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pbi_locate_pbip

Locate PBIP source code for a named dataset and optionally read a specific table's TMDL with its M source.

Instructions

Locate PBIP source code for a dataset (and optionally read a specific table's TMDL + M source).

Requires 'pbip_root' configured in config.json (path to local PBIP repo root, e.g. 'C:/repos/my-project/data/power-bi-report').

Args: dataset_name: Dataset name as it appears in Power BI Service (e.g. 'QC Compliance Report'). Folder match is fuzzy: case-insensitive + non-alphanumeric stripped. table_name: Optional table name. If provided and dataset found, reads that table's .tmdl and extracts its partition M source.

Returns: JSON with {status, matches[], optional table content + partition_source_m}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_nameYes
table_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It details the fuzzy matching algorithm for dataset names and the optional table reading behavior. It specifies the return JSON structure. It could further clarify that the tool is read-only, but the given detail is substantial.

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 concise, with clear sections (Args, Returns) and no extraneous information. The purpose is front-loaded, and every sentence adds value. It is structured for quick comprehension.

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?

Despite having an output schema, the description still summarizes the return values, enhancing completeness. All necessary context is provided: prerequisites, parameter details, behavior, and output structure. The tool's complexity is low, and the description covers it fully.

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?

The input schema provides no parameter descriptions (0% coverage), so the description fully compensates. It explains dataset_name's fuzzy matching rule and table_name's optional nature and effect, 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 the tool's verb 'Locate PBIP source code' and specifies the optional ability to read a specific table's TMDL and M source. It distinguishes from sibling tools like pbi_list_datasets, which list remote datasets, by focusing on local PBIP source code.

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 lists the prerequisite 'pbip_root' configuration and describes parameter usage, but does not explicitly state when to use this tool versus alternatives or when not to use it. This leaves room for ambiguity about selection criteria.

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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