Large catalogs increase risk because they widen the tool surface an agent can discover, while also making correct retrieval harder. The result is more context pressure, more failed searches, and a greater chance that the agent sees or uses the wrong capability. In practice, scale changes tool discovery into an identity governance issue.
Why This Matters for Security Teams
Large MCP tool catalogs turn agent security into a discovery and authorisation problem, not just an inventory problem. As the catalog grows, the agent has more ways to reach data, systems, and workflows it was never intended to touch, while retrieval quality often drops under context pressure. That combination is especially risky for autonomous agents because they can chain tools, retry failed actions, and continue operating after a human would have stopped. Current guidance from OWASP Top 10 for Agentic Applications 2026 and SailPoint research on AI agents as the new attack surface both point to the same operational reality: broad capability without precise governance creates accidental overreach and hidden misuse. In practice, many security teams discover the risk only after an agent has already reached the wrong tool or exposed data outside its intended scope.
How It Works in Practice
Tool catalogs increase risk in three connected ways. First, they expand the attack surface. Every additional MCP tool can introduce a new path to secrets, data movement, or privileged action, especially when access scoping is weak. Astrix Security’s MCP server security research found that only 18% of deployments implement any form of access scoping for tool permissions, while hard-coded secrets remain common. Second, large catalogs make retrieval less reliable. An agent under prompt pressure may surface the wrong capability, select a lookalike tool, or continue with incomplete context. Third, agents do not behave like static users. They explore, infer, and improvise, which means pre-approved role bundles do not reliably constrain what they can do at runtime.
Practitioners increasingly respond with smaller tool sets, explicit allowlists, and context-aware authorisation. The practical sequence is simple:
- Bind the agent to a workload identity, not a shared credential.
- Issue just-in-time, short-lived access for a specific task.
- Evaluate policy at request time, using the tool, the task, the data target, and the environment.
- Revoke access automatically when the task ends.
This is why controls aligned to NIST AI Risk Management Framework and CSA MAESTRO agentic AI threat modeling framework emphasize runtime governance rather than static catalog size. NHIMG’s analysis of CoPhish OAuth token theft via Copilot Studio shows how agents can be manipulated into revealing credentials or taking unintended action once trust is too broad. These controls tend to break down when catalogs are highly dynamic and tool ownership is unclear, because policy drift outpaces manual review.
Common Variations and Edge Cases
Tighter tool scoping often increases operational overhead, so organisations must balance safety against speed and developer friction. Best practice is evolving, and there is no universal standard for how many tools an agent should see at once. Some teams use per-domain catalogs, while others keep one catalog but expose only context-relevant tools through policy. The right answer depends on whether the agent is performing retrieval, execution, or multi-step orchestration.
Edge cases matter. A small catalog can still be dangerous if one tool can reach broad downstream systems. A large catalog may be acceptable if each tool is strictly scoped, ephemeral, and auditable. Conversely, even strong policy can fail if tools are poorly named, duplicate each other, or leak secrets through configuration. NHIMG research on the Moltbook AI agent keys breach and the OWASP NHI Top 10 both reinforce that identity sprawl and tool sprawl often appear together. For high-risk environments, current guidance suggests treating catalog curation as a governance control, not just a developer convenience.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Large tool catalogs amplify agent overreach and tool misuse risk. |
| CSA MAESTRO | T1 | MAESTRO addresses tool exposure and orchestration risk in agentic systems. |
| NIST AI RMF | GOVERN | AI RMF govern function fits policy, accountability, and oversight for agents. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Credential exposure and overbroad access often accompany large tool catalogs. |
| NIST CSF 2.0 | PR.AC-4 | Least privilege and access enforcement are central to reducing catalog risk. |
Limit exposed tools and validate every agent action against explicit runtime policy.
