mcp-powerBI-to-report

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but `execute_dax_dashboard_query` is an explicit alias for `execute_dax_report_query`, creating redundancy. Additionally, multiple tools list semantic models via different methods (`list_semantic_models`, `list_semantic_models_in_workspace_via_modeling_mcp`, `get_catalog`), which could confuse an agent if descriptions are skimmed. However, the descriptions clearly indicate when each is appropriate, so ambiguity is minimal.
Naming Consistency5/5
All tool names follow a consistent `verb_noun` pattern using snake_case. Verbs like `auth`, `complete`, `execute`, `get`, `list`, and `start` are clear and predictable. Even lengthy names like `list_semantic_models_in_workspace_via_modeling_mcp` adhere to the pattern. No mixing of naming conventions.
Tool Count4/5
With 11 tools, the count is within the typical well-scoped range. However, there is some redundancy: three authentication tools and four listing tools, with an alias that could be merged. Slightly more than necessary for the domain, but justified by different authentication methods and fallback scenarios.
Completeness4/5
The tool set covers the full workflow for Power BI reporting: authentication, workspace discovery, semantic model listing, and DAX query execution that returns a report. Minor gaps exist, such as no direct tool to list reports or manage models, but the primary purpose of querying and generating reports is well-covered.
Average 3.8/5 across 11 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 35 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavior fully. It only mentions listing models and workspace selection, but omits details about permissions, rate limits, or potential errors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the main purpose. It uses two sentences effectively without redundancy, but could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one optional parameter and no output schema, the description covers the core functionality, usage prerequisites, and a fallback instruction. It is adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the workspaceId parameter described adequately in the schema. The description adds contextual advice but does not significantly enhance parameter understanding beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists semantic models in My workspace or a specific workspace by id. It uses a specific verb and resource, but does not explicitly differentiate from sibling tools like list_semantic_models_in_workspace_via_modeling_mcp.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance to use list_workspaces first to resolve workspace ids and instructions for handling authentication and user input. Lacks explicit exclusion of 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It adds the behavioral note about keeping the Microsoft Modeling MCP process alive to reduce login prompts, but omits details on side effects, idempotency, or permission requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences that front-load the main purpose. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 5 parameters and no output schema, yet the description does not explain the return format, error handling, or any post-execution behavior. The description is too brief to be considered complete given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 60%, and the description does not add extra meaning beyond the schema's parameter descriptions. The mention of 'using default workspace/model when omitted' relates to default behavior rather than parameter semantics, so it provides minimal additional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (Execute a DAX query) and the resource (Power BI semantic model), and mentions default workspace/model behavior, which distinguishes it from sibling tools like execute_dax_dashboard_query or execute_dax_report_query.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for general DAX queries and mentions a keep-alive benefit, but does not explicitly state when to use this tool versus the dashboard or report query variants, nor does it specify 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry full behavioral disclosure. It mentions the output format but does not reveal side effects, authorization requirements, rate limits, error handling, or whether the operation is read-only. This is a significant gap for a tool that executes queries.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the core action and output. Every sentence adds value with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema or annotations exist. The description does not explain return values, error states, or prerequisites like Power BI access. For a tool with 7 parameters, more context is needed for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 71% (5 of 7 parameters have descriptions in the schema). The description adds no additional meaning beyond what the schema provides, such as usage tips or format details. Baseline 3 is appropriate given high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the verb 'Execute', the resource 'DAX query against a Power BI semantic model', and the dual output 'concise text answer and self-contained HTML dashboard/report'. It also explicitly targets 'executive review' and 'boss/CEO business questions', distinguishing it from sibling tools like execute_dax_query.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes 'Prefer this tool for boss/CEO business questions', providing clear context for when to use it. However, it does not explicitly exclude alternatives or state when not to use it, leaving some ambiguity relative to sibling tools like execute_dax_dashboard_query.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions polling and caching but lacks details on retries, timeouts, or side effects (e.g., overwriting existing tokens).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence of 15 words, efficiently stating the tool's action without wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a simple poll-and-cache tool with no parameters or output schema, but it could add context that it should be called after start_device_login and that it blocks until complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters and schema coverage is 100%. The description adds no parameter info, but the baseline is 4 since no compensation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool polls for pending device-code login and caches the token, clearly distinguishing it from siblings like start_device_login (which initiates) and auth_status (which checks status).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use after start_device_login but provides no explicit guidance on when to use this tool versus alternatives or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description only labels it as an alias without disclosing any behavioral traits (e.g., side effects, data modification, permission requirements). This is insufficient for a mutation-capable tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that conveys the essential information without unnecessary words. It is well-structured and immediately understandable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the tool is a simple alias, the description does not explain its behavior or return values. Given the lack of output schema and moderate parameter count, the description is minimally adequate but could provide more context by referencing execute_dax_report_query's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 71% schema description coverage, the schema already explains most parameters. The description adds no additional parameter information, resulting in a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it is an alias for execute_dax_report_query, clearly identifying its purpose and distinguishing it from siblings by naming the specific tool it replaces.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description notes it is for compatibility with earlier dashboard workflows, implying when to use (backward compatibility). It names the sibling it aliases, providing an alternative for new workflows, but lacks explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only mentions it uses the Power BI REST API without disclosing behavioral traits like safety, side effects, or rate limits. It doesn't confirm it is read-only or describe any potential blocking or pagination.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences: one stating the action and resource, the other providing usage context. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and no output schema, the description covers its purpose and usage context well. However, it doesn't describe the output structure, which could be helpful for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single boolean parameter fully. The description adds no additional meaning about the parameter beyond what is in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns 'all visible workspaces and semantic models' and provides specific usage examples like 'which model should I use?' Distinguishes from siblings like list_workspaces which returns only workspaces.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description identifies this as the 'preferred tool for open-ended questions' and gives example queries. While it doesn't explicitly exclude specific scenarios, the context is clear about its intended use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It only mentions 'authenticated account' but lacks details like authentication requirements, rate limits, or behavior when no workspaces exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. It front-loads the purpose and immediately follows with usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, but the description does not mention what is returned (e.g., list of workspace objects with properties). For a simple tool, it is adequate but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (parameter is described in schema). The main description does not add parameter details beyond the schema, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it lists all workspaces visible to the authenticated account via the Power BI REST API, which is a specific verb+resource combination. It distinguishes from siblings like list_semantic_models which list models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use this first when the user does not provide a workspace name or id,' giving explicit guidance on when to use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that tokens are not exposed, a key safety trait. However, with no annotations, it lacks details on prerequisites (e.g., requires prior authentication) or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the action and key constraint, no superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete enough for a simple parameterless tool. Minor gap: does not specify the output format (e.g., string, structured data), but the purpose is clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters and full schema coverage, the description adds context about the tool's purpose. Baseline is 4 for no-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool shows the authentication mode without exposing tokens. The title and description align, and the tool is distinct from siblings like login flows and queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied (checking auth status before other operations), but no explicit guidance on when to use or alternatives is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as read-only nature, authentication requirements, or side effects. The operation is implicitly read-only, but this is not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the key information without any superfluous words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one optional parameter and no output schema, the description covers the essential purpose, when to use, and the parameter behavior. It lacks details on potential errors or output format, but these are not critical given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the parameter 'workspaceNames' is adequately described in the schema. The tool description adds no additional meaning beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (list), resource (semantic models), and specific context (manually configured POWERBI_KNOWN_WORKSPACES using Microsoft Modeling MCP). It distinguishes from sibling tools by referencing the specific method and use case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance: 'Use this for CEO workflows when REST workspace discovery is unavailable.' This indicates when to use and implies when not to, differentiating from REST-based alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. It does not disclose what the tool returns (e.g., a user code), how it behaves (blocking vs. async), or prerequisites, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, followed by usage guidance. No wasted words, highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the flow involving complete_device_login and auth_status, the description lacks context about the login process, expected output, or next steps, making it incomplete for an autonomous agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline score of 4 applies. Description does not need to add parameter info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (start) and resource (delegated user device-code login for Power BI REST API), and distinguishes from siblings like complete_device_login by specifying it's for starting the login process.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (one-time local setup) and recommends an alternative (service principal for production), providing clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description adds value by disclosing the authentication method (XMLA) and the fallback nature. It does not describe side effects or failure modes, but for a read-only list operation, the behavioral context is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no waste: first sentence states action and auth, second gives fallback context, third gives parameter instruction. Front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with one parameter, the description covers purpose, usage context, and parameter guidance. It does not explain the return format, but given no output schema, this is acceptable. The description is complete enough for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the single parameter with description. The description reinforces the requirement for an explicit workspace name and provides guidance on handling missing values, adding meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action: list semantic models in a known workspace using XMLA auth, and distinguishes it as a fallback from REST-based tools. This clearly identifies the tool's purpose and separates it from siblings like 'list_semantic_models'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states this tool is a fallback when REST workspace discovery auth is unavailable, providing clear when-to-use guidance. It also instructs to ask the user if workspace name is missing instead of guessing, offering a usage best practice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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