Discover which neural decoding datasets are available for analysis, including Algonauts 2025, BOLD5000, Wen 2017, and Lebel 2023. Use this to identify study options for fMRI benchmarks or research workflows.
Lists available neural decoding studies (Algonauts 2025, BOLD5000, Wen 2017, Lebel 2023).
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Changes observed during successful MCP inspections.
v4.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists studies, which is a read-only operation, but does not specify return format, pagination, or ordering. The description is adequate for a simple listing tool but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and informative. It front-loads the main purpose and provides concrete examples, with no redundant or filler information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description is sufficient to understand the tool's basic function. However, it could mention the output format or how to use the results with other tools, but overall it is reasonably complete for a listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters jogging the baseline to 4. The description correctly notes that it takes no input, so there is nothing missing in parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists available neural decoding studies and enumerates specific examples (Algonauts 2025, BOLD5000, Wen 2017, Lebel 2023). This distinguishes it from sibling tools like system_diagnostics and benchmark_inference, which have different purposes, though it does not explicitly differentiate itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is a first step to discover which studies are available, but it does not explicitly state when to use it or how it relates to alternatives like benchmark_inference or get_atlas_regions. There is no mention of exclusions or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.