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dbdave

SSAS MCP Server

by dbdave
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: execute_dmv for DMV system queries, execute_query for MDX/DAX against the cube/model, and list_metadata for exploring metadata structure. No functional overlap.

    Naming Consistency5/5

    All tools follow a consistent verb_noun snake_case pattern: execute_dmv, execute_query, list_metadata. The naming is predictable and unambiguous.

    Tool Count5/5

    Three tools cover the essential read-only query and metadata exploration workflow for SSAS, with no extraneous or missing operations. The count is well-scoped for the domain.

    Completeness5/5

    The tool set provides complete coverage for the stated purpose: listing metadata to discover names, executing queries via MDX/DAX or DMV, and handling cube/model selection. No obvious gaps.

  • Average 4.6/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 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

  • 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.

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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

  • Behavior4/5

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

    No annotations, so description must disclose all behavior. It explains output content (measures, dimensions, hierarchies) and fallback for cube_name. No hidden side effects noted.

    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?

    Well-structured with Args section, but somewhat lengthy. Could be more concise, though every sentence adds value.

    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 output schema exists, return values are covered. Input behavior thoroughly explained. Lacks only minor details like error cases or permission requirements.

    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?

    Schema coverage 0%, but description fully explains both parameters: cube_name default behavior and dimension usage with example bracketed name. Adds significant value beyond 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?

    Describes specific action: listing queryable metadata of SSAS cube/tabular model for obtaining bracketed unique names. Distinguishes from sibling tools (execute_dmv, execute_query) as a preparatory step.

    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?

    Explicitly states to call before writing queries to avoid guessing names. Explains when to use dimension parameter. Does not mention when not to use but 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 carries the full burden. It discloses query constraints (no JOIN, GROUP BY, simple WHERE), supported rowsets, and the optional max_rows cap. It does not mention side effects or permissions, but for a read-only introspection tool this is acceptable.

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

    Conciseness4/5

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

    The description is well-structured: purpose, accepted format, rowsets, limitations, and parameter definitions. It is comprehensive without being overly verbose, though it could be slightly more concise.

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

    Completeness5/5

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

    Given the output schema exists (so return values are documented), the description covers all necessary aspects: input parameters, usage guidelines, behavioral constraints, and relevant rowsets. It is fully sufficient for an agent to decide when and how to invoke this 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?

    Schema coverage is 0%, so the description must compensate. It clearly documents both parameters: 'query' as the DMV SELECT query and 'max_rows' as an optional row cap with default 0. This adds essential meaning beyond the raw schema.

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

    Purpose5/5

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

    The description explicitly states it executes DMV queries for metadata introspection. It distinguishes from sibling tools like execute_query and list_metadata by specifying the DMV SQL format and target system rowsets.

    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 details the accepted query format (SELECT ... FROM $SYSTEM.<rowset>), lists useful rowsets for different models, and notes SQL limitations. It provides clear context for when to use this tool but does not explicitly exclude alternatives.

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

  • Behavior5/5

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

    Discloses read-only nature, statement acceptance criteria, return format (JSON table with columns/rows/row_count/truncated), row cap defaults (10000 or SSAS_ROW_LIMIT), and truncation flag. With no annotations, the description fully covers behavioral traits.

    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?

    Concise, front-loaded with the primary action, uses bullet points and structured Args section. Every sentence adds value without redundancy.

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

    Completeness5/5

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

    Given the presence of an output schema (context indicates yes), the description still adequately covers return format and row limiting. It also addresses accepted query types, making the tool fully understandable.

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

    Parameters5/5

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

    Despite 0% schema coverage, the description fully explains both parameters: query is the MDX/DAX text, max_rows is an optional row cap (0 = server default). This adds complete semantic meaning beyond the bare 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 the tool executes read-only MDX or DAX queries against an SSAS database, with specific syntax for each. It differentiates from sibling tools (execute_dmv, list_metadata) by specifying the query types and database context.

    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 guides when to use MDX vs DAX and lists accepted statement types (SELECT/WITH for MDX; EVALUATE/DEFINE for DAX). Implicitly excludes DMV queries and metadata calls, providing clear context for selection.

    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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  • Evaluate tool definition quality.

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