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Microsoft Fabric RTI MCP Server

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kusto_show_queryplan

Read-only

Analyze Kusto query performance without execution. Get the logical operator tree, estimated rows, and execution hints to understand cost and data scanning before running expensive queries.

Instructions

Retrieves the query execution plan without actually running the query.
This is significantly lighter than execution and useful for understanding
performance characteristics and estimating query impact.

:param query: The KQL query to get the execution plan for.
:param cluster_uri: The URI of the Kusto cluster.
:param database: Optional database name. If not provided, uses the default database.
:param client_request_properties: Optional dictionary of additional client request properties.
:return: A compact dictionary with the following keys:
    * query_text — the query as received by the engine
    * stats — planning statistics: Duration, PlanSize (bytes), RelopSize (bytes)
    * relop_tree — the logical operator tree (compact JSON)
    * execution_hints — extracted from the physical plan:
        * estimated_rows — total row count the engine expects to process
        * concurrency — parallelism hint (-1 = auto, 1 = parallel partitions)
        * spread — node spread hint (-1 = auto, 1 = distributed)
        * shard_scans — per-shard info: total_rows and has_selection (filter applied)
    * error — if the query has semantic errors (e.g., bad column name), this contains
        the error message. The query is NOT executed.

Critical:
* This does NOT execute the query — it only generates the plan.
* The plan shows the logical operators the engine would use.
* Use this to estimate cost and understand performance before running expensive queries.
* PlanSize indicates the overall plan complexity; RelopSize indicates the logical tree size.
* execution_hints.estimated_rows and shard_scans reveal the data volume the engine expects to scan.
* has_selection=true in shard_scans means a filter narrows the scan (extent pruning applies).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
cluster_uriYes
databaseNo
client_request_propertiesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that the tool does not execute the query, only generates the plan, and describes the return structure including error handling. No contradictions.

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

Conciseness5/5

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

The description is well-structured with sections (param, return, critical), uses bullet points for return keys, and is appropriately sized. 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?

Complexity is moderate with 4 params and a detailed return object. The description fully covers the purpose, parameters, return structure, and usage context. With an output schema present, the return documentation is sufficient. It clearly differentiates from siblings like kusto_query.

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 schema description coverage at 0%, the description manually documents each parameter (query, cluster_uri, database, client_request_properties) and the return value with detailed keys, adding significant 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 clearly states 'Retrieves the query execution plan without actually running the query', specifying the verb and resource. It distinguishes from execution by emphasizing that no query is run.

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?

It explicitly says 'This is significantly lighter than execution and useful for understanding performance characteristics and estimating query impact' and 'Critical: This does NOT execute the query'. This tells when to use (estimation) and when not (actual execution).

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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