Server Quality Checklist

Tool Scores

• get_atlas_regionsC
  2.7/5.0
  Behavior2/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Retrieves', implying a read operation, but does not state whether the tool is read-only, whether it has any side effects, or what the return format is. There is no mention of limitations, permissions, or error conditions.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is a single concise sentence that front-loads the main action. There is no unnecessary verbosity, and it conveys the core purpose efficiently. It could be slightly more informative without being overly long, but it is well-structured.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool has a single parameter, no output schema, and no annotations, the description is minimal. It does not specify the return format (e.g., region names, coordinates), whether the operation is read-only, or any prerequisites. For a tool with such sparse structured metadata, the description should provide more context to enable correct usage.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters2/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The schema description coverage is 0%, so the description must compensate for parameter meaning. However, it adds no information about the atlas_type parameter beyond what the schema already provides (enum values and default). The description does not explain what each atlas type represents or how the parameter affects the returned regions.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description states a specific action ('Retrieves') and a clear resource (HCP cortical or Harvard-Oxford subcortical ROI regions). It names the two atlas types, making the purpose unambiguous. It does not explicitly differentiate from siblings, but the siblings are unrelated, so no differentiation is needed.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  No guidance on when to use this tool versus alternatives. The description implies usage for retrieving atlas regions, but there is no explicit context, such as when to choose cortical vs subcortical, or any mention of related tools or workflows. Siblings are unrelated, so no exclusions are stated.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• benchmark_inferenceC
  2.8/5.0
  Behavior2/5

  Does 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 only states that a benchmark is run, without revealing whether the operation is read-only, potentially destructive, resource-intensive, or what the output format is. The agent cannot assess safety or side effects.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is a single, front-loaded sentence that communicates the core purpose without redundancy. It is appropriately concise and well-structured, with the action verb and key metrics immediately clear.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness1/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  With no annotations, no output schema, and no parameter documentation, the description is severely incomplete. It does not inform the agent about parameter meanings, return format, required environment, or side effects, making it impossible to call the tool correctly without external knowledge.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters1/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The schema has 0% description coverage, meaning parameter names alone must convey meaning. The description does not explain seq_len, batch_size, or iterations at all, failing to compensate for the lack of schema documentation. An agent would have to guess based on naming conventions alone, which is inadequate.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool runs a synthetic in-memory benchmark measuring speed, throughput, and VRAM footprint. The verb 'runs' and specific measurements differentiate it from siblings like system_diagnostics, which are about system health rather than model performance.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives such as system_diagnostics or get_atlas_regions. It does not mention any conditions, prerequisites, or scenarios where it is appropriate, leaving the agent to infer usage from the purpose alone.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• list_supported_studiesA
  3.7/5.0
  Behavior3/5

  Does 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.

  Conciseness5/5

  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.

  Completeness4/5

  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.

  Parameters4/5

  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.

  Purpose4/5

  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.

  Usage Guidelines3/5

  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.

• system_diagnosticsA
  3.7/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations provided, the description carries the full behavioral burden. The verb 'Returns' implies a read-only operation, which is useful, but nothing is said about side effects, cost/latency, or failure modes (e.g., what happens if CUDA is unavailable).

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  A single, front-loaded sentence lists exactly what the tool returns with no filler. Every word earns its place, and the structure conveys the complete scope efficiently.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple parameterless diagnostics tool, the description names all key output categories. However, with no output schema, the agent does not know the exact response format or field names, which would be helpful but is not critical for deciding to call it.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters4/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The tool has zero parameters, so the baseline is 4. The description correctly avoids inventing parameter explanations and the empty input schema fully covers parameter needs.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description uses a specific verb ('Returns') and names precise resources (hardware info, CUDA capability, VRAM, UltraTribe framework status). It clearly distinguishes itself from siblings like benchmark_inference and get_atlas_regions by content, though it does not explicitly name a sibling or state what it is not.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines3/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Usage context is implied: call this when you need environment/hardware information. However, there is no explicit guidance on when to prefer this over alternatives, when not to use it, or any prerequisites such as CUDA availability.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.