dashai-mcp

Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Each tool targets a distinct resource and action: server health, dataset summaries, dataset details, component catalog, training launch, job polling, run listing, run details, and prediction. No two tools overlap in purpose; list vs describe and list vs get are clearly summary vs detail pairs.
Naming Consistency4/5
All tools share a consistent 'dashai_' prefix, and most use a verb_noun pattern (list_datasets, describe_dataset, train_model, get_run). Exceptions are 'server_info' and 'job_status' which are noun_noun, and 'predict' which is verb-only, but the pattern is still predictable and readable.
Tool Count5/5
With 9 tools, the set is well-scoped and each tool serves a distinct step in the ML workflow. No unnecessary duplication exists, and the count is within the ideal range for a focused server.
Completeness4/5
The toolset covers the core train-predict lifecycle comprehensively: dataset exploration, component lookup, async training, job polling, run inspection, and prediction. Minor gaps like no job listing or dataset deletion are acknowledged and do not block the primary use cases.
Average 4.6/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only and idempotent, but the description goes further by disclosing that it merges four API calls and that missing parts are returned as null instead of failing the whole request. This adds meaningful behavioral context beyond the annotations.
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 well-structured with a purpose statement, rationale, Args, and Returns sections. It is slightly longer than necessary but each sentence serves a purpose, and the key information is front-loaded in the first sentence.
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?
The description covers the tool's purpose, why it exists, parameter semantics, return format, and partial-failure behavior. With supportive annotations and a clear output schema, it is fully sufficient for an agent to select and invoke 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?
The input schema already provides rich descriptions for both parameters (e.g., include_sample says 'Include ~10 sample rows. Set to false if the dataset has very wide columns.'). The description's Args section merely restates the schema without adding new meaning, so it does not elevate above the baseline.
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 opens with 'Returns everything needed to configure a training run over a dataset,' clearly stating the specific verb (returns) and resource (dataset). It also explains that it aggregates four raw API endpoints, which distinguishes it from sibling tools like dashai_list_datasets and dashai_train_model.
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 conveys when to use this tool: when deciding which columns are inputs and outputs for training requires seeing them together. It gives clear context but does not explicitly name alternatives or state when-not-to-use conditions, falling slightly short of a 5.
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds return format and filtering context, but does not disclose additional behavioral traits such as rate limits or auth needs. This is acceptable given the strong annotations, but not exemplary.
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 compact and well-organized: a clear purpose statement, a usage hint, a structured Args section, and a Returns section. Every sentence adds value, and the most important information (what it does) is front-loaded.
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 straightforward list tool, the description covers purpose, usage context, parameters, and return shape. It is complete enough for an agent to select and invoke the tool without needing external documentation, especially with strong annotations and a simple schema.
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 context showing 0% schema description coverage, the description explicitly explains both parameters: model_session_id as 'filter by experiment' and limit as 'maximum to return, 1-200 (default 50)'. This fully compensates for the schema gap and adds practical meaning beyond parameter names.
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 opens with 'Lists the recorded training runs, with their status,' which clearly states the action (list) and resource (training runs). It also distinguishes from siblings by focusing on training runs and comparing models within an experiment, avoiding confusion with list_datasets or get_run.
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 phrase 'Useful for comparing models trained within the same experiment' provides a clear context for when to use this tool. It implies usage for browsing runs and filtering by experiment, but does not explicitly exclude alternatives like get_run for single-run details, so it misses the top score.
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?
Annotations already declare readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds meaningful context by explaining the status values (not_started, started, finished, error) and their practical interpretation, which helps the agent decide whether to keep polling.
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 compact and well-structured: a clear purpose line, a brief but valuable status explanation, then Args and Returns sections. Every sentence adds necessary information with no filler or repetition.
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?
This is a simple polling tool, and the description covers purpose, parameter source, return format, and status interpretation. With annotations providing the safety profile, there is no critical missing context.
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?
Although schema_description_coverage is reported as 0%, the description explicitly documents the 'job_id' parameter and clarifies that it is returned when enqueuing. The schema also includes a similar description, so the parameter's meaning is fully covered and not just left to schema alone.
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 opens with 'Polls the status of an enqueued job', using a specific verb and resource. It also lists examples (training, prediction, explanation), which distinguishes it from sibling tools like dashai_train_model or dashai_predict.
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 clear context on when polling is useful, explicitly stating that a 'started' status is worth waiting on while 'error' will not improve with further polling. This gives implicit when-to-use and when-not-to-use guidance, though it does not explicitly name alternative 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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context beyond those: the catalogue is plugin-dependent, names cannot be guessed, and an unfiltered call returns a long result. It also documents the return JSON structure, including the 'schema' field, which is not present in the annotations.
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 well-structured: a one-sentence purpose, a high-value usage note, and compact Args/Returns sections. It is not overly long and every sentence carries useful information, though the Args section partly repeats schema content.
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, the description covers purpose, when to use it, the parameter, the return format, and a critical behavioral caveat (plugin-dependent catalogue). It is complete for an agent to invoke the tool correctly without additional context.
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 only parameter, 'params', is described in an Args section, and the 'types' filter's valid values are listed. While the input schema's description for 'types' already provides the same valid values and the long-output caveat, the tool description nonetheless clarifies the structure of the 'params' wrapper and the meaning of the returned 'schema' field, adding value 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 opens with 'Lists the registered components: models, metrics, tasks and optimizers', using a specific verb and enumerating the resource types. This clearly distinguishes the tool from sibling tools like dashai_list_datasets and dashai_describe_dataset.
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?
It explicitly states 'ALWAYS use this before dashai_train_model' and explains that names are exact/case-sensitive and the catalogue changes with installed plugins. This provides strong when-to-use guidance and a rationale for why the tool must be called first, which is more than typical.
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?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, so the description doesn't need to restate these. It adds value by detailing the exact return fields (id, name, date, status) and the empty response behavior, which are not evident from annotations alone.
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 well-structured with a clear summary, sections for args and returns, and a bullet-point parameter explanation. Every sentence adds value and there is no redundant fluff.
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 optional parameter), the description covers the purpose, usage, return format, and empty-case behavior. It also references a sibling tool for deeper inspection, making the description fully self-contained for an 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?
The description explains the 'limit' parameter with its range (1-200) and default (50), which compensates for the low schema description coverage (0% as per context). It provides enough detail for an agent to use the parameter correctly without relying solely on 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 uses a specific verb ('Lists') and a clear resource ('datasets loaded in dashAI'). It clearly distinguishes from sibling tools like dashai_describe_dataset by specifying it returns only summary fields, making it easy for an agent to choose this tool for overview purposes.
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?
The description explicitly states when to use this tool ('just enough to pick one') and directs the user to dashai_describe_dataset for detailed column information. It also explains the behavior when no datasets exist, giving clear context for expected use cases.
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?
The description adds significant behavioral context beyond the annotations: it explains the asynchronous nature, the need to poll with dashai_job_status, the FINISHED status requirement, and the exact JSON return format. It also notes that the request is rejected if the run is not finished. This fully discloses the tool's behavior.
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. It opens with a clear one-sentence summary, then provides async behavior, precondition, arguments, and return format in a logical order. No unnecessary filler exists.
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 tool with one parameter and rich annotations, the description covers all essential aspects: what it does, when to use it, how to handle the asynchronous job, the required run status, and the exact response structure. It is complete enough for an 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?
The input schema has 0% coverage on the top level, and the description compensates by explaining the 'params' object and the 'run_id' parameter, including that it must be a finished run. It reiterates the schema's description but adds context about the job_id and return payload. However, it doesn't add much beyond the schema's own field description for run_id.
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 action: 'Enqueues a prediction using the model of an already finished run.' This uses a specific verb ('enqueues') and resource ('prediction using a model'), and it distinguishes from sibling tools like dashai_train_model (training) and dashai_job_status (status checking).
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 clear context for when to use the tool: after a run has finished, and it explains the asynchronous workflow ('returns a job_id and the result is followed with dashai_job_status'). It also states a prerequisite (run must be FINISHED) and the rejection behavior. It does not explicitly mention alternatives, but the use case is well-defined.
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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering safety. The description adds the exact return JSON schema and failure response format, giving deeper insight into behavior beyond the 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 well-structured and front-loaded with the purpose, followed by usage timing and a clear return schema. Every sentence earns its place, and the failure format is stated compactly.
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 simple diagnostic tool with no arguments, the description fully covers purpose, when to use, return values, and error handling. The embedded output schema means no return value ambiguity remains, making it complete for this context.
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 schema includes a single NoArgs parameter with no properties, but the description explicitly states 'params (NoArgs): no parameters,' confirming the tool takes no meaningful arguments. This adds clarity beyond the raw schema and fully compensates for the 0% coverage since there are no real 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 'Checks that dashAI is running and summarizes what is loaded,' using a specific verb and resource. It distinguishes itself from sibling tools by positioning it as the first call for diagnostics, which is unique among the listed tools.
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 instructs to 'Call this FIRST when something fails or when you do not know whether the backend is up,' and explains how it disambiguates two failure types. This provides strong when-to-use guidance and implicitly contrasts with other tools.
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?
Annotations provide readOnlyHint=false, destructiveHint=false, idempotentHint=false, but the description adds critical behavioral context: it enqueues a job and returns immediately, does not wait, training can take minutes/hours, and it collapses three raw API calls. Error behavior is also described ('On failure: "Error: ..." stating which parameter dashAI rejected'). This goes well beyond annotation data.
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 structured with clear paragraphs: function, async behavior, API collapsation, Args, Returns, Examples. Every sentence adds value, but the Args section largely duplicates schema information and makes the description longer than strictly necessary. Still, it is front-loaded with the most important usage rules and alternative references.
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 complexity (async, multi-step training, many parameters, output schema), the description covers all necessary context: how it works, how to poll, what returns, error handling, and how it differs from siblings. It even provides usage examples. No significant gap remains.
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 schema already has detailed descriptions for nested TrainModel properties. The description's Args section condenses these into a readable list and adds context by explaining that the parameters map to the three API calls (session, run, job). It also notes 'proportions adding up to 1.0' for splits, reinforcing the schema. This adds semantic clarity beyond the raw schema, especially given the top-level schema coverage is 0%.
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?
Description opens with 'Trains a model on a dataset and returns the id of the enqueued job.' This is a specific verb+resource statement that clearly distinguishes the tool from siblings like dashai_get_run ('Do not use it to read results') and dashai_job_status (polling). It also details the collapsed API calls, leaving no ambiguity about what the tool accomplishes.
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 states when to use: 'It does NOT wait for it to finish... progress is polled with dashai_job_status.' Provides a clear exclusion: 'Do not use it to read results: that is dashai_get_run, with the run_id.' This gives direct when/when-not guidance relative to siblings.
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 annotations already declaring readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, the description adds important behavioral context: metrics are empty for unfinished runs (not an error) and the return format (JSON with model parameters, status, and per-split metrics). This enhances transparency beyond the 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: a one-sentence purpose, followed by a use-case note, and clearly labeled Args/Returns sections. Every sentence adds value, and the format is easy to scan.
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 simple read-only tool with one parameter and an output schema, the description fully covers the relevant context: the return value's structure, the dependency on run completion, and the absence of errors for empty metrics. No significant gaps are present.
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%, so description must compensate. It explains the nested 'params' structure containing run_id and describes run_id as the run identifier. While it repeats the schema's 'run id' description, it clarifies the nesting and provides enough context for a single-param tool. It could add how to obtain run_id (e.g., from list_runs), but it's sufficient.
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 states a specific action and resource: 'Returns the configuration and metrics of a training run.' It clearly differentiates from siblings like dashai_job_status (status only) and dashai_list_runs (list only) by focusing on full run details. The tool's role is unambiguous.
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?
Explicit guidance is provided: 'This is where results are read once dashai_job_status says `finished`.' This instructs when to use it and implies the alternative workflow with job_status, while also warning that incomplete runs yield empty metrics. This is clear usage direction.
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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