Skip to main content
Glama

TunnelMind Data API

by ai.tunnelmind

ghostroute_ct_proofs

Returns GhostRoute's per-cert inclusion proofs: each is a cryptographic demonstration that the exact certificate a host serves is included in an append-only CT log whose root TunnelMind signature-verified — upgrading "a monitor said this cert exists" to "proven in a log we witness". Failed attempts are included with a reason; a cert that suddenly cannot be proven is itself a signal.

Use this tool when:

  • You want to know whether a specific AI host's live cert is provably logged (pass domain), or

  • You want the corpus-wide proof rollup across watched hosts (omit domain).

Inputs:

  • domain (query, optional): a hostname to filter to; omit for corpus-wide.

  • limit (query, optional): max recent rows, 1–200, default 50.

Returns:

  • domain (echo, null when corpus-wide).

  • summary: total_attempts, proven, unproven, domains, last_observed_at.

  • recent[]: recent attempts (log_operator, leaf_index, tree_size, sth_root_hash, inclusion_proven, reason, ...).

  • by_domain[]: per-host attempts / proven rollup.

Latency:

  • Typical <300ms (KV-cached 5m).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
domainNo

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries full burden and does so richly: it discloses failed attempts include a `reason`, that a cert suddenly unprovable is itself a signal, and that results are KV-cached for 5 minutes with typical latency under 300ms. This goes beyond a simple read operation.

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 (Use, Inputs, Returns, Latency) and front-loaded purpose. Every sentence contributes meaningful context, from the cryptographic explanation to latency boundaries, with no filler.

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 and no annotations, the description provides a complete picture: purpose, when to use, input semantics, output field details, interpretation of failures, and performance expectations. It is self-sufficient for an AI agent to select and invoke correctly.

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?

Schema coverage is 0%, but the description fully compensates: `domain` is explained as an optional hostname filter, while `limit` is documented as max recent rows with range 1–200 and default 50. This adds real meaning beyond the bare 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?

The description opens with a specific verb and resource: 'Returns GhostRoute's per-cert inclusion proofs.' It clearly distinguishes this from sibling tools by emphasizing cryptographic demonstration of CT log inclusion, which differs from alerts, witness, and verification tools.

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 'Use this tool when' bullets provide clear use cases: checking a specific domain's live cert provability or getting a corpus-wide rollup. It doesn't name alternative sibling tools or state when not to use it, but the context is clear and actionable.

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