risks, and degraded agent performance.
To help, we will outline a framework for evaluating API readiness for AI agent consumption and propose an AI Readiness Profiles – a governance mechanism that can be enforced through API management platforms like MuleSoft API Governance.
Documentation is foundational
MCP enables AI agents to discover and invoke APIs based on context. The better documented and more precisely described an API is, the higher the likelihood an agent will select the right tool for the task. But documentation quality is only one dimension. APIs that are perfectly functional for human-driven applications may be wholly unsuitable for agent consumption. The reasons fall into ten categories.
10 reasons why every API shouldn’t be MCP-exposed
Let’s discuss specific reasons why not to expose all of your APIs to MCP.
1. Documentation quality and discoverability
MCP relies on tool descriptions for agent discovery. Vague or incomplete descriptions lead to wrong tool selection or hallucinated parameters. If an agent can’t understand what an API does, it will misuse it.
- Governance checkpoint: Are endpoints, parameters, and responses fully described with examples?
2. Security and access control
Some APIs expose sensitive operations: delete actions, financial transactions, PII access. Exposing these as MCP tools gives AI agents potential access without the same guardrails humans have. Authorization boundaries must be explicit.
- Governance checkpoint: Does this API require human-in-the-loop authorization? Are there operations that should never be agent-invocable?
3. Idempotency and side effects
Agents retry. Agents explore. Non-idempotent APIs – those where repeated calls create duplicate records, trigger multiple payments, or corrupt state – are dangerous in agent hands.
- Governance checkpoint: Is this API safe to call multiple times with the same parameters?
4. Cost and rate limits
Some APIs are expensive per call: third-party data providers, AI inference endpoints, metered SaaS APIs. Agents don’t inherently understand costs. They call what seems relevant. Without governance, agent traffic can generate runaway spend.
- Governance checkpoint: What is the cost per call? Are there rate limits that agent traffic could exhaust?
5. Latency and agent UX
Slow APIs degrade the agent experience. If an agent waits 30 seconds for a tool response, the end-user suffers. Batch processing endpoints, legacy system calls, and complex database joins may work fine for async human workflows but fail in real-time agent contexts.
- Governance checkpoint: What is the P95 response time? Is this acceptable for synchronous agent invocation?
6. Infrastructure readiness and capacity
This is distinct from latency. APIs are often designed and load-tested for known traffic profiles — “this handles 100 requests per minute from our mobile app.” Agents don’t respect those assumptions. They explore, retry, and fan out. Worse, the backend infrastructure – databases, mainframes, ESBs – often serves multiple consumers. Agent-induced load on one API can starve or destabilize unrelated systems sharing that backend.
- Governance checkpoint: Is the backend scaled for unpredictable, exploratory traffic? Are there circuit breakers to isolate agent traffic from production workloads?
7. Data granularity mismatch
Some APIs return massive payloads full database dumps, paginated lists of thousands of records. LLMs have context limits. Flooding them with raw data degrades reasoning quality. APIs need to be right-sized for agent consumption, or wrapped with filtering and summary layers.
- Governance checkpoint: What is the typical response payload size? Does it fit within agent context constraints?
8. Business process integrity
Some operations require human approval, multi-step workflows, or audit trails. Exposing them as single MCP tools bypasses process controls entirely. An “approve purchase order” API shouldn’t be directly agent-invocable without workflow context.
- Governance checkpoint: Does this operation require human-in-the-loop approval or audit logging that agent invocation would bypass?
9. Semantic overlap and tool collision
If you expose 50 APIs with similar descriptions – “get customer data,” “fetch customer info,” “retrieve customer record” – agents struggle to differentiate. Curation isn’t just about quality; it’s about creating a clean, non-overlapping tool surface.
- Governance checkpoint: Are there other MCP-exposed tools with similar descriptions? Is the semantic boundary clear?
10. Regulatory and compliance constraints
Certain data access has regulatory implications: GDPR right-to-know requests, HIPAA-protected health information, SOX-controlled financial data. Exposing these as MCP tools without compliance review creates audit risk.
- Governance checkpoint: Does this API access regulated data? Has legal/compliance reviewed agent-based access?
The AI Readiness Profile
These ten dimensions form the basis of an AI Readiness Profiles – a governance ruleset that determines whether an API should be exposed as an MCP tool. The profile can be implemented as a set of automated and manual checks:
| dimension | Automated check | Manual review |
|---|---|---|
| Documentation Quality | API spec completeness scoring | — |
| Security | OAuth scopes, sensitive operation flags | Security review for high-risk APIs |
| Idempotency | HTTP method analysis, retry-safety metadata | — |
| Cost | Cost per call metadata, rate limit config | Finance review for high-cost APIs |
| Latency | P95 response time from monitoring | — |
| Infrastructure readiness | Load test results, circuit breaker config | Capacity planning review |
| Data granularity | Response size analysis | — |
| Business process | Workflow dependency metadata | Process owner sign off |
| Semantic overlap | Similarity scoring against existing MCP tools | — |
| Compliance | Data classification tags | Legal/compliance review |
APIs that pass the profile, either through automated validation or explicit approval, are flagged as AI Ready and eligible for MCP exposure. Those that don’t are excluded until remediated.
Implementation path
With the AI Readiness Profile in hand, start implementing with the following phases:
- Inventory and classification: Tag existing APIs with metadata required for AI Readiness evaluation. Identify high-value candidates for MCP exposure
- Governance ruleset: Define the AI Readiness Profile in your API governance platform. Set thresholds for automated checks and workflows for manual reviews
- CI/CD integration: Integrate AI Readiness validation into the deployment pipeline. APIs that pass the profile can have MCP tooling auto-generated; those that fail are blocked or flagged
- Runtime monitoring: Monitor agent traffic patterns, cost accumulation, and infrastructure impact. Feed learnings back into the governance ruleset
Your blueprint for success
MCP is a powerful protocol for enabling AI agents to interact with enterprise systems. But power without governance is risk.
Not every API should be an MCP tool. The APIs that should be are those that are well-documented, secure, idempotent, cost-controlled, fast, scalable, right-sized, process-compliant, semantically distinct, and regulation-cleared.
The AI Readiness Profile provides a framework for making that determination systematically – turning what would otherwise be a sprawling, risky surface into a curated, governed, agent-ready toolset. To dive deeper into how you can get AI ready, watch the Unlocking AI: Your Agent-Ready API Blueprint for Success webinar.
