Don’t give an AI agent an unrestricted SQL tool when a small set of business operations can do the job. A tool such as findSchoolsMissingContact gives the agent a specific capability with constrained inputs; a general executeSql tool can let it choose tables, fields, joins, and operations. The key issue is not that SQL is inherently unsafe—it is where authority lives and how it is enforced.
Why raw SQL gives an agent too much authority
A model that can submit arbitrary SQL has a broad mechanism whose effects depend on the database account, exposed schema, result handling, and controls around execution. A prompt telling it not to access certain records is not an access-control boundary. OWASP’s LLM06:2025 Excessive Agency guidance recommends limiting an agent’s tools, functions, permissions, and autonomy, and favors granular extensions over open-ended ones.
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That does not mean every agent must avoid SQL entirely. A carefully bounded, read-only SQL path may suit some analytics tasks when database permissions and data exposure are tightly constrained. But when the task is a known business action, a named capability is usually easier to bound and review than a general-purpose query tool.
Expose business capabilities, not database mechanics
Start with the task the user needs done, then provide only the operations that task requires. For example, an agent asked to identify schools missing contact details could call findSchoolsMissingContact with a constrained input schema. It need not invent joins, select arbitrary columns, or understand the database’s full structure.
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- A capability name should describe a real operation, not disguise an unrestricted query endpoint.
- Inputs should be limited to the values needed for that operation, with validation in trusted code.
- Return only the rows and fields required to answer the user’s request.
- Avoid automatically exposing every CRUD operation if the agent needs only one or two actions.
Granular tools trade some flexibility for a smaller authority surface. They require design and maintenance as business operations evolve, and they may be a poor fit for open-ended exploratory analytics. Choose based on the task rather than treating either architecture as universally right.
Keep identity, scope, and policy on the server
The agent’s tool input should not determine whose data it can access or enlarge its own permissions. Derive identity and tenant scope from the authenticated user, keep credentials and authorization state in trusted server-side code, and enforce access at the application and downstream resource layers. OWASP recommends downstream authorization and execution in the user’s security context with the minimum privileges needed.
Constrain database credentials as well as the tool interface. For read-oriented work, a read-only database identity and narrowly scoped views or equivalent controls can limit the impact of mistakes. Keep write authority separate and grant it only to operations that require it. A narrow tool connected to an over-privileged account is not a sufficient boundary.
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Do not confuse approval, authorization, validation, and audit
These controls answer different questions. Approval asks whether a proposed sensitive action should proceed; authorization asks whether this actor is allowed to perform it; validation asks whether the requested change is legal under business rules; audit records what happened. One does not replace the others.
- Authenticate the user and derive the applicable identity and data scope on the server.
- Check authorization for the specific operation and target resource.
- Validate the requested state against domain rules.
- For high-impact changes, require an appropriate approval before execution.
- Perform the operation, record an audit event, and return the persisted result rather than presenting the model’s proposed input as saved state.
The precise checks and approval thresholds depend on the application. The important point is to implement them in trusted layers, not rely on the model to follow a prompt.
Keep parameterized SQL in the implementation
Using business tools instead of model-authored SQL does not eliminate SQL injection risks in the application code behind those tools. OWASP’s SQL Injection Prevention Cheat Sheet recommends prepared statements with parameter binding so the database treats values as data rather than SQL code.
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Parameterization addresses the boundary between SQL code and values. It does not decide whether the agent should be allowed to access a table, see a field, or perform a business action. Use both safe query construction and independent authorization and least-privilege controls.
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Tool responses should contain only information the agent needs to continue the task. Avoid returning unrestricted query results or sensitive fields simply because the database can provide them. Give the model a safe, useful error when an operation fails, and preserve diagnostic details in appropriately protected server telemetry. Traces should not casually capture sensitive tool inputs or internal exceptions.
Logging is valuable only when its own access and retention are controlled. An audit record of an action and diagnostic telemetry for a failure serve different purposes; design each for its intended audience and protect both.
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Evaluate the architecture on the boundaries that matter
When comparing a raw SQL tool with task-specific capabilities, assess where authority is granted and where controls are enforced—not just how convenient the interface looks.
| Question | General SQL tool | Bounded business capabilities |
|---|---|---|
| What can the agent request? | Potentially arbitrary queries or operations, depending on tool and database permissions. | Only the operations and inputs deliberately exposed. |
| Where are permissions enforced? | Must be enforced in the application and database; a prompt or query interface is not an access-control system. | Can be enforced per operation in trusted application code and at the downstream resource. |
| How are reads and writes separated? | Depends on database credentials and execution controls. | Can be represented as separate capabilities and backed by distinct least-privilege credentials. |
| How much schema does the agent need? | Potentially enough to compose queries across exposed schema. | Only the fields and concepts required by the operation. |
| What is the trade-off? | Flexible for some open-ended analysis, but the reachable effects depend on granted authority. | More bounded and task-oriented, but requires designing and maintaining operations as needs change. |
Neither column guarantees safety by itself. A capability can still be overly broad or poorly authorized, while a narrowly privileged read-only query tool may be a deliberate choice for a specific workload. Review the complete path from user identity through tool execution to database permissions and returned data.
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What the TeaQL adapter example does—and does not—establish
Philip Z’s TeaQL article describes an @teaql/ai-sdk adapter built around allowlisted business capabilities. In its example, the server-side execution closure retains the UserContext, resources, authorization state, and credentials instead of asking the model to provide them. The article also describes approval metadata, audit behavior, and mapping internal failures to safer errors.
Those are architectural choices to inspect, not independent proof that an implementation or deployment is secure. The article reports a small SQLite demonstration and project tests; it does not establish production security validation. It also identifies generator-produced capabilities, a hosted demo, OpenTelemetry export, and cross-runtime MCP execution as follow-up work. Evaluate the code and controls for your own deployment rather than treating the adapter’s existence or reported tests as a security certification.
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