Claude Bedrock JSON Bug: Pydantic Validation Errors Fix

Unpacking the Bedrock/Claude Nested Parameter Problem

Alright, folks, let's dive straight into a pretty gnarly bug that's been tripping up some of us who are deeply entrenched in the world of AI agents and tool calling with strands and Bedrock's Claude models. Imagine you're building a super smart agent, empowering it with custom tools that take a bunch of different inputs. Everything's humming along, you define your tool's schema, and it looks perfect. But then, bam! You hit a wall. The core issue? When strands tries to orchestrate a tool call using the Bedrock Claude model, and that tool expects nested object parameters – essentially, a dictionary within another dictionary – Claude decides to get a little rebellious. Instead of sending that neat Python dictionary as, well, a dictionary, it incorrectly serializes it into a plain JSON string. Yep, you heard that right, a string! This seemingly minor hiccup then ricochets through your system, often leading to a dreaded Pydantic validation error. Pydantic, bless its heart, is just doing its job, expecting a proper dictionary for a parameter it deems an 'object,' but it receives a string instead. It's like ordering a delicious burger and getting a picture of a burger – looks similar, but entirely unpalatable when it comes to actual execution.

This isn't just a small annoyance, guys. This bug can completely derail your AI workflows, stopping your agent development dead in its tracks. You're left scratching your head, wondering why your perfectly defined tool schema isn't working, despite the model seemingly understanding the call. The most frustrating part? This issue doesn't seem to pop up with other models, like Gemini. That's a huge clue, pointing us towards a specific interaction problem between Bedrock/Claude and how strands handles its tool output. It strongly suggests that either Claude is misinterpreting the schema and stringifying the data, or strands isn't correctly parsing the Bedrock Converse API response for these nested structures. For anyone working with fastmcp or similar frameworks that rely heavily on Pydantic for input validation, this JSON string serialization problem with nested object parameters is a critical barrier. It forces developers into unexpected debugging sessions, trying to figure out why a str type is showing up where a dict is expected, utterly confusing the validation process. We're talking about lost time, increased frustration, and a significant roadblock to robust and reliable AI agent behavior. Understanding this core problem is the first step to finding a solid workaround or, even better, a permanent fix.

Diving Deep into the Technical Details: What's Going Wrong?

Let's get a bit more granular and really dissect what's happening under the hood. Our tool schema, which is the contract between our AI agent and the function it calls, clearly defines a parameters field. In our example, for an ecs_troubleshooting_tool, this parameters field is designed to accept a complex object – specifically, an anyOf schema allowing either an object with additionalProperties: true or null. This setup explicitly tells the system that parameters should be a dictionary that can hold various keys like ecs_cluster_name, ecs_service_name, and time_window. The expectation is that when our strands agent invokes this tool, it passes these nested object parameters as a standard Python dictionary, which is then seamlessly picked up by Pydantic for validation. For instance, we expect a call like this: ecs_troubleshooting_tool(action="fetch_service_events", parameters={"ecs_cluster_name": "my-cluster", "ecs_service_name": "my-service", "time_window": 3600}) where parameters is undeniably a Python dict.

However, what we actually get from the Bedrock/Claude model is something entirely different and profoundly problematic. Instead of a dictionary, the parameters argument arrives as a single, JSON-encoded string: ecs_troubleshooting_tool(action="fetch_service_events", parameters='{"ecs_cluster_name": "my-cluster", "ecs_service_name": "my-service", "time_window": 3600}'). See the single quotes around the entire dictionary structure? That's the tell-tale sign of a string. This JSON string serialization is the root cause of our Pydantic validation error. Pydantic, being the meticulous guardian of data types that it is, looks at the input for the parameters field. It was told to expect a dict, but it receives a str. It promptly throws a ValidationError along the lines of Input should be a valid dictionary [type=dict_type, input_value='{"ecs_cluster_name": "in..."}', input_type=str]. This error message is crystal clear: Pydantic expected a dictionary type (dict_type) but was given a string type (str) with the input_value being the stringified JSON. This discrepancy breaks the entire tool invocation chain, as the validated arguments are essential for the tool's proper functioning. The problematic environment often involves strands version 1.18.0, fastmcp in the 2.13.x range, Python 3.11 or 3.12, and specifically Claude models from Bedrock such as us.anthropic.claude-sonnet-4-20250514-v1:0 and other versions. This precise technical mismatch highlights a fundamental miscommunication in how nested data structures are passed and parsed, rendering what should be a straightforward tool call into a frustrating debugging exercise.

Potential Culprits and How We're Thinking About Solutions

When a bug like this rears its ugly head, especially one that impacts fundamental AI agent development and tool integration, it's time to put on our detective hats and figure out the root causes. We've identified a few prime suspects. First up, and a strong contender, is the BedrockModel response parsing within strands itself. It's entirely possible that when the Bedrock Converse API sends back its tool call arguments, it might, for certain complex nested parameters, be returning them as a pre-encoded JSON string. If strands' BedrockModel implementation isn't explicitly designed to check for and deserialize these nested objects from strings back into Python dictionaries, then it's just passing along the problem. This would explain why Gemini models, which likely have a different response structure or strands handles them differently, don't exhibit the same issue. It's a classic case of expectation versus reality – the API might send a string, expecting the client (strands) to parse it, and strands might expect the API to send a ready-to-use dictionary.

Another possible, though perhaps less likely, culprit is schema interpretation by Claude itself. Could it be that Claude, when processing a type: "object" within a tool schema, is making an independent decision to stringify that nested structure before handing it off to Bedrock's Converse API? While less probable given Gemini's correct behavior, it's not entirely out of the question that different LLMs might interpret complex OpenAPI or JSON Schema specifications with subtle variations. Lastly, we could also look at the broader strands tool invocation logic. Is there a specific stage in strands where it should be applying a generic deserialization step for any parameter designated as `type: