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DataSearcher MCP

DataSearcher MCP

by MarkIvor
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

load_dbt

Load dbt manifest.json metadata into the Knowledge Base, enabling AI assistants to query and analyze dbt models using natural language.

Instructions

Загрузить метаданные из dbt manifest.json в Knowledge Base (ТЗ 1.2).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "load_dbt"
  }
}

Input Schema

NameRequiredDescriptionDefault
manifest_pathNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.7/5.0
Behavior2/5

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

Annotations are absent, so the description must carry the full behavioral burden. It only discloses that metadata is loaded, but not whether the load overwrites existing content, merges, or requires any prerequisites. Idempotency, permissions, and failure behavior are left unstated.

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 a single, front-loaded sentence that conveys the core action and resource. The parenthetical 'ТЗ 1.2' adds little for an external agent, slightly reducing efficiency, but the overall structure is compact.

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?

An output schema exists, but the absence of annotations and the one-line description leave critical context missing for a mutating tool. An agent knows what to load but not the side effects on the Knowledge Base, whether data is replaced, or what preconditions must hold.

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 coverage is 0%, so the description should compensate by explaining manifest_path. It only indirectly references the dbt manifest file and says nothing about the parameter's optionality or default empty-string behavior.

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 states a clear action ('load'), a specific source ('dbt manifest.json'), and a destination ('Knowledge Base'). It is specific enough to distinguish from generic siblings like load_file or sync_datahub, though it does not explicitly name an alternative.

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 given on when to use this tool versus load_file, sync_datahub, or update_metadata. The 'ТЗ 1.2' reference is an internal spec label that provides no actionable usage context for an AI agent.

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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