pbi-mcp

Server Quality Checklist

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Latest release: v2.0.0
Disambiguation5/5
Each tool targets a distinct action (e.g., create vs. bulk create, list measures vs. get model) with no overlap. Even similar tools like pbi_create_measure and pbi_bulk_create_measures are clearly separated by batch vs. single.
Naming Consistency4/5
All tools start with 'pbi_' followed by a verb_noun pattern for most (pbi_list_measures, pbi_create_measure), but pbi_model_lineage uses noun_verb, breaking the pattern. Still, the naming is clear and readable overall.
Tool Count5/5
With 13 tools covering project setup, model inspection, measure CRUD, column and relationship creation, and backup management, the count is well-scoped for the Power BI model domain—not too few or too many.
Completeness3/5
The set covers core operations well (measure CRUD, column/relationship creation, calc groups, backups), but lacks update/delete for columns, relationships, and calc groups, leaving some lifecycle gaps.
Average 3.6/5 across 13 of 13 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 18 commits in the last 12 weeks
- Last stable release on July 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions that the tool fails if the name already exists, which is a behavioral trait. However, no annotations are provided, and the description does not disclose other relevant behaviors like destructiveness, permissions, 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise but lacks structure. It does not front-load key information or separate sections, and it omits important details, making it less useful than a longer, structured description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters (3 required), no output schema, and no annotations, the description is insufficient. It does not explain return values, parameter dependencies, or usage context, leaving significant gaps for the AI agent to reliably invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description only mentions 'table' but does not explain the meaning, format, or constraints of parameters like dax, name, format, or display_folder. It adds no value beyond the schema's property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the specific verb 'Create' and resource 'measure', and includes the failure condition 'Fails if the measure name already exists'. This clearly distinguishes it from siblings like update_measure or delete_measure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. For example, it does not mention that if the measure name exists, one should use update_measure. No prerequisites or context are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral burden. It discloses the default cardinality and the precondition, but omits important traits such as whether the relationship can be edited after creation, whether it replaces existing relationships, permission requirements, or error behavior on incompatible columns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the key action and default behavior, and contains no unnecessary words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and 7 parameters (4 required), the description is too brief. It lacks details about return values, failure modes, model modifications, and parameter ranges, making it insufficient for an agent to fully understand the tool's effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the 'from -> to' direction and the column existence requirement, but does not describe the meaning or allowed values of 'cardinality', 'cross_filter', 'is_active', or the role of each parameter beyond what is inferred from names.
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 action (create a relationship), the default cardinality direction (many-to-one from -> to), and a precondition (columns must exist). It is distinct from sibling tools like create_measure, but does not explicitly differentiate from other potential relationship methods.
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 gives a precondition ('Both endpoint columns must already exist'), which helps agents know when the tool is applicable. However, it does not provide guidance on when not to use it or mention alternatives, leaving the agent to infer context from sibling tool names.
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?
Discloses that source_column defaults to column name if no dax provided, but no information on permissions, side effects, or success/failure behavior. With no annotations, more detail would be beneficial.
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?
Extremely concise single sentence with no wasted words. Front-loads the core action.
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 and minimal parameter explanation. Does not describe return value, error cases, or post-creation behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only dax and source_column are mentioned; data_type, summarize_by, table, name are not explained. With 0% schema description coverage, the description should cover all parameters but fails to do so.
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?
Clearly states 'Create a column in table' with verb and resource. Distinguishes between calculated columns (dax) and simple columns (source_column). However, does not explicitly differentiate from sibling tools like pbi_create_measure.
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?
Provides guidance on when to use dax vs source_column, but no explicit when-not-to-use or comparison to alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the action and sequencing, omitting details about side effects, required permissions, error handling, or idempotency. This minimal disclosure leaves the agent unaware of important behavioral traits such as whether calling it multiple times is harmless or what happens on invalid paths.
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, well-structured sentence that directly communicates the core action and a critical usage instruction. It is front-loaded with the action and essential context, with no extraneous words. Every element earns its place, making it highly efficient for an AI agent.
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 low complexity (one parameter, no output schema) and the description adequately conveys its purpose and usage order. However, it lacks details about parameter validation, error behavior, and prerequisites (e.g., whether a connection to the model server is required). For a simple initialization tool, the description is minimally viable but could be more complete to support robust agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only the parameter 'path' with no description (0% coverage). The tool description implies that 'path' refers to a .pbip file location, but it does not elaborate on format (absolute vs relative), supported protocols, or typical values. Since the description does not compensate for the missing schema documentation, the agent lacks sufficient guidance on how to specify the path correctly.
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 action: 'Point the model server at a Power BI Project (.pbip).' It also includes the crucial note to 'Call this first,' which distinguishes it as a setup step from sibling tools that operate on backups, measures, or models. The verb 'point' is slightly vague, but the context makes it understandable.
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 advises 'Call this first,' indicating it is a prerequisite for other operations. This provides clear guidance on when to use it. However, it does not mention when not to use it or suggest alternatives, but given the sibling tools, alternatives are implied as later operations.
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 the full burden. It only states what is returned but does not disclose behavioral traits such as whether the tool is read-only, if it has performance implications, or if it requires authentication or a specific state. The simplicity leaves the agent guessing.
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 that is front-loaded and contains no unnecessary words. It communicates the essential purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain what the return data looks like or any format details, but it does not. It also omits contextual information like whether the project must be set. For a simple read tool, this may be acceptable, but the lack of any behavioral or structural details makes it incomplete.
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 0 parameters, so schema description coverage is 100%. The description adds no parameter information, but with no parameters, the baseline score of 4 is appropriate as no additional meaning 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 uses a specific verb 'Return' and identifies the resource as 'tables, columns, measures, and relationships for the project.' It clearly distinguishes itself from sibling tools like pbi_list_measures or pbi_model_lineage by explicitly listing the model components returned.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites (e.g., a project must be selected) or any case where another tool would be more appropriate.
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 burden of behavioral disclosure. It correctly notes that omitted fields are kept (partial update behavior), which is helpful. However, it does not disclose permission requirements, side effects, or response format, so transparency is moderate.
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: two sentences with zero wasted words. Every part contributes meaning, front-loading the purpose and key behavioral trait (partial update).
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?
For a mutation tool with 5 parameters and no output schema, the description is too sparse. It lacks prerequisites, error handling, return value details, and examples. The sibling tools provide context but are not referenced. The description is incomplete for robust agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description mentions 'DAX/format/folder', providing meaning for three of five parameters. It does not explain the role of 'name' and 'table' (which are identifiers), nor does it give format syntax. The added value is modest, justifying a score of 3.
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 updates an existing measure and specifies which attributes can be updated (DAX, format, folder). It effectively differentiates from sibling tools like create_measure and delete_measure due to the explicit 'Update' verb.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as pbi_create_measure. There are no conditions, prerequisites, or exclusions mentioned, leaving the agent without context for tool selection.
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 discloses that items use SELECTEDMEASURE() patterns and that order becomes display order. However, it does not specify if the action is destructive, what happens on duplicate names, or error handling. Basic behavioral info is present but incomplete.
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: two short sentences. The first sentence states the primary purpose, the second details key behavioral aspects of items and ordering. No extraneous words, front-loaded with essential information.
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 the tool has three required parameters, no annotations, and no output schema, the description covers the basic creation purpose and items structure but omits meaning of the precedence parameter, error scenarios, and return value. It is minimally adequate but not comprehensive.
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 0% schema coverage, the description must compensate. It explains the items parameter format as a list of objects with name and dax attributes, adding meaning beyond the schema. For name and precedence, no additional detail is given beyond the schema definitions. Overall, partial parametric guidance.
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 'Create a calculation group table', identifying the verb and resource. It also specifies the structure of items and behavior of ordering, which distinguishes it from sibling tools like pbi_create_measure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as pbi_create_measure. Usage context is only implied by the tool's name and description, but no explicit when-to-use or when-not-to-use is given.
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 provides moderate transparency by specifying DAX and format are included. However, it does not disclose pagination, limits, ordering, or behavior when no table is specified (e.g., does it list all measures?).
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, well-structured sentence of 12 words. It is front-loaded with the main action and avoids 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 the tool's simplicity, one optional parameter, and an existing output schema, the description covers the essential purpose and key output characteristics (DAX, format). Could mention default behavior (lists all measures) but not necessary.
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 no parameter descriptions, so the description adds value by explaining the table parameter is for optional filtering. This clarifies the only parameter's role.
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 measures and includes DAX and format, distinguishing it from create/update/delete siblings. However, it implicitly assumes the model context is understood, which could be clearer.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions optional filtering to one table, giving some usage context. But it does not explicitly state when to use this tool over alternatives like pbi_get_model or pbi_model_lineage, nor does it provide any 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 are provided, so the description must carry the full burden. It indicates it returns a graph (likely read-only), but does not disclose potential side effects, authentication needs, error behavior, or whether the operation is safe. Only minimal behavioral context is given.
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 concise sentences with no wasted words. The first sentence defines the tool's output (dependency graph), and the second explains the essential input. It is appropriately sized and front-loaded.
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 the tool's simplicity (one parameter, no output schema), the description covers the core functionality. However, it omits details about the output format (e.g., JSON structure) and ignores potential issues like invalid measure names. Adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the 'measure' parameter by stating it should be passed for the dependency graph. With 0% schema description coverage, this explanation is helpful but lacks details on format, optionality (though schema shows default null), and constraints.
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 returns a dependency graph for a given measure, specifying 'who references whom' and 'deps + dependents'. This distinguishes it from sibling tools which focus on backups, CRUD operations on measures, or model settings.
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 tells the user to pass a measure to get its dependencies and dependents. However, it does not explicitly mention when not to use this tool or suggest alternatives, but the context is clear enough for an agent to decide.
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, so the description must fully disclose behavior. It mentions ordering (newest first) but fails to state read-only nature, auth requirements, 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?
Single sentence, front-loaded with action and resource, no wasted words. Highly concise.
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 list tool with an output schema, the description is largely complete. It lacks mention of potential size limits or necessary context, but core functionality is covered.
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?
Input schema has zero parameters, so no parameter information is needed. Baseline of 4 applies; description adds no parameter details because there are none.
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', the resource 'every .bak-* snapshot', and ordering 'newest first'. It distinguishes this tool from siblings like pbi_restore_backup by focusing on listing.
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 versus alternatives. Usage is implied as a prerequisite to restore operations, but no when-not or alternative suggestions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It mentions 'over its original' hinting at destructive overwrite, but lacks detail on auth, error handling, 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?
One sentence, front-loaded with all key information, no unnecessary 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?
Given one parameter and no output schema, description is adequate but minimal. For a restore operation, more detail on impact and prerequisites would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It adds meaning by specifying the parameter is a path from pbi_list_backups, which is not in the schema. Could include format or validation.
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 (restore), the resource (backup file), and ties it to a sibling tool (path from pbi_list_backups), distinguishing it from list/set tools.
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 specifies that the backup path should come from pbi_list_backups, providing clear usage guidance. It does not explicitly state when not to use or alternatives, but the context is sufficient.
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, the description discloses refusal behavior due to dependencies, force bypass, and dry_run preview. This adds significant behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, no wasted words. All information is front-loaded and essential.
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 delete tool with no output schema, the description covers dependency behavior, force, and dry_run. It lacks details on return value or error conditions, but is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description adds meaning for force and dry_run. However, it does not explain `name` and `table` beyond what the schema provides (type and required).
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 'Delete a measure,' which is a specific verb-resource pair. It distinguishes from siblings like pbi_create_measure and pbi_update_measure.
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 explains when force and dry_run are useful, but does not explicitly mention when not to use the tool or alternatives among siblings.
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 exist, so the description carries the full burden. It discloses a key behavior: 'The whole batch is validated before any write happens,' adding transparency about validation-before-write. However, it does not mention idempotency or error handling.
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: first states purpose, second provides structure and behavioral note. No extraneous information, efficient and front-loaded.
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 one-parameter input and no output schema, the description covers what the tool does, required fields, and a key behavioral trait (validation before write). It lacks details on error handling, limits, or success response, but is reasonably complete for this simple 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?
With 0% schema description coverage, the description fully compensates by specifying the structure of the measures array: table, name, dax, optional format and display_folder. This provides critical meaning beyond the schema's empty items definition.
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 'Create many measures at once,' specifying the verb (create), resource (measures), and bulk scope. It distinguishes from the sibling pbi_create_measure which handles a single measure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when many measures need to be created at once, but lacks explicit guidance on when not to use it or alternatives (e.g., pbi_create_measure for single measures). No when-to-use vs. when-not statements.
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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