Skip to main content
Glama

get_data_latency

Read-onlyIdempotent

Retrieve current WebSocket and REST API latency metrics, plus data freshness lag per type for supported venues.

Instructions

Get current latency metrics for supported venue APIs. Returns WebSocket latency (current, 1h avg, 24h avg), REST API latency, and data freshness lag per data type (orderbook, fills, funding, OI).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesResult data object

Implementation Reference

  • src/index.ts:2029-2038 (registration)
    Tool registration for 'get_data_latency' using registerTool. Defined at line 2029 with description at 2031, inputSchema {} (no params) at 2032, outputSchema ObjectOutputSchema at 2033, and handler at lines 2034-2037 that calls api().dataQuality.latency() and formats the response.
    registerTool(
      "get_data_latency",
      "Get current latency metrics for supported venue APIs. Returns WebSocket latency (current, 1h avg, 24h avg), REST API latency, and data freshness lag per data type (orderbook, fills, funding, OI).",
      {},
      ObjectOutputSchema,
      async () => {
        const data = await api().dataQuality.latency();
        return formatResponse(data);
      }
    );
  • The actual handler function for get_data_latency. It calls the SDK method api().dataQuality.latency() and wraps the result via formatResponse() which formats as both text and structured content.
    async () => {
      const data = await api().dataQuality.latency();
      return formatResponse(data);
    }
  • Output schema ObjectOutputSchema used by get_data_latency: returns a single object with a 'data' field.
    const ObjectOutputSchema: ZodRawShape = {
      data: z.record(z.unknown()).describe("Result data object"),
    };
  • The registerTool helper function that wraps the SDK call and adds API key guard, error handling, and schema validation for every tool including get_data_latency.
    function registerTool(
      name: string,
      description: string,
      inputSchema: ZodRawShape,
      outputSchema: ZodRawShape,
      handler: (params: any) => Promise<McpContent>
    ): void {
      server.registerTool(
        name,
        {
          description,
          inputSchema,
          outputSchema,
          annotations: TOOL_ANNOTATIONS,
        },
        async (params: any) => {
          if (!client) {
            return {
              content: [{ type: "text" as const, text: MISSING_KEY_MESSAGE }],
              isError: true,
            };
          }
          try {
            return await handler(params);
          } catch (err) {
            const error = err instanceof OxArchiveError ? err : new OxArchiveError(String(err), 500);
            return formatError(error);
          }
        }
      );
    }
  • The formatResponse helper used by the handler to format the latency data into both text and structuredContent output.
    function formatResponse(
      data: unknown,
      meta?: { nextCursor?: string; paginated?: boolean }
    ): McpContent {
      let header = "";
      let body: unknown = data;
    
      if (Array.isArray(data)) {
        header = `Returned ${data.length} record${data.length !== 1 ? "s" : ""}`;
        // Only truncate paginated endpoints — the user can cursor for more.
        // Non-paginated results (instruments, current snapshots) return everything.
        if (meta?.paginated && data.length > MAX_PAGINATED_ITEMS) {
          header += ` (showing first ${MAX_PAGINATED_ITEMS}; use cursor to get more)`;
          body = data.slice(0, MAX_PAGINATED_ITEMS);
        }
      }
    
      if (meta?.nextCursor) {
        header += header
          ? `\nNext page cursor: "${meta.nextCursor}"`
          : `Use cursor: "${meta.nextCursor}" to get the next page`;
      }
    
      const json = JSON.stringify(body, null, 2);
      const text = header ? `${header}\n\n${json}` : json;
    
      // Build structuredContent matching ListOutputSchema or ObjectOutputSchema
      const structuredContent: Record<string, unknown> = Array.isArray(data)
        ? {
            records: body,
            count: data.length,
            ...(meta?.nextCursor && { nextCursor: meta.nextCursor }),
          }
        : { data };
    
      return { content: [{ type: "text", text }], structuredContent };
    }
Behavior4/5

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

Annotations already indicate readOnly, non-destructive, idempotent, and open-world behavior. The description adds value by specifying the exact metrics returned (e.g., current, 1h avg, 24h avg for WebSocket latency, REST API latency, and per-type data freshness). No contradictions.

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?

A single sentence that front-loads the core action and details. Every word provides value; no redundancy.

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

Completeness5/5

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

With no parameters, annotations present, and an output schema, the description gives sufficient detail about what the tool returns. The rule states that if output schema exists, return value explanation is not needed, so completeness is high.

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

Parameters4/5

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

Input schema has zero parameters, so schema coverage is 100%. The description does not need to add parameter semantics. Baseline 4 is appropriate.

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

Purpose5/5

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

The description clearly states the tool retrieves current latency metrics for venue APIs, listing specific metrics (WebSocket latency, REST API latency, data freshness lag per data type). This distinguishes it from sibling tools that focus on other data (candles, orderbooks, etc.).

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 on when to use this tool versus siblings like 'get_freshness' or 'get_data_quality_status'. The description does not provide context about appropriate use cases or exclusions.

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/0xArchiveIO/0xarchive-mcp'

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