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alilxxey

openobserve-community-mcp

list_dashboards

Retrieve dashboard listings from OpenObserve Community Edition to monitor and analyze organizational data visualizations.

Instructions

List dashboards in the current organization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
folderNo
titleNo
page_sizeNo
include_rawNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The low-level API client method that performs the actual network request to list dashboards.
    def list_dashboards(
        self,
        *,
        folder: str | None = None,
        title: str | None = None,
        page_size: int | None = None,
    ) -> Any:
        query: dict[str, str | int | float | bool] = {}
        if folder:
            query["folder"] = folder
        if title:
            query["title"] = title
        if page_size is not None:
            query["pageSize"] = page_size
    
        return self.request_json(
            "GET",
            self._org_path("/api/{org_id}/dashboards"),
            query=query or None,
        )
  • The MCP tool handler function that calls the client and formats the response using the build_list_dashboards_result helper.
    def list_dashboards(
        folder: str | None = None,
        title: str | None = None,
        page_size: int | None = None,
        include_raw: bool = False,
    ) -> dict[str, Any]:
        """List dashboards in the current organization."""
        client = client_provider.get()
        raw = client.list_dashboards(folder=folder, title=title, page_size=page_size)
        return build_list_dashboards_result(
            org_id=client.resolve_org_id(),
            raw=raw,
            include_raw=include_raw,
        )
Behavior2/5

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 states it's a list operation, implying read-only behavior, but doesn't mention pagination (despite a 'page_size' parameter), rate limits, authentication requirements, or what 'include_raw' entails. For a tool with 4 parameters and no annotation coverage, this leaves significant gaps in understanding its behavior.

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, efficient sentence that front-loads the core purpose. There is zero waste or redundancy, making it highly concise and well-structured for quick understanding.

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's moderate complexity (4 parameters, no annotations, but with an output schema), the description is incomplete. It covers the basic purpose but lacks parameter explanations, usage guidelines, and behavioral details. The presence of an output schema means return values are documented elsewhere, but the description doesn't add enough context to compensate for other gaps.

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 description coverage is 0%, so the description must compensate for undocumented parameters. It adds no information about parameters like 'folder', 'title', 'page_size', or 'include_raw'. The description only mentions the organizational scope, which doesn't map to any specific parameter. With 4 parameters and no schema descriptions, this is inadequate.

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 ('dashboards') with scope ('in the current organization'). It distinguishes from siblings like 'get_dashboard' by indicating it returns multiple items rather than a single one. However, it doesn't explicitly differentiate from other list/search tools like 'list_streams' or 'search_logs' in terms of what makes dashboards unique.

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. It doesn't mention when to prefer 'list_dashboards' over 'search_around' or 'search_logs' for dashboard-related queries, nor does it specify prerequisites like authentication or organizational context. Usage is implied by the name but not explicitly stated.

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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