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TunnelMind Data API

by ai.tunnelmind

stream_task

Opens a persistent SSE connection that emits events as the task progresses. The stream closes automatically when the task reaches a terminal state or after ~90 seconds (timeout). Heartbeat comments are sent every ~15 seconds to keep the connection alive through proxies.

Event types:

  • status — emitted when status changes (pending → running → complete/failed)

  • result — emitted on complete with the full result payload

  • error — emitted on failed, cancelled, or expired with error info

  • SSE comment (: heartbeat) — keepalive, no data

Use this tool when:

  • You want real-time progress without polling.

  • You are in an environment that supports SSE (EventSource API).

Do NOT use this tool when:

  • You want a simple one-shot status check — use get_task instead.

  • Your HTTP client doesn't support streaming responses.

Inputs:

  • task_id (path, required): 26-char ULID.

Returns:

  • SSE stream (text/event-stream). Each event is event: <type>\\ndata: <json>\\n\\n.

Cost:

  • Free. Counts as one request against rate limits when the stream opens.

Latency:

  • First event: <200ms. Stream duration: up to 90s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYes

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses the connection lifecycle (persistent SSE, auto-close on terminal state or ~90s timeout), keepalive heartbeats every ~15s, event types, cost (free but counts against rate limits), and latency. It also explains the SSE event format. This goes well beyond minimal disclosure.

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 well-structured with clear sections (overview, event types, usage guidance, inputs, returns, cost, latency). Every sentence provides useful information without fluff. It is front-loaded with the primary purpose and organized for easy scanning.

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?

Given no output schema, the description fully explains the return value (SSE stream format, event types, data structure) and also covers timeout, heartbeat, cost, and latency. It addresses terminal states and error conditions, making it complete for an agent to invoke the tool correctly.

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?

The schema coverage is 0% (no property descriptions), so the description must compensate. It does add value by clarifying `task_id` as a path parameter and restating the 26-char ULID requirement, which is helpful context beyond the raw schema. However, it does not offer examples or further semantics, but for a single simple parameter this is adequate.

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 opens with 'Opens a persistent SSE connection that emits events as the task progresses,' which clearly states the tool's purpose with a specific verb and resource. It also distinguishes itself from siblings by explicitly naming `get_task` as the alternative for one-shot status checks.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes dedicated 'Use this tool when' and 'Do NOT use this tool when' sections, providing explicit guidance on when to stream events versus when to poll with `get_task`, and cautions against use when SSE streaming is unsupported. This is exemplary alternative-selection guidance.

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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TDQS

B3.3/5.0
Disambiguation2/5

Many tools overlap in purpose, such as cross_lens_verify, cross_lens_lookup, profile_entity, and preflight_should_i_act, which all return node verdicts with subtle differences. Sigil verification tools and receipt-related tools also have similar names and require deep reading to distinguish.

Naming Consistency3/5

The tool names are mostly readable, but the pattern is mixed: some use verb_noun (get_domain, create_subscription) while others use domain prefixes (sigil_*, ghostroute_*, intel_*). Within each domain, naming is consistent, but the overall style lacks uniformity.

Tool Count1/5

With 90 tools, this server is extremely overloaded. Even for a multi-purpose data API, the sheer number overwhelms and makes navigation difficult, far exceeding the typical well-scoped MCP server. The count is an extreme mismatch for the apparent scope.

Completeness4/5

The tool surface is very comprehensive, covering tracker lookup, cross-lens verification, receipts, compliance, subscriptions, tasks, intel probes, and more. Minor gaps exist, such as no batch cross-lens verification, but core workflows are well covered.

Resources