Skip to main content
Glama

TunnelMind Data API

by ai.tunnelmind

agent_registries_lookup

P75 registry aggregation: the cross-lens join applied to agent identity. Incumbent providers each consult only their own registry; this endpoint checks every registry TunnelMind can reach and labels each answer with an explicit state, so a gap can never be mistaken for a clean result:

  • observed — the subject IS in this registry (record attached)

  • not_present — checked, and it isn't (an observation, not a gap)

  • not_applicable — the registry doesn't key on this subject type

  • degraded — the check failed (timeout, upstream error)

  • unavailable — the registry is not publicly consultable (closed / platform-scoped); stated in-band because silence would read as clean

Registries (v0): crawler_ranges (operator-published IP feeds — Googlebot, GPTBot, PerplexityBot…), wba_directory (RFC 9421 Signature-Agent JWKS at the subject domain), mcp_registry (registry.modelcontextprotocol.io), tunnelmind_known_agents (curated verifiable/claim-only set), plus honest unavailable rows for Visa TAP, Mastercard Agent Pay, and Cloudflare Verified Bots.

Use this tool when:

  • You are deciding whether an agent, bot, or MCP server is registered anywhere that vouches for it — in one call instead of five.

  • You need the blind spots stated: which registries could NOT be consulted for this subject, and why.

Inputs: subject — an IP (registry membership by published ranges), a domain (WBA directory + MCP registry), or an agent name/UA fragment (e.g. gptbot, claudebot). ?receipt=true attaches a signed Receipt v1.0 committed to the transparency log.

Latency: remote registry checks are KV-cached for 1h; a warm call is edge-fast, a cold one bounded by 5s per-registry timeouts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
receiptNoAttach a signed TunnelMind Receipt v1.0 over the aggregation.
subjectYesIP address, domain, or agent name / User-Agent fragment.

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description fully covers behavior: it checks all reachable registries, returns explicit states (observed, not_present, etc.), lists specific registries, mentions caching (KV-cached 1h), latency bounds (5s cold), and receipt option. 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with bullet points but verbose (4 paragraphs). Some redundancy, e.g., repeating cross-lens concept. Front-loaded with purpose, but could be trimmed while retaining key details.

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 comprehensively explains all possible answer states, registries checked, and latency behavior. It provides sufficient context for an AI agent to understand tool's scope and results.

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?

Schema coverage is 100% with descriptions, but the description adds context: explains subject types (IP, domain, agent name/UA fragment) and receipt as 'signed Receipt v1.0 committed to transparency log', enhancing understanding beyond the enum.

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 performs a cross-lens join across multiple registries to check agent/bot/MCP server registration. It distinguishes from siblings by contrasting with 'incumbent providers each consult only their own registry' and positions this as a single-call aggregation.

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

Usage Guidelines4/5

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

Explicit bullet points for when to use: deciding on registration status and needing blind spots. Implicitly contrasts with checking registries individually, but does not name specific alternative tools or explicitly state when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All TunnelMind Data API toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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