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TunnelMind Data API

by ai.tunnelmind

intel_agent

Probes a domain for known AI agent integration signals: llms.txt, ai.txt, /.well-known/ai-plugin.json, openapi.json, swagger.json, MCP manifest, MCP SSE endpoint. Returns a score based on the count of signals detected. Use this to assess whether a domain is ready for agent-to-agent interaction.

Use this tool when:

  • You want to know whether a domain exposes an MCP server or OpenAPI spec for agents.

  • You are cataloguing the AI-agent-ready surface of a set of domains.

  • You need to decide whether to attempt programmatic API access to a domain.

Do NOT use this tool when:

  • You need tracker/surveillance data about the domain — use get_domain instead.

  • You need the robots.txt AI crawler policy — use intel_robots instead.

  • You need HTTP security posture — use intel_http instead.

Inputs:

  • domain (query, required): Domain to probe.

Returns:

  • Boolean flags per signal (llms_txt, ai_plugin, openapi, mcp_manifest, mcp_endpoint, mcp_sse).

  • agent_surface_score: integer 0-8, count of signals detected.

Cost:

  • Free. No API key required.

Latency:

  • Typical: 2-5s (parallel probes), p99: 8s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoWhen true, return a task handle immediately instead of blocking. Poll get_task for the result.
domainYes
receiptNoWhen true, attach a signed Receipt v1.0 committed to the transparency log. Additive — a signing failure never costs you the observation (ADR-014).

TDQS

A4.5/5.0
Behavior4/5

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

Since no annotations are provided, the description carries the full burden. It transparently discloses the signals probed, return shape (boolean flags plus agent_surface_score 0-8), cost (free), and latency (typical 2-5s, p99 8s). It does not detail failure modes or edge cases (e.g., unreachable domains), but the core behavioral profile is well covered.

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: purpose, when to use, when not to use, inputs, returns, cost, and latency. Every section earns its place, and the core purpose is front-loaded. No redundant or filler content.

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 the absence of an output schema, the description fully specifies the output (boolean flags and integer score) and provides cost and latency. It also covers use cases, alternatives, and input requirements. This is complete for a signature probe tool, leaving little ambiguity about behavior or results.

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

Parameters3/5

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

Schema description coverage is high (67%), and async/receipt already have descriptions in the schema. The description's Inputs section only mentions 'domain' as 'Domain to probe', adding marginal meaning beyond the schema's example. It doesn't comment on the async or receipt parameters at all, but they are adequately documented in the schema.

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?

States a specific verb ('Probes') and resource ('a domain for known AI agent integration signals'), enumerating the exact signals (llms.txt, ai.txt, etc.). Clearly differentiates from siblings like intel_http, intel_robots, and get_domain by naming them in the 'Do NOT use' section.

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?

Provides explicit 'Use this tool when' and 'Do NOT use this tool when' bullet lists, naming specific alternative tools (get_domain, intel_robots, intel_http) and the exact scenarios that call for them. This gives clear decision 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