mcp-powerBI

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but 'get_catalog' overlaps with 'list_workspaces' and 'list_semantic_models' by combining their functionality. The descriptions help differentiate, but some confusion is possible.
Naming Consistency4/5
All names use snake_case and mostly follow a verb_noun pattern. Minor deviations include 'auth_status' (abbreviation) and the very long 'list_semantic_models_in_workspace_via_modeling_mcp', but overall consistent.
Tool Count5/5
7 tools is a reasonable number for a Power BI discovery-focused server. Each tool serves a clear role without excessive redundancy.
Completeness3/5
The tool set covers authentication and read-only discovery well, but lacks any create, update, delete, or execute operations. For a full Power BI management surface, this is notably incomplete.
Average 4/5 across 7 of 7 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses the two workspace contexts but lacks information on side effects, permissions, error handling, or rate limits. For a listing tool, basic operational traits are missing.
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 the core purpose in the first sentence. No wasted words; every sentence adds value. Ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should clarify the return format or structure, which it does not. It also fails to address error cases (e.g., invalid workspaceId). While the tool's name implies the output, the description is minimally adequate for a simple listing tool, but lacks completeness for edge cases.
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 has 100% coverage with the workspaceId parameter described. The description adds value by explaining when to omit workspaceId (for My workspace) and how to handle workspace discovery. This extra context goes beyond the schema, earning a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists semantic models and specifies two scopes (My workspace or specific workspace). It is specific with verb+resource, but does not explicitly differentiate between the sibling tool list_semantic_models_in_workspace_via_modeling_mcp, so it loses points for sibling distinction.
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 advises to use list_workspaces first to resolve IDs and provides guidance for handling unauthenticated workspace discovery. However, it does not compare with the sibling list_semantic_models_in_workspace_via_modeling_mcp, nor does it specify when to use this tool versus alternatives, leaving ambiguity.
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 bear full burden. It mentions polling and caching but lacks details like timeout behavior, error handling, or conditions under which polling stops. Gaps remain.
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 that is front-loaded with the main action. No wasted words; every part contributes to understanding.
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, so description should explain return values. It only mentions caching but does not specify what the tool returns (e.g., success status, token). Gaps in completeness for a critical authentication tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, and schema coverage is 100% trivially. Description adds no parameter info, but baseline for 0 parameters is 4. No additional meaning 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?
Description clearly states the verb 'poll' and 'cache' and the specific resource 'pending device-code login'. It distinguishes from sibling tools like 'start_device_login' by indicating it completes 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 Guidelines3/5
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 implies usage after 'start_device_login' but does not state prerequisites or exclusions, leaving the agent to infer.
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?
With no annotations, the description carries the full burden. It discloses that the tool reads and does not expose tokens, but does not specify the return format, possible values of auth mode, or error conditions. Adequate for a simple read-only status tool but lacks depth.
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 sentence, front-loaded with the action ('Show'), and contains no extraneous words. Every word contributes to the tool's purpose and safety guarantee.
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 parameterless status check tool, the description covers the core purpose and a key behavioral trait (no token exposure). However, it does not describe the return value format or possible values, which could be useful. Overall adequate.
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 tool has zero parameters, and the schema is fully covered (100%). The description adds context about the tool's behavior (no token exposure) but no parameter semantics are needed. Baseline for zero parameters is 4.
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 which Power BI authentication mode is configured', with the added safety note 'without exposing tokens', distinguishing it from sibling tools that may expose tokens. This is a specific verb+resource combination.
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 the tool is safe to use (no token exposure) and can be used to check configured auth mode, but does not explicitly state when to use it versus sibling tools like 'start_device_login' or 'complete_device_login'. No exclusions or alternatives are provided.
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?
Without annotations, the description carries full burden. It implies a read-only operation but lacks details on authentication, rate limits, or return format. It is adequate but not enriched beyond the basic function.
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 with no wasted words. It is front-loaded with the core action and then provides usage guidance.
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 simplicity and the presence of sibling tools for specific queries, the description is complete enough for an AI to understand when to use this tool. No output schema is needed for such a straightforward list.
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 one self-explanatory parameter ('includeMyWorkspace'). The description does not add additional meaning beyond the schema, meeting the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Return') and clearly identifies the resources ('all visible workspaces and semantic models'). It distinguishes from siblings by stating it's preferred for open-ended queries like 'which model should I use?' and 'what workspaces can I access?'.
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 provides usage guidance for open-ended questions and contrasts with other tools (implied). It does not explicitly mention cases when to avoid using it, but the context is sufficiently clear.
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 carries the full burden. It mentions the tool lists workspaces visible to the authenticated account, indicating a read operation. However, it does not disclose potential behaviors like pagination, rate limits, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one optional parameter and no output schema, the description is nearly complete. It covers purpose and usage context. It could be improved by noting any limits or sorting, but it sufficiently meets the needs of an 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 coverage is 100%, so the description adds no additional meaning beyond the schema. The parameter 'includeMyWorkspace' is well-described in the schema, and the description does not enhance it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (list), resource (Fabric/Power BI workspaces), and context (visible to authenticated account). It also distinguishes from siblings by noting it's the first tool to use when no workspace name/id is provided, implying other tools for specific cases.
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 says 'Use this first when the user does not provide a workspace name or id,' providing clear guidance on when to invoke. It does not name specific alternative tools, but the sibling list implies alternatives exist.
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 carries the full burden. It mentions the login process but does not disclose what the tool returns (e.g., a device code or URL) or any 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?
Two sentences, no filler words, front-loaded with the action and resource, then usage guidance. Efficient and to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain what the tool returns or how to proceed after invocation. It currently lacks this information, which is needed for a login initiation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so no additional meaning is needed beyond the input schema, which has 100% 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 clearly states the verb 'start' and the resource 'delegated user device-code login'. It distinguishes from siblings like 'complete_device_login' which finishes the flow.
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 'Use only for one-time local setup' and recommends 'service principal is recommended for production', providing clear context and an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It mentions XMLA auth and fallback nature, giving key behavioral context. However, lacks details on error handling or empty results.
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 and usage. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple parameter and no output schema, description covers purpose, usage, and parameter semantics adequately. Minor gap: no mention of return format or potential errors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with description. Description adds guidance to ask user if workspace is missing and provides example value, enhancing semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool lists semantic models in a known workspace, using a specific auth method. It distinguishes from siblings like 'list_semantic_models' (likely without workspace filter) and 'list_workspaces'.
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 it's a fallback when REST auth is unavailable, and instructs not to guess workspace name, providing clear when-to-use and what to do if missing.
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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