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startreedata

StarTree MCP Server for Apache Pinot

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by startreedata

create-table-config

Generate table configurations for Apache Pinot using the MCP server, enabling streamlined setup by inputting JSON and optional validation skips.

Instructions

Create table configuration

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableConfigJsonYes
validationTypesToSkipNo

Implementation Reference

  • MCP tool handler for 'create_table_config' decorated with @mcp.tool. It validates inputs via type hints (serving as schema), registers the tool, and delegates execution to PinotClient.create_table_config.
    @mcp.tool
    def create_table_config(
        tableConfigJson: str, validationTypesToSkip: Optional[str] = None
    ) -> str:
        """Create table configuration"""
        try:
            results = pinot_client.create_table_config(
                tableConfigJson,
                validationTypesToSkip,
            )
            return json.dumps(results, indent=2)
        except Exception as e:
            return f"Error: {str(e)}"
  • Core helper function in PinotClient class that implements the HTTP POST request to the Pinot controller endpoint for creating a table configuration.
    def create_table_config(
        self,
        tableConfigJson: str,
        validationTypesToSkip: str | None = None,
    ) -> dict[str, Any]:
        url = f"{self.config.controller_url}/{PinotEndpoints.TABLES}"
        params: dict[str, str] = {}
        if validationTypesToSkip:
            params["validationTypesToSkip"] = validationTypesToSkip
        headers = self._create_auth_headers()
        headers["Content-Type"] = "application/json"
        response = requests.post(
            url,
            headers=headers,
            params=params,
            data=tableConfigJson,
            timeout=(self.config.connection_timeout, self.config.request_timeout),
            verify=True,
        )
        response.raise_for_status()
        try:
            return response.json()
        except requests.exceptions.JSONDecodeError:
            return {
                "status": "success",
                "message": "Table config creation request processed.",
                "response_body": response.text,
            }
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Create table configuration' implies a write operation but fails to describe any behavioral traits such as permissions required, whether the creation is idempotent, error handling, or side effects. This leaves critical operational context unspecified for a mutation tool.

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 with a single three-word phrase, 'Create table configuration', which is front-loaded and wastes no words. While it lacks detail, it efficiently communicates the core action without unnecessary elaboration, earning full marks for brevity and structure.

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

Completeness1/5

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

Given the tool's complexity as a mutation operation with two parameters, 0% schema coverage, no annotations, and no output schema, the description is severely incomplete. It does not compensate for the lack of structured data, failing to explain behavior, parameters, or outcomes, making it inadequate for effective tool use.

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%, meaning parameters are undocumented in the schema. The description adds no information about the two parameters ('tableConfigJson' and 'validationTypesToSkip'), their purposes, formats, or examples. For a tool with two parameters and no schema documentation, this represents a significant gap in parameter understanding.

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

Purpose2/5

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

The description 'Create table configuration' is a tautology that restates the tool name with minimal elaboration. It specifies the verb 'Create' and resource 'table configuration', but lacks specificity about what a table configuration entails or how it differs from sibling tools like 'create-schema' or 'update-table-config'. This provides only basic purpose without meaningful differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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 prerequisites, appropriate contexts, or exclusions, nor does it reference sibling tools like 'create-schema' or 'update-table-config' for comparison. Without any usage instructions, the agent lacks direction on tool 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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