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TunnelMind Data API

by ai.tunnelmind

get_task

Returns the current status of a task created by an ?async=true intel request. Poll this endpoint until status is one of: complete, failed, cancelled, expired. On complete, the result field contains the same payload the sync endpoint would have returned. On failed, error.message explains the failure.

Use this tool when:

  • You submitted an intel probe with ?async=true and need to retrieve the result.

  • You want to check whether a background task finished without opening an SSE stream.

Do NOT use this tool when:

  • You want real-time event streaming — use stream_task instead.

  • You have no task_id — submit a probe with ?async=true first.

Inputs:

  • task_id (path, required): 26-char ULID returned in the 202 response.

Returns:

  • status: pending | running | complete | failed | cancelled | expired.

  • result: populated when status is complete. Null otherwise.

  • error: populated when status is failed. Null otherwise.

  • expires_at: tasks expire 1 hour after creation.

Cost:

  • Free. Does not count against rate limits.

Latency:

  • Typical: <100ms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYes

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses behavior: polling semantics, terminal statuses, result/error fields, expiration time, cost (free), and latency. This goes well beyond the minimal expectation.

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?

Well-organized with clear sections (Inputs, Returns, Cost, Latency). Every sentence contributes valuable information without verbosity, making it easy to scan and understand.

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?

Despite no output schema, the description enumerates all possible statuses and the conditions for `result` and `error` fields, making the tool's behavior fully comprehensible. Cost and latency further enhance completeness.

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

Parameters5/5

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

The single parameter `task_id` is explained as a 26-char ULID returned in the 202 response, adding useful origin context beyond the schema's pattern and example. With 0% schema description coverage, this compensation is strong.

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 'Returns the current status of a task created by an `?async=true` intel request', which is a specific verb and resource. It distinguishes itself from sibling tools by explicitly contrasting with `stream_task`.

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?

Explicit 'Use this tool when' and 'Do NOT use this tool when' sections provide clear context and name alternatives like `stream_task`. It also specifies prerequisites, such as having a task_id from an async request.

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