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alilxxey

openobserve-community-mcp

list_streams

Retrieve available data streams in your OpenObserve organization to monitor logs and traces, with filtering and pagination options.

Instructions

List streams available in the current organization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stream_typeNologs
keywordNo
offsetNo
limitNo
sortNoname
include_rawNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The MCP tool definition for `list_streams` in `openobserve_mcp/server.py`.
    @server.tool()
    def list_streams(
        stream_type: str = "logs",
        keyword: str = "",
        offset: int = 0,
        limit: int = 50,
        sort: str = "name",
        include_raw: bool = False,
    ) -> dict[str, Any]:
        """List streams available in the current organization."""
        client = client_provider.get()
        raw = client.list_streams(
            stream_type=stream_type,
            keyword=keyword,
            offset=offset,
            limit=limit,
            sort=sort,
        )
        return build_list_streams_result(
            org_id=client.resolve_org_id(),
            stream_type=stream_type,
            raw=raw,
            include_raw=include_raw,
        )
  • The underlying OpenObserve API client method that performs the network request for `list_streams`.
    def list_streams(
        self,
        *,
        stream_type: str,
        keyword: str = "",
        offset: int = 0,
        limit: int = 50,
        sort: str = "name",
    ) -> Any:
        return self.request_json(
            "GET",
            self._org_path("/api/{org_id}/streams"),
            query={
                "type": stream_type,
                "keyword": keyword,
                "offset": offset,
                "limit": limit,
                "sort": sort,
            },
        )
Behavior2/5

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 only states the basic action of listing streams, without mentioning permissions needed, rate limits, pagination behavior (implied by offset/limit parameters but not described), or what 'include_raw' entails. This is inadequate for a tool with multiple parameters and no annotation coverage.

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 a single, clear sentence with no wasted words. It's front-loaded with the core purpose, making it easy to scan and understand quickly, which is ideal for conciseness.

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

Completeness3/5

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

Given the tool has an output schema (which handles return values), 6 parameters with low coverage, and no annotations, the description is minimally adequate but incomplete. It states what the tool does but lacks details on behavior, parameter usage, and differentiation from siblings, making it insufficient for full contextual understanding.

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?

The description adds no parameter semantics beyond the basic action. With 6 parameters and 0% schema description coverage, the schema provides only titles and types without explanations. The description doesn't compensate by explaining what 'stream_type', 'keyword', 'include_raw', etc., mean or how they affect the listing, leaving parameters largely undocumented.

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 action ('List') and resource ('streams available in the current organization'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'get_stream_schema' or 'search_logs', which might also relate to streams, 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 no guidance on when to use this tool versus alternatives like 'search_logs' or 'get_stream_schema'. It mentions 'current organization' as context but doesn't specify prerequisites, exclusions, or comparative use cases with siblings, leaving the agent to infer usage.

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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