Skip to main content
Glama
alilxxey

openobserve-community-mcp

get_stream_schema

Retrieve schema details for a specific data stream to understand its structure and available fields.

Instructions

Get schema information for a specific stream. Increase fields_limit to inspect more fields from large schemas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stream_nameYes
fields_limitNo
include_rawNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for get_stream_schema which calls the OpenObserve client to fetch the schema.
    def get_stream_schema(
        stream_name: str,
        fields_limit: int = 100,
        include_raw: bool = False,
    ) -> dict[str, Any]:
        """Get schema information for a specific stream. Increase fields_limit to inspect more fields from large schemas."""
        client = client_provider.get()
        raw = client.get_stream_schema(stream_name=stream_name)
        return build_stream_schema_result(
            org_id=client.resolve_org_id(),
            stream_name=stream_name,
            raw=raw,
            fields_limit=fields_limit,
            include_raw=include_raw,
        )
  • Low-level API client method to fetch the schema of a stream.
    def get_stream_schema(self, *, stream_name: str) -> Any:
        return self.request_json(
            "GET",
            self._org_path("/api/{org_id}/streams/{stream_name}/schema", stream_name=stream_name),
        )
Behavior3/5

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. It adds some context by mentioning that increasing 'fields_limit' helps inspect more fields from large schemas, which hints at performance or output limitations. However, it doesn't cover other behavioral traits such as error handling, rate limits, authentication needs, or what the output looks like (though an output schema exists, reducing the need for detailed return value explanation). The description is adequate but lacks depth.

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 highly concise and well-structured, consisting of two sentences that directly address the tool's purpose and a key parameter tip. Every sentence earns its place by providing essential information without redundancy or fluff, making it easy for an AI agent to parse and understand quickly.

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 the tool's moderate complexity (3 parameters, 1 required), no annotations, and the presence of an output schema, the description is reasonably complete. It covers the core purpose and offers a practical tip for parameter usage. The output schema likely handles return value details, so the description doesn't need to explain those. However, it could benefit from more behavioral context or usage guidelines to be fully comprehensive.

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

Parameters3/5

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

Schema description coverage is 0%, meaning the input schema provides no descriptions for parameters. The description adds some semantic value by explaining that increasing 'fields_limit' helps inspect more fields from large schemas, which clarifies the purpose of that parameter. However, it doesn't explain 'stream_name' or 'include_raw,' leaving two parameters without additional meaning. With 0% coverage, the description partially compensates but not fully, aligning with the baseline for moderate compensation.

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 clearly states the tool's purpose: 'Get schema information for a specific stream.' It uses a specific verb ('Get') and resource ('schema information for a specific stream'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'list_streams' or 'search_around,' which might also involve stream-related operations, so it doesn't reach the highest score.

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?

The description provides minimal usage guidance: it mentions increasing 'fields_limit' for large schemas, which implies a context for parameter adjustment. However, it doesn't specify when to use this tool versus alternatives (e.g., 'list_streams' for listing streams or 'search_around' for broader queries), nor does it outline prerequisites or exclusions. This lack of explicit guidance limits its helpfulness 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.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/alilxxey/openobserve-community-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server