neurochat

by AyushXRane

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: loading atlases and volumes, listing regions, navigating, display settings, overlays, ROI statistics, volume arithmetic, screenshots, and script export. No two tools could be confused.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., load_atlas, list_regions, compare_volumes). Even 'screenshot' is a common noun-as-verb, fitting the pattern. No mixing of styles.
Tool Count5/5
With 10 tools covering loading, exploration, visualization, statistics, comparison, and export, the set is well-scoped for neuroimaging analysis. Each tool earns its place without unnecessary redundancy.
Completeness4/5
The tools cover core workflows: data loading, region inspection, navigation, display, statistics, and script export. Minor gaps exist, such as missing histogram or filtering capabilities, but the surface is sufficient for reproducible exploration.
Average 4.5/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behavioral traits: display-only, no registration, alignment requirement. No annotation provided so description carries burden, and it does well.
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?
Front-loaded with core action, followed by essential warnings and parameter list. Every sentence serves a purpose; mild repetitiveness in param docs.
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?
Covers operation, return value, behavioral boundaries, and parameter details. Lacks mention of error handling or result format, but adequate for visualization 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?
With 0% schema coverage, description compensates by explaining each parameter's role, defaults, and constraints (e.g., opacity range). Adds clarity beyond schema types.
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 verb 'stack' and resource 'loaded volumes', and distinguishes from siblings like compare_volumes by emphasizing display-only and no registration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance on when not to use (if volumes unaligned) and explains defaults, but could be more explicit about alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the side effect (writing a file) and return value (path), along with dependencies and reproducibility. This is transparent for a write operation.
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 exceptionally concise: two sentences plus an Args block, no redundancy, and front-loaded with the key action. Every sentence adds value.
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's simplicity (one parameter, no output schema), the description covers purpose, parameter, and usage. It could mention overwrite behavior, but it is sufficiently complete for the agent to invoke correctly.
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?
Schema coverage is 0%, but the description provides meaningful context for the 'path' parameter with an example ('~/analysis/session.py'), explaining where to write the script. This adds value beyond the schema's type and title.
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 action ('Write') and the resource ('whole session as a standalone runnable .py and return its path'). It distinguishes from sibling tools that load, navigate, or display data, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates this is the deliverable and specifies dependencies (numpy, nibabel, nilearn), implying when to use. It does not explicitly exclude alternatives or compare to siblings, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return values (shape, voxel size, etc.) and states no preprocessing is performed. Lacks mention of side effects on session state, but adequately covers safety for a load operation.
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?
Concise paragraph covering purpose and return info, followed by clear argument descriptions. Every sentence is informative and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 3-parameter tool with no output schema, the description fully covers inputs, expected outputs, and key behaviors (no preprocessing, space assertion). No obvious gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, description thoroughly explains each parameter: path format, name default, and space assertion semantics. Adds significant value beyond schema types.
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?
Clearly states it loads a preprocessed NIfTI volume (MRI, PET) and returns detailed info. Distinguishes from sibling 'load_atlas' which loads atlases.
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?
Specifies that the volume must be already-preprocessed, but does not provide explicit guidance on when to use versus alternatives like compare_volumes or roi_stats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: resampling of volume b onto a's grid if grids differ, and that the result is added to the session. Without annotations, this is valuable behavioral context.
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 extremely concise with a clear front-loaded purpose, followed by precise parameter explanations. Every sentence adds value without repetition or fluff.
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?
The description sufficiently covers the tool's behavior for a neuroimaging context, but could be slightly more complete by noting the output's data type or that it becomes available for subsequent operations. However, the absence of an output schema is mitigated by the 'adds to session' statement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite zero schema description coverage, the Args section provides full semantic meaning for all four parameters: 'a' is minuend/numerator and output grid, 'b' is second volume with resampling note, 'method' lists options, and 'name' is a handle. This completely compensates for the schema gap.
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 it performs voxelwise difference or ratio between two volumes and adds the result to the session. It specifies 'Arithmetic, not inference,' effectively distinguishing it from statistical tools in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives important usage guidance by saying the output is not a statistical map and should be described as a difference image. It does not explicitly mention when not to use or alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It reveals the tool returns voxel counts and centroids, and imposes a 200-row cap. It does not mention error states or permissions, but the core behavior is transparent.
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 compact two-paragraph docstring with clear Args section. Every sentence adds value, no repetition or fluff.
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?
Despite no output schema, the description notes return includes voxel counts and centroids, and covers limiting behavior. It lacks details on ordering or error handling, but is sufficient for a simple listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description adds full semantic meaning: query is a substring/fuzzy filter, limit is capped at 200 with default 50. This goes well beyond the bare schema types.
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 lists or searches atlas regions with voxel counts and centroids, using specific verb and resource. It distinguishes from siblings like load_atlas (loading) and roi_stats (statistics).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the query parameter (substring/fuzzy filter) and that omitting it returns everything. It also notes the limit cap at 200. However, it lacks explicit contrast with sibling tools like roi_stats or compare_volumes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses rendering behavior (live canvas vs. server-side) and notes the response indicates which method ran. It lacks details on downscaling ratio or behavior on empty views, but overall provides useful behavioral context.
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 three concise sentences plus one line for the parameter, with the main action front-loaded. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single optional parameter, no output schema, and no annotations, the description sufficiently covers the tool's behavior: it captures a view, returns an image and path, and explains rendering modes. No missing critical information.
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?
With 0% schema description coverage, the description adds meaning by explaining the filename parameter: 'Optional name for the PNG inside the session's temp directory.' This clarifies the parameter's purpose beyond the raw schema.
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 explicitly states 'Capture the current view as a downscaled PNG and return the image and its path.' It uses a specific verb (capture) and resource (current view as PNG), clearly distinguishing it from sibling tools that load or process data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a screenshot of the current view is needed, mentioning two rendering modes (browser vs server-side). However, it does not explicitly state when to use or not use this tool, nor does it name alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description clarifies that min/max 'does not alter the data,' indicating non-destructive behavior. It doesn't detail other traits like permission needs or reversibility, but the behavior is straightforward.
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 concise at 7 lines with a clear Args structure. Every sentence provides essential information without redundancy.
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?
The description covers purpose, parameters, and non-destructive nature. It lacks detail about return values or confirmation of changes, but given the simple display operation, it is mostly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully compensates by explaining each parameter: colormap examples, min/max meaning, opacity range, and volume default. This adds significant value beyond the bare schema.
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 'set colormap, intensity window and opacity for one layer,' specifying the verb and resource. It uniquely handles display settings, distinguishing from siblings like load_volume or navigate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions 'Defaults to the most recently loaded' volume, implying typical use. However, it does not explicitly state when not to use or provide alternatives, though the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that non-bundled atlases are fetched and cached, which is relevant behavioral context. No annotations are provided, so the description covers the safety profile (read-only load). It could be more explicit about read-only nature but is sufficient.
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 three sentences plus argument details, with each sentence contributing purpose, usage importance, and parameter details. It is front-loaded with the core action and has no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter) and no output schema, the description covers what is returned (full label list) and why it matters. It is complete for an agent to understand and use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no parameter descriptions (0% coverage), but the description lists all valid values for 'atlas_name' and explains caching behavior for non-bundled atlases. This fully compensates for the missing schema descriptions.
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 'Load an atlas and return its full label list,' specifying the verb and resource. It distinguishes from siblings like 'load_volume' by focusing on atlases and labels, and explains the role of the label list in grounding region names.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells when to use the tool: before referencing regions, emphasizing that labels from this tool must be used over the agent's own knowledge. It lists valid atlas names. However, it does not explicitly mention when not to use it or compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses mutual exclusivity, error handling (near-miss returns suggestions, moves nothing), coordinate source restrictions, and default values for space and volume.
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 concise with a clear summary, usage paragraph, and well-labeled Args section. Every sentence adds meaningful information without redundancy.
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 no output schema and simple side-effect behavior, the description covers usage, defaults, and error cases. Minor missing detail about visual feedback does not detract significantly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully documents all four parameters with examples, units, and defaults, adding significant value beyond the bare schema.
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 moves the crosshair to a named atlas region or explicit coordinates, and it distinguishes from sibling tools like load_atlas and load_volume.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies that exactly one of region_label or coords must be used, explains behavior for near-misses, and notes coordinate source restrictions. It lacks explicit comparison to alternatives like list_regions but is sufficient for its context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavior: it returns specific statistics, emphasizes reporting exclusion counts, states no inference is performed, and details the exclude_zeros parameter implications. This surpasses the need for annotations.
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 concise and well-structured: first line states purpose, then lists return values, followed by parameter details. No unnecessary words, and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters with no output schema, the description covers all necessary aspects: return values, parameter constraints, default behavior, and usage caveats. It is fully complete for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must add meaning. It explains volume defaults to the most recently loaded, clarifies mutual exclusivity of region_label and mask_path, and describes exclude_zeros default and rationale. This adds significant value beyond the schema.
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 returns descriptive statistics (n_voxels, mean, sd, median, min, max) inside an atlas region or mask file. It explicitly distinguishes from sibling tools like compare_volumes by stating no inference is performed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use the tool (for descriptive statistics) and what it does not do (no inference). It also explains mutual exclusivity of region_label and mask_path, and notes the default volume. However, it does not explicitly compare to alternative sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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