mcp-altair-studio

by alan4041207

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct data science operation or utility (e.g., classification vs. clustering vs. PCA vs. data cleaning), with no overlapping responsibilities. The advanced escape hatch is clearly separated as a catch-all for unsupported operators.
Naming Consistency5/5
All tools follow a consistent 'altair_verb_noun' pattern in snake_case, with clear action words (e.g., import_data, train_classifier, run_operator_chain). The minor deviation in 'descriptive_stats' still fits the pattern well.
Tool Count5/5
18 tools is well-calibrated for a data science MCP server, covering data ingestion, preparation, modeling, evaluation, and process management without being overwhelming. Each tool feels necessary and justified.
Completeness4/5
The set covers the core data science workflow (import, clean, transform, model, evaluate, export) thoroughly. A dedicated regression tool is missing, but the advanced operator-chain escape hatch fills that gap, making it nearly complete for common tasks.
Average 3.5/5 across 18 of 18 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 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
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavioral traits. It only states the output is the dataset with an added cluster-id column. It does not mention any side effects, destructive behavior, permissions needed, or what happens if parameters are invalid. The reference to action numbers is not behavioral.
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 concise with two sentences that state the core function and returns. However, it could be better structured to separate the purpose from the action numbers, but overall it is efficiently written with no fluff.
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 performs k-Means clustering with 3 parameters, no output schema, and no annotations, the description is incomplete. It does not explain how the cluster-id is labeled, the algorithm's behavior, handling of edge cases, or prerequisites like data format. The action number reference provides versioning context but not operational completeness.
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 description does not explain the purpose or constraints of the parameters ('k', 'csvFile', 'repositoryEntry'). Schema coverage is 67% with csvFile and repositoryEntry having descriptions, but the parameter 'k' lacks explanation beyond its default and range. The description adds no additional semantic information beyond the schema.
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 segments data using k-Means clustering and returns the dataset with an added cluster-id column. The verb 'Segment' and resource 'data with k-Means clustering' are specific. However, it does not differentiate from sibling tools like altair_association_rules or altair_reduce_dimensions_pca, which also perform segmentation or dimension reduction.
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 mentions 'Covers actions 56, 60, 64-65 (segmentation / clustering)', which is internal reference to Altair AI Studio actions but provides no guidance on when to use this tool versus alternatives. It lacks explicit when-to-use, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only reveals the output format and a prerequisite. It does not disclose error handling, effects of maxRows, or whether the operation is read-only. Minimal behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it is under-specified. It is not verbose, but the lack of detail reduces its usefulness.
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 no output schema and minimal description, the tool lacks completeness. It does not explain return values, pagination, or error scenarios, which are important for a read operation.
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?
Schema coverage is 0% and the description provides no information about parameters. It fails to explain the meaning of 'path' (e.g., absolute vs relative) or 'maxRows' (e.g., row limit behavior).
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 reads a data entry as CSV from the Altair AI Studio repository, specifying the verb, resource, format, and a prerequisite. It distinguishes from siblings like altair_list_repository (listing) and altair_store_csv_to_repository (writing).
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 mentions a required extension but provides no explicit guidance on when to use this tool versus alternatives like altair_import_data or altair_list_repository. The usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 the action without explaining behavior: whether the split is random, if it modifies the original data, or any side effects like creating new repository entries. The internal action number '66' adds no 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, consisting of two sentences with no wasted words. It front-loads the primary purpose and includes a secondary reference to an internal action identifier, which is brief but does not clutter the description.
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's complexity (splitting data for machine learning), the description is insufficient. It lacks details about the split strategy (e.g., random, stratified), reproducibility (no seed parameter), handling of imbalanced classes, or output format. Absence of output schema and annotations exacerbates the lack of completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents two of three parameters (csvFile and repositoryEntry) with descriptions of their usage and mutual exclusivity. trainRatio has default and range constraints. The description adds no parameter-specific information beyond what the schema provides. With 67% schema coverage, a baseline of 3 is appropriate.
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 splits a dataset into train/test partitions, which is a specific verb+resource. However, it mentions 'train/validation/test split' in parentheses, which is slightly different from the main description but still conveys the core purpose. It distinguishes from sibling tools like altair_clean_data or altair_normalize_data because splitting is a distinct operation.
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. It does not mention prerequisites, such as the need for a dataset already in the repository or local file, or any conditions under which splitting is appropriate. There is no comparison with other data manipulation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It notes the HTTP bridge requirement but does not indicate whether the tool overwrites or appends, what happens on failure, or any side effects. This is insufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the main action and key requirement. It is efficient without unnecessary elaboration, though it could be slightly more structured with parameter hints.
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?
The tool has only two parameters and no output schema, so the description should be relatively complete. However, it omits parameter details, error conditions, and behavior for existing entries, making it inadequate for an agent to use correctly.
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?
Schema description coverage is 0%, so the description must compensate. It implies csvFilePath is the local CSV file and path is the repository location, but adds no details on format, validation, or constraints. The parameter names are somewhat self-explanatory, but the lack of explicit semantics hurts.
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 writes a local CSV file's contents to a repository entry, with specific reference to action 82-83 flows. This distinguishes it from sibling tools like altair_import_data or altair_read_repository_entry, though it could be more explicit about the nature of a repository entry.
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 mentions the HTTP bridge extension requirement and references specific actions, providing context for use. However, it does not specify when not to use this tool or suggest alternatives among siblings, leaving the agent without clear exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavior. It only states the action (normalize/scale) without mentioning side effects, data mutation, missing value handling, or output format.
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?
Two sentences with no unnecessary words. Efficient and front-loaded, though it could benefit from slightly more structure.
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 absence of output schema and annotations, the description is too brief. It does not explain what the tool returns, whether it modifies the input, or how to handle errors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 67% (two params have descriptions). The description adds the list of methods but does not elaborate on parameter usage, defaults, or trade-offs. With moderate coverage, the description provides marginal additional value.
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 'Normalize/scale numeric attributes' and lists four specific methods (Z-transformation, range, proportion, interquartile), distinguishing it from sibling tools like altair_descriptive_stats or altair_clean_data.
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, no prerequisites, and no scenarios where it is appropriate or inappropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It describes what operations are performed (replace missing values, remove duplicates, drop columns) but fails to mention important behaviors such as whether the tool modifies the file in-place or returns a new dataset, required authentication, or limitations on data size.
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 two sentences long: the first sentence states the core purpose, and the second provides context via action numbers. It is front-loaded, concise, and contains no redundant information.
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?
With no output schema, the description should clarify what the tool returns or whether it has side effects. It only states 'clean a dataset' without explaining the output format or any changes to the input file. Given the 5 parameters and lack of annotations, the description leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters are fully described in the schema (100% coverage). The description reiterates the operations but does not add significant meaning beyond the schema, such as default behavior or typical usage patterns. Baseline score of 3 is appropriate.
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's purpose: cleaning a dataset with specific operations (missing value handling, duplicate removal, column dropping). It also references actions 11-15 and 20 of data preparation, providing context. While it doesn't explicitly differentiate from sibling tools, the operations are distinct enough for an agent to understand the tool's scope.
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 usage for cleaning tasks but does not explicitly state when to use this tool versus alternatives like altair_normalize_data or altair_split_data. The reference to specific data preparation actions gives some guidance, but no exclusions or comparison are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states it computes stats for every numeric column. It does not disclose behavioral traits like handling of non-numeric columns, side effects, or permissions.
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 very short (two sentences) and concise. It conveys the purpose without waste, but could include more useful information without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description does not explain the return format or value. It mentions statistics but lacks details on how results are presented. For a simple computation tool, this is adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters described. The description adds no additional meaning beyond what the schema provides, so baseline 3 is appropriate.
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 computes descriptive statistics (average, min, max, standard deviation, median, count) for every numeric column, which is a specific verb+resource. It distinguishes from sibling tools that handle association rules, clustering, cleaning, etc.
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 explicit when-to-use or when-not-to-use guidance is given. It does not mention alternatives among sibling tools or provide context for selecting this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. While it mentions the variance threshold, it omits important details such as whether the tool is destructive (modifies original data), prerequisites (e.g., numeric data), performance implications, or edge cases (e.g., insufficient variance). This is a significant gap.
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 concise (two sentences) with no redundant information. It front-loads the core functionality. However, the second sentence about action number adds minimal value and could be integrated.
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's complexity (PCA with a parameter), no output schema, and no annotations, the description is incomplete. It fails to specify what the output is (e.g., reduced dataset), prerequisites (numeric data, no missing values), or limitations. This leaves the agent with insufficient information to correctly invoke and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (2 of 3 parameters described). The description adds context by linking varianceThreshold to the target variance objective, but does not provide syntax or constraints beyond the schema. For a low-coverage schema, the description partially compensates but remains insufficient for all parameters.
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 'reduce', the resource 'dimensionality', and the method 'PCA'. It specifies the behavior of keeping enough components to reach a target variance threshold, making the tool's purpose unambiguous and distinct from siblings like clustering or normalization.
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 does not explicitly state when to use PCA vs other dimensionality reduction methods or when not to use it. It mentions 'Covers action 43 (PCA)' but provides no guidance on alternatives or context. Usage is implied but not clearly delineated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool creates a new column but does not disclose behavioral traits such as whether it modifies the dataset in place, if it is reversible, or what happens if an attribute with the same name exists. This is insufficient for a mutation tool.
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 two sentences, including an example and a reference to action 21. Every sentence adds value; there is no redundancy.
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 4 parameters, no output schema, and missing annotations, the description is incomplete. It lacks details on parameter relationships (csvFile vs repositoryEntry), return value, and usage context (e.g., whether it can be undone). Key gaps reduce the agent's ability to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75%, slightly below the 80% threshold, so the description should supplement. The description provides an example for attributeName and expression, adding context where the schema lacks description for attributeName. However, it does not explain the mutual exclusivity of csvFile and repositoryEntry, which the schema already hints at.
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 creates a new derived/calculated column using a RapidMiner expression, with an explicit example. It distinguishes from sibling tools like altair_clean_data or altair_normalize_data by specifying 'generate attribute' and referencing action 21, which is unique among siblings.
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 usage when a derived column is needed, but provides no explicit guidance on when to use this tool versus alternatives (e.g., altair_clean_data) or when not to use it. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It says 'Import/preview tabular data', but it is unclear whether this tool modifies state (imports) or only previews (read-only). The vague reference to 'ingestion actions' does not clarify safety, authentication, 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 two sentences with no wasted words. The first sentence states the core function, and the second provides categorical context. It is efficiently structured and easy to parse.
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 the main purpose and parameters for a simple tool without output schema or annotations. It lacks some context like whether the tool returns data or saves it, and what prerequisites exist (e.g., repository access). However, given the low complexity, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with clear descriptions for both csvFile and repositoryEntry. The tool description adds that these are two sources, but the schema already explains their meaning. The description does not add significant semantic value beyond the schema.
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 imports/previews tabular data from a repository entry or local CSV file. The mention of covering specific actions (1-2, 5-7, 9) provides some context, but it does not explicitly differentiate among siblings like altair_read_repository_entry, which may also handle repository data.
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 indicates the tool is used for importing/previewing data from CSV or repository, but it does not specify when to choose this over alternatives, nor does it provide any exclusions or prerequisites. The agent is left to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the algorithm and data format but does not mention side effects, authentication needs, or whether the tool creates or modifies data. The behavioral impact is largely unspecified.
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 two sentences, front-loaded with purpose and algorithm, then data requirement. Every sentence adds value without redundancy.
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?
No output schema is provided, and the description does not mention return values. For a mining tool, the output format (e.g., rules with metrics) is important but omitted. The action codes reference is vague.
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?
Schema coverage is 50%, with descriptions for only two parameters (csvFile, repositoryEntry). The description adds no parameter details, leaving minSupport and minConfidence unexplained. It does not compensate 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 the tool mines association rules using FP-Growth and is used for market-basket analysis. It specifies the algorithm and the required data format, distinguishing it from other Altair tools like clustering or classification.
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 provides a prerequisite (data must be in transactional/binominal form) and mentions coverage of association rule actions. It implies usage context (market-basket analysis) but does not explicitly state when not to use it or suggest alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey all behavioral traits. It mentions 'headlessly via rapidminer-batch' but does not disclose return values, error handling, or side effects. The absence of output schema makes this gap more significant.
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 consists of two succinct sentences with no extraneous information. The core action is front-loaded, and every word adds value.
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 lack of an output schema, the description should explain what the tool returns or produces (e.g., logs, process results). It only describes the action but omits the outcome, making it incomplete for an agent to understand the tool's full effect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds no new meaning beyond the schema's parameter descriptions; it only restates the context of headless execution. No extra format or constraint details are provided.
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 uses specific verbs ('Execute') and resources ('.rmp process file'), and differentiates from siblings like altair_run_operator_chain by specifying it runs saved files headlessly. It also cites relevant action numbers (76-80, 84) for clarity.
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 clearly states when to use: for saved .rmp files, headless execution, and repeatable workflows. It implicitly distinguishes from siblings (e.g., altair_open_process_in_studio for interactive use), but does not explicitly state 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that the tool replaces the current process (destructive) and hands control to the user, but does not detail side effects like loss of unsaved changes or behavior if the HTTP bridge is missing. Partial transparency.
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?
A single, clear sentence that front-loads the main action and key requirement. It is not verbose, though could be slightly restructured for even faster parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core action, prerequisite, and outcome, but lacks details on error handling, return value, or validation. Given the tool's simplicity and lack of output schema, it is minimally adequate but not fully comprehensive.
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 only one parameter (xml) and 0% schema coverage, the description adds meaning by stating 'with the given XML', clarifying the parameter's role as the process XML content. No additional details on format or constraints, but adequate for a single string param.
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 'replace' and the resource 'process currently open in the Altair AI Studio GUI', and distinguishes the tool from siblings like altair_get_current_process (read) or altair_run_process_file (run without human interaction) by emphasizing handing control back to the user.
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 mentions a requirement ('requires the HTTP bridge extension'), but does not explicitly state when to use this tool versus alternatives, such as altair_get_current_process for reading or altair_run_process_file for batch execution. Usage context is implied but not formally guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It indicates the tool reads XML (non-destructive) and requires an extension, but does not explicitly declare it as read-only or safe. The behavior is clear, but lacks explicit confirmation of no 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 two sentences with no wasted words. It front-loads the main action and adds a practical usage note. Every sentence contributes meaning.
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 (no parameters, no output schema), the description provides adequate context: purpose, prerequisite, and use case. It does not describe the return format or size, but for a read operation, this is acceptable. Slight improvement could be a note on the response structure.
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 input schema has no parameters, so schema coverage is effectively 100%. The description does not need to add parameter information, earning a baseline of 4 for zero-parameter tools.
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 ('Read the XML') and the resource ('process currently open in the Altair AI Studio GUI'). It differentiates from siblings by focusing on the GUI state rather than repository or file operations.
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 the requirement ('requires the HTTP bridge extension') and provides use case ('useful to see what the human user is working on before suggesting changes'). It implies when to use but does not explicitly state when not to use or list alternatives, though the sibling set suggests alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It states the return value (performance vector) but does not disclose side effects (e.g., whether the model is saved or if the training set is modified). This leaves some behavioral ambiguity.
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 two sentences, each serving a distinct purpose: the first defines functionality, the second specifies return and scope. No wasted words.
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 absence of an output schema, the description adequately explains the return type (performance vector). It covers the main purpose and key parameters, though it could mention prerequisites (e.g., labelAttribute must exist in data) or outcome of training.
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 60%, and the description adds value by mapping the learner enum to human-readable model names and explaining the folds parameter in the context of k-fold cross-validation. It also clarifies the relationship between csvFile and repositoryEntry.
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 identifies the tool as training and evaluating classification models with k-fold cross-validation, listing eight specific model types. This verb+resource combination is distinct from sibling tools like clustering or association rules.
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 provides context by mentioning the covered actions (46-55, 66-74) and supervised learning + validation, implying when to use. However, it lacks explicit exclusions or alternatives, such as mentioning when to use other classification-related sibling tools.
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 must disclose behavioral traits. It describes what it checks but does not detail return format or potential errors. However, it is sufficient for a diagnostic read-only tool.
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, two sentences with no waste. It front-loads the purpose and provides a usage recommendation.
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 is adequate for a simple diagnostic tool with no parameters. It lacks output format details, but the context of 'check connection' implies a status result.
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?
No parameters exist, so schema coverage is 100%. The description does not need to add parameter information. Baseline 4 is appropriate.
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's purpose: checking connectivity to Altair AI Studio 2026.0.5 by verifying two specific components. It is distinct from sibling tools which focus on data operations.
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?
Explicitly advises to run this first when troubleshooting, providing clear usage context. Does not mention exclusions or alternatives, but the sibling tools are clearly different.
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 provided, so description carries full burden. It discloses the need to write results via write_csv and implies the tool executes arbitrary graphs, which could have side effects. Missing details on error handling or performance, but sufficient for typical usage.
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 two-sentence description that front-loads 'ADVANCED / escape hatch' to set expectations. Every sentence adds value: purpose, when to use, tip, requirement. No wasted words.
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 complexity (nested objects, no output schema, no annotations), the description provides enough context: purpose, usage guidance, a key requirement, and a practical tip. It does not explain the return value, but the write_csv instruction implies the result is in the file. Overall, well-rounded.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description adds context by mentioning 'operator class keys, parameters, and port-level connections' and the tip about Export Process. However, it does not detail each parameter structure beyond what schema provides, leaving some interpretation for agents.
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 is an advanced escape hatch to run arbitrary operator graphs, distinguishing it from dedicated sibling tools by listing examples like DBSCAN, Hugging Face, etc. The verb 'run' and resource 'operator graph' are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this for operators not covered by the dedicated tools' and provides a practical tip on exporting from Altair AI Studio. Also states a critical requirement: the graph must end with write_csv to an absolute path.
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 full burden. It discloses the requirement for the HTTP bridge extension and the repository living inside a running Studio session, which is important behavioral context. It does not mention side effects or return format, but adds value beyond the schema.
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: purpose, requirement, and examples. It is concise, front-loaded, and every sentence adds value without waste.
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 a single optional parameter and no output schema, the description adequately covers purpose, prerequisite, and parameter usage. It could be more complete by mentioning return format or error behavior, but for a simple list tool it is sufficient.
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 description coverage is 0%, so the description must compensate. It adds significant meaning by providing example paths (e.g., '//Local Repository' or '//Samples/data') and indicating the format, which clarifies the parameter's purpose beyond the schema's default value.
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 folders/entries in the Altair AI Studio repository, with a specific verb and resource. It distinguishes from sibling tools like altair_read_repository_entry or altair_store_csv_to_repository which perform different operations.
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 provides context (requires HTTP bridge extension, lives inside running Studio session) and example paths, guiding when to use the tool. However, it does not explicitly state when not to use or mention alternatives, leaving some room for improvement.
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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