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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds a performance caveat about large runs, which is useful 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.

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

Two concise sentences with no filler; the key action and a critical warning are front-loaded. Every word serves a purpose.

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

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

The description hints at output ('full metrics and params') but does not fully describe return values or behavior. With no output schema and missing parameter details, the description is adequate but not comprehensive for a tool with two required parameters.

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

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

Schema coverage is 0%, so the description must compensate. It mentions 'full metrics and params' but does not explain the role of 'experiment_id' or 'run_ids', nor their formats. The warning about keeping the list short hints at run_ids but lacks clarity.

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

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

The description uses a specific verb ('compare') and resource ('runs') and adds 'side-by-side with full metrics and params', which clearly distinguishes it from siblings like 'get_run' (single run) or 'query_runs' (search).

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

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

The description provides a clear performance warning: 'Runs can be large — keep the list short.' This guides the agent on best practices. However, it does not explicitly mention when to use alternatives or when not to use the tool.

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

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

Annotations indicate readOnlyHint=false, and the description confirms it's a write operation. It discloses that the destination model is created if missing and the promotion pattern. However, it does not clarify whether the source version remains unchanged or if it's a copy vs move.

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

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

The description is concise and includes an example. The Args section somewhat duplicates schema but adds clarity. No extraneous content.

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

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

Given no output schema, the description adequately covers purpose, parameters, and an example. For a simple mutation tool with 3 required params, it is sufficiently complete.

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

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

Schema has 0% description coverage, but the description's Args section and example add meaning to each parameter (source model/version, destination model). The example demonstrates usage.

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

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

The description clearly states it promotes a model version to another registered model, with the MLflow 3 promotion pattern and auto-creation of destination. This is distinct from siblings like delete_model_version or register_model.

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

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

The description mentions the MLflow 3 promotion pattern but does not explicitly state when to use this tool versus alternatives like transition_model_version_stage. No when-not-to-use guidance is provided.

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

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

Annotations already provide destructiveHint=true, but the description adds valuable context: the delete is a soft delete (move to 'deleted' stage and recoverable). This goes beyond the annotations and helps the agent understand the true behavior.

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

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

The description is concise: two short paragraphs. The first paragraph front-loads the purpose and behavior. The second paragraph lists the argument. Every sentence adds value without redundancy.

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

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

For a simple tool with one parameter and no output schema, the description covers the purpose, effect on runs, lifecycle stage, and recoverability. It does not mention return values, but that is acceptable given the tool's simplicity.

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

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

The single parameter 'experiment_id' is merely restated as 'The experiment ID to delete' without additional format, validation, or example. With 0% schema description coverage, the description adds minimal value beyond the schema itself.

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

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

The description clearly states the verb 'delete', the resource 'experiment', and specifies it moves to a 'deleted' lifecycle stage, which distinguishes it from other delete tools like delete_run. It explains the soft-delete behavior, 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.

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

The description provides clear context: the experiment will not be shown in UI or queries but is recoverable. This guides the agent on when to use it. However, it does not explicitly state when not to use it or mention alternatives among sibling tools.

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

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

Annotations indicate destructiveHint=true, and the description confirms the alias is permanently removed but the model version is not affected. This adds useful context beyond annotations without contradiction.

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

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

Two sentences plus an Args block, front-loaded with the main action. Every sentence is necessary and informative.

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

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

Given the low complexity (2 required params, no output schema), the description covers the effect, usage, and parameter meaning completely. No gaps.

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

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

Schema coverage is 0%, but the description explains both parameters: 'name' as 'Name of the registered model' and 'alias' as 'Alias to remove, e.g. champion, production'. This compensates for missing schema descriptions.

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

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

The description clearly states 'Remove an alias from a registered model' with examples like 'revoke champion'. It distinguishes from sibling tools by specifying it only removes the alias, not the model version.

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

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

The description implies when to use (to revoke an alias) and provides parameter guidance in the Args block. It does not explicitly mention when not to use or compare to alternatives, but it is clear enough.

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

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

Annotations already declare destructiveHint=true, and the description reinforces this by stating 'Irreversible.' It adds minor detail about metadata loss, but does not disclose other behavioral traits like permissions or cascading effects.

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

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

The description is extremely concise: two sentences and a bulleted list. Key information is front-loaded, and every sentence adds value without redundancy.

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

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

Given the tool's simplicity, annotations, and lack of output schema, the description adequately covers what the tool does and its irreversible nature. It could mention prerequisites or error conditions, but it is largely complete for a delete operation.

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

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

With 0% schema description coverage, the description provides basic explanations for the two parameters. However, these explanations merely restate the parameter names ('Name of the registered model'; 'Version number to delete') and add no additional format or context beyond the schema.

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

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

The description clearly states it deletes a specific model version and emphasizes irreversibility. It effectively distinguishes from siblings like delete_registered_model, which deletes the entire model, or copy_model_version.

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

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

The description implies when to use (when a specific version needs deletion) but does not explicitly provide when-not-to-use or alternative tool suggestions. Usage context is implied by the name and sibling list.

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

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

Annotations already indicate the tool is destructive. The description adds context: 'Irreversible — all versions, aliases, and tags are permanently removed,' which explains the scope of destruction beyond the annotations. However, it does not mention error behavior 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.

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

The description is very concise: two sentences for purpose and behavior, plus a brief parameter doc. It is front-loaded with the main action and irreversible nature, with no redundant information.

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

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

For a simple deletion tool with one parameter and no output schema, the description covers the key aspects: what is deleted, irreversibility, and what is affected. It could mention error handling for non-existent models, but overall it is complete enough.

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

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

Schema coverage is 0%, so the description must compensate. The parameter 'name' is explained as 'Name of the registered model to delete,' which provides meaningful context beyond the schema's type string. This is sufficient for a single string parameter.

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

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

The description clearly states 'Delete an entire registered model and all its versions' with a specific verb and resource, distinguishing it from sibling tools like delete_model_version (only a single version) and delete_model_alias (only an alias).

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

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

The description implies usage for complete removal, but does not explicitly state when not to use or suggest alternatives (e.g., delete_model_version for a single version). The destructive hint in annotations is clear, but guidance is minimal.

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

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

Annotations already mark destructiveHint=true. The description adds important behavioral context: the delete is a soft delete (movement to 'deleted' stage) and recoverability, which goes beyond what annotations provide. No contradictions.

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

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

The description is concise with two sentences: the first defines the action and its key effect, the second provides parameter details. It is front-loaded and efficient.

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

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

Given the simple operation, the description covers purpose, behavioral effect, and parameter. No output schema exists, but the description does not need to explain return values. Annotations provide destructive hint.

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

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

The schema has 0% description coverage, but the description explicitly documents the run_id parameter: 'The run ID to delete.' This adds meaning beyond the bare schema definition.

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

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

The description clearly states the tool deletes a run and explains it moves it to the 'deleted' lifecycle stage, distinguishing it from a hard delete. The verb 'delete' is specific and the resource 'run' is unambiguous, setting it apart from sibling delete tools like delete_experiment.

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

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

The description indicates when to use the tool (to delete a run) and explains the consequence (not shown in UI but recoverable). It provides enough context for an agent to decide, though it does not explicitly list alternatives or contraindications.

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

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

Annotations already declare readOnlyHint=true, and the description aligns with this. The description adds the behavioral constraint that it only works for text/json files, which is valuable beyond the annotation. However, no other behaviors (e.g., file size limits, error handling) are disclosed.

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

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

The description is extremely concise with a single phrase, containing no redundant information. It is front-loaded with the core action. However, it could be slightly more structured with separate sentences for purpose and parameter hints.

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

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

Given the tool's low complexity (read only, 2 params, output schema exists), the description covers the basic purpose and file type constraint. However, it lacks parameter documentation and sibling differentiation, making it incomplete for effective agent selection.

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

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

With 0% schema description coverage, the description should explain the two required parameters ('run_id', 'artifact_path'). It does not define what they represent, their formats, or constraints. The description only adds context about file type but not parameter details.

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

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

The description clearly states the verb 'Read and return' and the resource 'artifact content', with a specific file type qualifier 'for text/json files'. This distinguishes it from binary artifacts but not clearly from sibling 'get_run_artifact' which may serve a similar purpose.

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

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

No guidance is provided on when to use this tool versus alternatives like 'get_run_artifact' or 'get_run_artifacts'. The description does not mention prerequisites, limitations, or exclusions.

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

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

Annotations declare readOnlyHint=true, consistent with 'Get'. Description adds useful context about metric names with special characters, which is not in annotations. However, lacks details on edge cases (e.g., no runs, ties).

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

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

Two concise sentences, front-loaded with purpose, second sentence adds an edge case. No redundancy or unnecessary words.

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

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

No output schema, and description does not specify return format or behavior in ambiguous cases (ties, missing metric). Adequate for basic use but incomplete for complex scenarios.

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

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

Schema coverage is 0%, but description only explains the 'metric' parameter with an example. 'experiment_id' and 'ascending' are not elaborated, leaving the agent underinformed about required inputs.

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

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

Clearly states the tool retrieves the best run by a specific metric, distinguishing it from siblings like 'get_run' and 'get_runs'. Also notes handling of special characters in metric names, adding specificity.

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

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

Provides a clear use case (getting best run by metric), but does not explicitly state when to avoid this tool or contrast with alternatives like 'get_run_metrics' or filtering.

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

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

Annotation already declares readOnlyHint=true, and the description adds the behavioral aspect of using name as identifier. However, it lacks disclosure of what constitutes 'details' or any error conditions.

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

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

The description is a single concise sentence that is front-loaded, containing no extraneous words.

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

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

For a simple read-only tool with no output schema, the description is incomplete as it omits what 'details' are returned (e.g., metadata, metrics) and lacks guidance on error cases.

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

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

With schema description coverage at 0%, the description must compensate but provides no additional meaning about the 'name' parameter beyond its existence, failing to mention format, constraints, or examples.

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

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

The description uses the specific verb 'Get' and resource 'experiment details', and differentiates the tool by the input method 'by name' compared to presumably ID-based alternatives among sibling tools.

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

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

The description implies convenience over ID-based retrieval but does not explicitly state when to use this tool versus alternatives like get_experiments or search_experiments.

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

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

The description aligns with the readOnlyHint annotation, indicating a safe read operation. It adds context about uniqueness and scope (across runs), though no details on error behavior or limits.

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

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

The description is a single concise sentence, front-loading the purpose with no unnecessary words.

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

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

For a simple tool with one parameter and an output schema, the description adequately covers the core function. Minor gaps include missing parameter details and edge cases, but overall complete enough.

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

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

The single parameter 'experiment_id' is described only by its title; the tool description does not add any semantic detail, format, or example, and the schema has 0% description coverage, so the description fails to compensate.

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

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

The description clearly states it gets unique metric names across all runs in an experiment, which distinguishes it from sibling tools like get_run_metrics (for a single run) and get_experiment_params (for parameter names).

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

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

The description implies use when needing metric names across runs, but does not explicitly contrast with alternatives or mention when not to use it. Given many sibling tools, some guidance would help.

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

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

Annotations already indicate readOnlyHint=true, so the description's statement 'Get' aligns. It adds the detail 'unique' which is useful, but no further behavioral traits (e.g., error handling, order) are disclosed.

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

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

Single, well-structured sentence with no redundant information. Every word contributes to the meaning.

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

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

For a simple read tool with an output schema, the description covers the main functionality adequately. It does not address edge cases (e.g., empty experiment) but is complete enough for typical use.

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

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

Schema description coverage is 0%, so the description should compensate. While the parameter name experiment_id is self-explanatory and the tool description implies its role, there is no explicit parameter documentation. This is minimally sufficient.

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

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

Description states the action 'Get' and the resource 'all unique parameter names used across all runs in an experiment', clearly specifying the scope and output. This distinguishes it from siblings like get_experiment_metrics and get_runs.

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

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

No explicit guidance on when to use this tool vs alternatives. It does not mention use cases or exclusions, but the purpose is clear enough for basic selection.

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

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

The description adds no behavioral detail beyond the readOnlyHint annotation. It does not mention return format, pagination, or potential performance implications for retrieving all experiments.

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

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

Single sentence, front-loaded, no wasted words. Perfectly concise for a simple retrieval tool.

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

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

Despite having an output schema, the description does not explain what fields are returned or how the data is structured. For a tool that returns all experiments, more context (e.g., 'returns list of experiment objects with id, name, etc.') would be helpful.

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

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

With zero parameters and 100% schema coverage, the description provides the essential meaning ('Get all experiments'). No parameter details are needed, so the description adequately compensates.

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

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

The description states 'Get all experiments' which clearly identifies the verb (get) and resource (experiments). It implicitly distinguishes from sibling tools like get_experiment_by_name or search_experiments that target specific subsets.

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

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

No guidance on when to use this tool vs alternatives like search_experiments or get_experiment_by_name. Given the large number of sibling tools, explicit usage context is missing.

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

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

Annotations include readOnlyHint=true, which the description aligns with. However, the description adds no additional behavioral context beyond what is already conveyed by the annotation, such as performance implications or error conditions.

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

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

The description is a single, front-loaded sentence with no unnecessary words. Every word contributes meaning.

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

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

Given the simple one-parameter tool with an output schema, the description adequately states purpose but lacks usage context, parameter details, or behavioral notes. It is minimally sufficient.

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

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

Schema description coverage is 0%, yet the description does not elaborate on the only parameter (experiment_id). It implies its role by mentioning 'an experiment' but provides no format, example, or constraints.

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

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

The description clearly states the action ('Get'), the resource ('unique tag keys'), and the scope ('across all runs in an experiment'). It effectively distinguishes from sibling tools like get_experiment_metrics or get_experiment_params.

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

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

No guidance is provided on when to use this tool versus alternatives such as get_experiment_metrics or search_runs_by_tags. The description does not specify prerequisites or typical use cases.

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

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

The description is consistent with the readOnlyHint annotation, indicating a read operation. It adds context about returning the latest version per stage, which is beyond what annotations provide. No contradictions are present.

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

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

The description is concise (two sentences plus bulleted arguments), front-loads the purpose, and includes only essential information. Every component contributes to understanding, with no redundant text.

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

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

Given the presence of an output schema (which does not require return value documentation), the description covers inputs well. It lacks mention of potential edge cases (e.g., no version for a stage), but overall it is fairly complete for a simple retrieval tool.

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

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

With 0% schema description coverage, the description fully documents both parameters: 'name' is described as the registered model name, and 'stages' is explained with examples and default behavior. This adds significant meaning beyond the schema's type-only definitions.

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

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

The description clearly states the action 'Get' and the resource 'latest model versions for each stage', with concrete examples like 'Staging' and 'Production'. This distinguishes it from siblings such as get_model_version and get_model_versions, which serve different purposes.

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

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

The description explains the tool's purpose and behavior (e.g., filtering by stages, returning latest versions). It provides usage instructions for the 'stages' parameter, including the default behavior when None. However, it does not explicitly state when to use this tool over alternatives, though the purpose clearly differentiates it from sibling tools.

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

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

Annotations already declare readOnlyHint=true, so the description doesn't need to restate read-only. It adds the source of the model ID but no additional behavioral traits (e.g., no mention of permission 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.

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

Extremely concise: two sentences that front-load the main purpose and include a bullet for the parameter. No extraneous words.

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

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

With no output schema, the description only says 'detailed information', which is vague. It does not describe the returned fields or structure. While the tool is simple, an agent may benefit from knowing what properties are returned.

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

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

Schema description coverage is 0%, but the description adds a meaningful hint about the parameter source ('obtained from search_logged_models results'), partially compensating for the lack of schema-level description. However, it doesn't specify format or constraints.

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

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

Description clearly states the verb 'Get' and the resource 'logged model', specifying the unique identifier (model ID) and its source from search_logged_models results. This distinguishes it from siblings like get_model_version or get_registered_model.

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

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

The description provides clear context by stating that the model_id is obtained from search_logged_models results, giving a prerequisite. However, it does not explicitly exclude alternatives or state when not to use this tool.

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

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

The annotation readOnlyHint=true already indicates a safe read operation. The description adds value by specifying the returned fields (metrics, stage, run_id). No contradictions and no missing behavioral cues like error conditions or permissions for a simple getter.

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

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

A single sentence with no superfluous words. The key information (action, resource, returned data) is front-loaded and immediately useful.

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

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

Given the simplicity of the tool and the presence of readOnlyHint, the description is adequate but not comprehensive. It lacks guidance on distinguishing from similar siblings like 'get_model_version_by_alias' and does not mention what happens if the version does not exist.

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

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

Schema description coverage is 0%, so the description must compensate. It does not explain the meaning or format of 'model_name' or 'version' beyond implying they identify the version. No additional constraints or examples are provided.

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

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

The description clearly states the verb 'get', the specific resource 'model version', and lists the details returned (metrics, stage, run_id). It differentiates from sibling tools like 'get_model_versions' (plural) and 'get_latest_versions'.

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

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

No explicit guidance on when to use this tool versus alternatives. The description implies usage when you have a specific version identifier, but does not mention disambiguation from tools like 'get_model_version_by_alias' or 'get_latest_versions'.

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

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

Annotations already declare readOnlyHint=true, so the read-only nature is clear. The description adds that it retrieves a single version by alias but doesn't mention potential errors (e.g., alias not found) or return structure. Adds minimal extra 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.

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

The description is short and well-structured as a docstring with an Args section. Every sentence provides value, and the purpose is front-loaded. No wasted words.

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

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

The tool has no output schema, and the description does not describe the return value. For a retrieval tool, hinting at the return structure (e.g., 'returns a model version object') would improve completeness. Input parameters are well-covered, but the overall description is slightly incomplete without return info.

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

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

Input schema has 0% description coverage, so the description fully compensates by explaining each parameter: 'name: Name of the registered model.' and 'alias: The alias assigned to the version, e.g. ''champion''.' This adds clear meaning beyond the schema's property titles.

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

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

The description clearly states the verb ('Get') and the resource ('a model version by its alias'), with an example alias ('champion', 'production'). It distinguishes from sibling tools like get_model_version (by version number) and get_latest_versions (all versions).

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

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

The description implies usage by providing alias examples but does not explicitly state when to use this tool vs alternatives like get_model_version or get_latest_versions. No when-not-to-use guidance is given.

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

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

The description adds no behavioral information beyond the annotation 'readOnlyHint: true'. It does not disclose properties like sorting order, pagination, or whether deleted versions are included. The annotation covers the read-only aspect, but the description fails to provide additional context.

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

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

The description is concise (one sentence, no fluff). However, it sacrifices completeness for brevity, omitting essential usage details. It is appropriately sized but could be improved without added length.

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

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

Given the simplicity (1 param, readOnly annotation, output schema exists), the description is still insufficient. It lacks context about the output (list of version objects), ordering, error handling, and how to distinguish from sibling tools. The output schema existence reduces the need to explain return values, but the description remains too sparse.

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

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

The only parameter 'model_name' has 0% schema description coverage and the tool description does not explain it beyond the schema's title 'Model Name'. No details about format, uniqueness, or expected values are provided, leaving the agent with no extra semantic understanding.

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

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

The description 'Get all versions of a registered model' clearly states the action (get) and resource (all versions of a model). However, it does not differentiate from siblings like 'get_model_version' (single version) or 'get_latest_versions' (only latest), which could cause confusion.

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

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

No guidance is provided on when to use this tool versus alternatives. For example, it does not mention that it returns all versions (including non-latest) or what to do if the model doesn't exist. A sibling such as 'get_model_version' suggests a single version lookup, but no explicit when-to-use information is given.

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

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

Annotations already declare readOnlyHint=true, so the read-only behavior is covered. The description adds the important detail that the tool returns None if the run has no parent, which is a key behavioral trait not captured by annotations.

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

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

Extremely concise: two short sentences that state the purpose and parameter explanation. No unnecessary words.

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

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

For a simple tool with one parameter, readOnly annotation, and an output schema (presumably defining the return type), the description is sufficiently complete. It could optionally mention the return type (e.g., 'Run object'), but the output schema likely handles that.

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

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

Schema description coverage is 0%, so the description must compensate. It clearly explains the single parameter 'run_id' as 'The child run ID to find the parent for', adding semantics beyond the schema's title and type.

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

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

Description clearly states the tool retrieves the parent run of a nested run, with a specific verb ('Get') and resource ('parent run'). It also specifies the return behavior when there is no parent ('Returns None'), which distinguishes it from other run-related tools like get_run.

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

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

No explicit guidance on when to use this tool versus siblings such as get_run or query_runs. The description implies usage for parent retrieval, but does not specify prerequisites or alternatives.

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

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

Annotations already declare readOnlyHint=true, so the description's safety profile is covered. The description adds that the result can be large, which provides useful context beyond the annotation.

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

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

Two sentences, front-loaded with the main purpose, and a concise caveat. No redundant information.

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

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

Given the simple tool with one parameter and no output schema, the description covers the essential purpose and a size warning. Lacks return value description but is otherwise sufficient for selection.

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

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

The only parameter `name` is described as 'Name of the registered model,' which adds minimal meaning beyond the schema's type and title. With 0% schema coverage, the description should offer more value but does not.

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

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

Describes the tool as 'Get full details of a registered model including all versions and aliases,' which is a specific verb-resource combination with clear scope. It distinguishes from siblings like `get_model_version` and `get_registered_models`.

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

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

No explicit guidance on when to use this tool versus alternatives. The caveat 'Can be large for models with many versions' implies a caution but does not specify when to avoid 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.

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

Annotations already indicate readOnlyHint=true, confirming a safe read operation. The description adds the 'all' scope but does not disclose any behavioral traits like pagination, sorting, or performance implications. For a tool with no parameters and an output schema, the description is adequate but could provide more context about the response structure or limitations.

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

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

The description is a single, clear sentence with no extraneous information. It effectively conveys the tool's purpose without wasting words.

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

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

Given the tool's simplicity (no parameters, no nested objects, output schema present), the description is sufficiently complete. It covers the core functionality of listing all registered models, and the output schema can define the return format.

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

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

The tool has zero parameters, so the description inherently covers the parameter semantics by implying no input is needed. Schema description coverage is 100% (no params to describe). The description adds no additional parameter meaning because none exists, which is appropriate.

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

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

The description clearly states the action ('List all registered models') and the resource ('in the model registry'). It uses a specific verb and identifies the exact scope, effectively distinguishing it from sibling tools like get_registered_model (single model) or search_logged_models (different search criteria).

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention that this tool returns all models without filtering, whereas siblings like search_logged_models or get_registered_model might be more appropriate for specific lookups. An explicit 'when to use' note would improve decision-making.

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

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

The description adds a performance warning about potentially large data, which goes beyond the readOnlyHint annotation. No contradictions were found.

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

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

The description is extremely concise with two purposeful sentences: one for purpose, one for caution. No redundant information.

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

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

For a simple read-only tool, the description covers purpose and a performance note. However, it lacks specifics about the returned 'detailed information' (no output schema). Overall adequate given tool simplicity.

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

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

The description does not explain the 'run_id' parameter beyond the schema; it only implies its role via 'specific run'. With 0% schema description coverage, the description fails to add value for the parameter.

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

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

The description clearly states 'Get detailed information about a specific run', specifying the action and resource. It distinguishes from sibling tools like 'get_runs' (plural) by implying single-run focus, but does not explicitly differentiate from other run-information tools.

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

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

The description advises 'avoid fetching many runs at once', indicating appropriate usage for single runs. It does not explicitly name alternative tools for batch retrieval, but the caution provides clear context.

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

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

Description adds behavioral detail (download and local path) beyond readOnlyHint annotation. However, it omits important traits like what happens if artifact doesn't exist, file size limits, or caching behavior. Annotations already cover read-only nature.

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

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

Single sentence, no redundancy, front-loaded with key action and output. Efficient and scannable.

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

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

For a simple retrieval tool, the description covers basic action and output, but lacks details on error handling, expected path format, or whether the path is temporary. With no output schema, more context about the return value would be helpful.

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

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

Schema has 0% description coverage for both parameters. Description does not provide any additional meaning for 'run_id' or 'artifact_path', leaving agents to infer their format or constraints.

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

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

Description clearly states the verb 'download and return' and resource 'specific artifact', with specific output 'local path'. It distinguishes from siblings like 'get_artifact_content' which returns content, and 'get_run_artifacts' which lists artifacts.

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

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

No guidance on when to use this tool vs alternatives. Does not specify prerequisites, exclusions, or mention sibling tools like 'get_artifact_content' for cases where content is needed instead of path.

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

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

The description aligns with annotations (readOnlyHint), but it doesn't add behavioral context beyond listing, such as pagination or recursion behavior. With annotations present, the bar is lower, and the description is adequate but not enriched.

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

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

Two short sentences, no wasted words. The purpose is front-loaded, and every word earns its place.

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

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

Given the presence of an output schema (not shown but indicated), the description does not need to detail return values. It covers the key usage (listing artifacts and browsing). However, it could mention that it returns a list of artifact paths.

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

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

Schema coverage is 0%, so the description must compensate. It adds meaning to 'path' with an example but does not explain 'run_id'. The compensation is only partial.

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

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

The description clearly states 'List artifacts for a specific run.' with a specific verb and resource, and it distinguishes itself from sibling tools like get_artifact_content and get_run.

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

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

Provides guidance on using the 'path' parameter to browse directories with an example, which helps the agent know when to use it. However, it doesn't explicitly mention when not to use the tool or alternatives.

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

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

Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds 'full history' but does not disclose any other behavioral traits (e.g., rate limits, maximum history length, or response format). With annotations handling the main safety aspect, a score of 3 is appropriate—adequate but not enriching beyond annotations.

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

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

The description is one short sentence, which is concise and front-loaded. However, it lacks any structured detail (e.g., bullet points or examples) that could improve clarity without adding much length. It earns points for brevity but loses slightly for being too minimal to fully support an agent.

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

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

Given the tool has 2 parameters, annotations, and an output schema, the description provides the core purpose but does not elaborate on the output structure or any constraints. Since an output schema exists, the return format is covered elsewhere, but the description could still offer examples or edge cases. Score 3 for being minimally complete but not rich.

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

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

Schema description coverage is 0%, so the description carries full responsibility for explaining parameters. However, the description only says 'Get the full history of a specific metric for a run', which does not add meaning to run_id or metric_name beyond their names. The agent must infer their roles, which is minimal support. Score 2 for insufficient compensation.

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

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

The description clearly states the tool retrieves 'full history of a specific metric for a run'. The verb 'get' and resource 'full history' are specific, and it differentiates from sibling 'get_run_metrics' which likely returns all metrics. However, 'full history' is somewhat ambiguous (e.g., does it include all steps or all timestamps?). Still, it's clear enough for an agent. Score 4 for good but not perfect clarity.

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

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

No explicit guidance on when to use this tool versus alternatives like get_run_metrics or get_run. The context of sibling tools implies this is for a single metric's history, but the description doesn't state this or mention any exclusions. Usage is only implied, not explicitly guided.

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

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

Annotations already declare readOnlyHint=true. Description adds 'with their latest values' which is helpful but does not disclose potential limits, batch sizes, or return format beyond that.

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

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

Single sentence is concise and front-loaded. No wasted words.

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

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

For a simple tool with good annotations and no output schema, the description covers the basic purpose but lacks detail on return format (e.g., key-value pairs, timestamps) which could be helpful for a complete understanding.

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

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

Single parameter run_id has no schema description (0% coverage). Description adds no additional semantic meaning beyond the parameter name itself, leaving the agent to infer that it's a run identifier.

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

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

Description clearly states verb 'Get', resource 'all metrics for a specific run', and scope 'with their latest values'. Distinguishes from sibling tools like get_run_metric and get_experiment_metrics.

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

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

No explicit guidance on when to use this tool versus alternatives like get_run_metric, get_experiment_metrics, or query_runs. Context must be inferred from the description alone.

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

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

Beyond the readOnlyHint annotation, the description warns that each run is large and can flood context, advising small limits. This adds behavioral insight about resource usage and potential performance impact.

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

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

Highly concise and well-structured: one-line purpose, a brief behavioral note, then a clear Args list with examples. Every sentence adds value, no redundancy.

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

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

Given the tool's complexity (4 parameters, output schema exists), the description covers all necessary aspects: what is retrieved, how to limit and paginate, ordering syntax, and usage examples. The output schema handles return values, making this complete for agent use.

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

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

Schema parameters have 0% description coverage, but the description explains each parameter in detail: experiment_id is the ID, limit is max with small limit advice, offset for pagination, and order_by with formatting examples including backticks for special characters. This fully compensates for the schema gap.

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

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

The description clearly states 'Get runs for a specific experiment with full details,' specifying the verb, resource, and scope. It distinguishes from siblings like 'get_run' (single run) and 'query_runs' (complex queries) by focusing on a specific experiment and full detail retrieval.

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

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

Provides explicit guidance on limiting results (3-10) to avoid context flooding and using offset for pagination. Includes examples for typical usage. Does not directly compare to sibling tools but implies appropriate use cases.

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

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

The description adds 'connectivity' to the read-only nature indicated by annotations, providing slight extra context. No contradictions. It is adequate given the simplicity of the tool.

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

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

The description is a single 5-word sentence with no extraneous information. It is efficiently front-loaded and earns its place.

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

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

Given the tool's simplicity (no parameters, no output schema, read-only annotation), the description is complete. It tells the agent exactly what the tool does.

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

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

There are no parameters, and schema coverage is 100%. The description does not need to add parameter information; it suffices baseline score 4.

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

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

The description 'Check MLflow server health and connectivity' clearly states the action (check) and the resource (MLflow server health/connectivity). It distinguishes the tool from its siblings, which are focused on runs, experiments, and models.

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

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

The description implies usage for verifying server status before other operations, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. Usage is implied but not detailed.

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

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

The annotations already set readOnlyHint=true, and the description adds valuable context about potential large result sizes and the need for limits. No contradiction between description and annotations.

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

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

The description is concisely structured with sections for args, examples, and a warning. Every sentence adds value, and it is well organized for an AI agent.

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

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

Given the tool's complexity (5 params, output schema present), the description covers all parameters with examples and syntax details. The output schema is available, so return value description is not needed.

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

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

With 0% schema coverage, the description compensates by explaining each parameter, including the MLflow filter syntax and the backtick handling for order_by. It provides examples that add meaning.

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

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

The description explicitly states it queries runs using MLflow's filter syntax with optional sorting. It distinguishes from siblings like 'search_runs_by_tags' and 'get_runs' by highlighting the MLflow filter syntax, a specific verb and resource.

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

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

The description warns that 'Runs can be large. Use wise limits to avoid flooding context,' providing implicit guidance on when to use and caution. It does not explicitly state when not to use, 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.

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

Annotations declare the tool is not read-only (readOnlyHint=false), consistent with the description's 'register' and 'creates' verbs. The description adds behavioral context by specifying URI formats (LoggedModel and Run artifact) and the optional tags, but does not clarify behavior if the model already exists.

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

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

The description is well-structured: a concise opening sentence, clear Args section, and practical Examples. Every sentence adds value, and it is front-loaded with the main action.

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

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

Given three parameters, no output schema, and sparse annotations, the description covers the core functionality and parameter details with examples. However, it omits potential error conditions or behavior on re-registration, which would strengthen completeness.

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

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

With 0% schema description coverage, the description fully compensates. It explains model_name, model_uri with examples (including two URI schemes), and tags as an optional dict. This adds significant meaning beyond the schema's minimal type info.

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

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

The description clearly states the tool registers a model into the registry and creates the registered model if it doesn't exist. This specific verb-resource combination distinguishes it from sibling tools like get_registered_model or delete_registered_model.

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

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

The description provides examples of usage with different URI types but does not explicitly state when to use this tool versus alternatives like copy_model_version or transition_model_version_stage. It implies usage when registering a model but lacks explicit context.

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

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

The description explains the tool's read-only behavior (consistent with readOnlyHint annotation) and details the filtering and sorting capabilities. It goes beyond annotations by describing parameter format and supported fields, but does not disclose edge cases or default ordering behavior.

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

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

The description is well-structured with a concise purpose statement, bullet-like parameter explanations, and examples. It is front-loaded and every sentence adds value. Minor improvement possible by shortening the examples slightly.

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

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

For a search tool with an output schema, the description covers parameter semantics and usage examples. It does not discuss pagination or default result limit behavior, but these are minor omissions. Overall, it provides sufficient context for an AI 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.

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

Despite 0% schema description coverage, the description fully explains all three optional parameters: filter_string (with column names and examples), order_by (format and example), and max_results (default value). This compensates entirely for the schema gap and adds meaningful semantics.

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

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

The description clearly states 'Search experiments with optional filtering and sorting,' specifying a specific verb and resource. It distinguishes from sibling tools like search_logged_models and search_runs_by_tags by targeting experiments. The included filter syntax and examples further clarify its purpose.

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

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

The description provides examples and explains filter syntax, which implies when to use. However, it does not explicitly state when not to use this tool or mention alternatives such as get_experiments or get_experiment_by_name for simpler queries. The guidance is adequate but not comprehensive.

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

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

The description adds behavioral details beyond the readOnlyHint annotation, such as filter_string using AND only, constraints on datasets parameter, and default for max_results. No contradictions with annotations.

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

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

The description is well-structured with Args section, examples, and no redundant information. Every sentence adds value, and it's appropriately concise for the complexity.

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

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

Given the tool's complexity (search, filter, sort, datasets) and the presence of an output schema, the description covers all inputs, edge cases (filter limitations), and provides examples, making it complete.

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

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

Schema coverage is 0%, but the description provides detailed semantics for all 5 parameters, including structure, examples, and constraints (e.g., filter_string AND-only, datasets dict requirements). Fully compensates for lack of schema descriptions.

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

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

The description clearly states 'Search for logged models across one or more experiments', specifying the verb (search) and resource (logged models). This distinguishes it from siblings like query_runs or search_experiments.

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

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

The description warns about large results and suggests using wise limits, providing initial usage context. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.

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

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

Annotations declare readOnlyHint=true, and the description adds context about run size and limiting results, which is useful beyond the annotation.

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

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

Two sentences: first states purpose, second adds concise advice. No wasted words.

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

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

With an output schema present, the description need not explain return values. It covers tags and limit but omits experiment_id guidance, which is required.

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

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

Schema coverage is 0%, but the description provides an example tag format and advice on limit. However, experiment_id and offset are not elaborated.

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

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

The description clearly states the tool finds runs with specific tags, providing an example. This distinguishes it from siblings like get_runs and query_runs.

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

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

The advice 'use wise limits' implies caution but does not explicitly compare to alternatives or state when not to use this tool.

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

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

Annotations indicate idempotentHint=true (safe to repeat) and readOnlyHint=false (mutation). The description 'set a tag' implies overwriting behavior, but no additional detail is provided (e.g., limits, side effects). Annotations already supply key behavioral info.

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

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

Short and to the point, but the 'Args' section essentially duplicates the schema. Could be more concise by omitting argument descriptions that add no new information.

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

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

No output schema and high parameter count (3) with 0% coverage. The description does not clarify return value, overwrite behavior, or any prerequisites. Incomplete for a mutation tool.

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

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

Schema description coverage is 0%, so the description must compensate. It provides examples for key ('team', 'status') but no type constraints or default values. Minimal added meaning beyond parameter names.

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

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

The description clearly states the tool sets a tag on an experiment and lists parameters. However, it does not differentiate from sibling tools like set_run_tag or set_registered_model_tag, which serve similar purposes for different entities.

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

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

No guidance on when to use this tool versus alternatives (e.g., set_run_tag for runs, set_registered_model_tag for models). The description lacks context for selection.

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

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

Annotations set readOnlyHint=false (mutates) and idempotentHint=true. Description says 'assign', which aligns with mutation. No additional behavioral context is provided beyond what annotations already convey. No contradictions.

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

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

Description is concise: one-line summary, Args list, and an example. Every sentence is purposeful and front-loaded with the key action.

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

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

For a simple mutation tool with 3 parameters and idempotent annotations, the description covers usage adequately. It does not mention return value (no output schema) or behavior on reassignment, but overall it's nearly complete.

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

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

Schema has 0% description coverage, so description compensates by listing each parameter with brief explanations (name, alias, version) and includes an example. This adds meaning beyond the schema's type-only definitions.

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

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

Description clearly states 'Assign an alias to a specific model version' with an example ('promote best model to champion'). This is a specific verb+resource that distinguishes from sibling tools like delete_model_alias or get_model_version_by_alias.

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

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

The description provides an example but does not explicitly specify when to use this tool vs alternatives (e.g., transition_model_version_stage). It implies usage for assigning aliases but lacks when-not guidance. Clear context but no exclusions.

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

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

Annotations declare readOnlyHint=false and idempotentHint=true, so the description does not need to restate safety. However, it adds no behavioral context beyond the operation—e.g., whether a tag is overwritten or appended, required permissions, or side effects. With annotations covering the baseline, the description still lacks value-add.

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

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

The description is short and front-loaded with the purpose. However, the args list in the description is redundant with the input schema, adding no new information. Could be more compact by removing the repetitive arg listing.

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

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

For a simple 3-parameter mutation with idempotency annotation, the description covers the basic operation. Missing details: return value (none), error behavior (overwrite?), and explicit note on idempotency. Adequate but not complete.

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

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

Schema coverage is 0% (no descriptions in schema). The description adds examples for key ('problem_type', 'team', 'framework') and a brief description for name ('Name of the registered model'). This provides some guidance but does not fully define the parameters' semantics or constraints.

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

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

The description clearly states 'Set a tag on a registered model' with examples like problem_type, team, framework. The verb+resource is specific and distinguishes from sibling tools that operate on other entities (experiments, runs) or set aliases.

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

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

No usage guidance provided. The description does not mention when to use this tool versus alternatives (e.g., set_experiment_tag, set_run_tag) or any context for when it is appropriate. Implied by name but not explicit.

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

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

Annotations already indicate idempotentHint=true and readOnlyHint=false. The description adds no behavioral traits beyond what the name and annotations convey (e.g., no mention of overwrite behavior 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.

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

The description is a single sentence with a clear Args section. It is front-loaded, concise, and every sentence is useful without unnecessary repetition.

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

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

While the tool is simple, the description lacks details on what happens on success (e.g., return value) and whether tags are overwritten or appended. However, given the idempotentHint and no output schema, the description is adequate but not exhaustive.

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

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

Schema description coverage is 0%, but the description repeats parameter names with minimal elaboration and provides examples for the 'key' parameter (e.g., 'best_model', 'reviewed_by'), adding moderate value beyond the schema.

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

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

The description clearly states the action ('Set a tag on a run') with concrete examples ('best model', 'flag for review'), and the name and context distinguish it from sibling tools like set_experiment_tag and set_registered_model_tag.

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

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

The description implicitly limits usage to runs and provides examples, but does not explicitly state when to use this tool versus alternatives or when not to use it.

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

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

Annotations already mark destructiveHint=true, so the description adds context about the archive_existing parameter behavior. It also notes deprecation, but does not detail other behavioral aspects like permission requirements or side effects beyond archiving. Still adds value beyond annotations.

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

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

The description is concise: a one-sentence purpose, deprecation notice, usage guidance, then parameter list. Front-loaded with key info, every sentence earns its place.

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

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

The description covers purpose, usage guidelines, and parameter details comprehensively. However, it lacks any mention of return value or success/failure behavior, which would be helpful given no output schema. Still mostly complete for a legacy tool.

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

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

Schema coverage is 0%, so the description fully compensates by explaining each parameter: stage enum values ('Staging', 'Production', 'Archived') and archive_existing boolean behavior ('If True, archive existing versions in the target stage'). This adds significant meaning beyond the schema.

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

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

The description explicitly states the tool transitions a model version to a new stage (Staging, Production, Archived). It also distinguishes from siblings like set_model_alias and copy_model_version by recommending them for newer workflows.

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

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

The description explicitly says 'Use this only when working with legacy stage-based deployments' and provides alternatives (set_model_alias, copy_model_version), clearly indicating when to use and when not to use this tool.

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

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

Annotations already indicate write (readOnlyHint=false) and idempotency (idempotentHint=true). The description adds minimal behavioral detail beyond 'update' – it doesn't confirm side effects, authorization needs, or error states. Since annotations carry the burden, a score of 3 is appropriate.

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

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

The description is extremely concise: two succinct sentences plus a parameter list. No wasted words, and every sentence serves a purpose.

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

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

For a simple update tool with 3 parameters and no output schema, the description covers the core operation and parameter semantics. However, it misses edge cases (e.g., version must exist, return behavior) and does not relate to sibling tools. Given the low complexity, a 3 is adequate.

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

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

With schema description coverage at 0%, the description must add meaning. It explains that 'name' is the registered model name, 'version' is the version number, and 'description' is the new text. This provides crucial context that the schema titles alone lack, though more detail (e.g., version format) would strengthen it.

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

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

The description clearly states 'Update the description of a model version,' which is a specific verb and resource. It effectively distinguishes from sibling tools like copy_model_version, delete_model_version, and transition_model_version_stage that 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.

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, conditions (e.g., version must exist), or exclusions relative to other update tools like set_model_alias or set_registered_model_tag.

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