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TunnelMind Data API

by ai.tunnelmind

audit_export

Returns NDJSON (one JSON object per line) of audit log entries. Each entry records the operation called, the identity, hashes of the request and response, duration, and an Ed25519 signature over the canonical entry JSON. Entries are hash-chained: each entry's prev_entry_hash is SHA-256 of the previous entry's signature, making deletion of any entry detectable offline.

Authenticated callers receive only their own entries (identity_sub match). Admin key holders receive all entries.

Use this tool when:

  • You want a tamper-evident record of your own API calls.

  • You are auditing a sequence of requests for compliance or debugging.

  • You want to verify the audit chain integrity offline.

Do NOT use this tool when:

  • You are anonymous — authentication is required.

  • You want task status — use get_task instead.

Inputs:

  • from (query, optional): ISO 8601 start datetime. Default: 7 days ago.

  • to (query, optional): ISO 8601 end datetime. Default: now.

  • limit (query, optional): Max entries. 1–5000, default 1000.

Returns:

  • NDJSON stream, one AuditEntry per line.

  • X-Total-Count response header with entry count.

  • X-Took-Ms response header.

Verify the chain offline:

  • For each consecutive pair (A, B): SHA-256(A.signature) == B.prev_entry_hash.

  • For each entry: verify Ed25519 signature against public key in /.well-known/atap.json.

Cost:

  • Counts as one request against the daily limit.

Latency:

  • Typical: <300ms for 1000 entries, p99: <1s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
fromNo
limitNo

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure, and it excels: it explains the hash-chaining and deletion detection, authentication scoping (own entries vs admin), NDJSON stream format, response headers, offline verification procedure, cost counting, and latency profile. This is far beyond minimal transparency.

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?

Although lengthy, the description is well-structured with clear sections (description, use cases, inputs, returns, verification, cost, latency), front-loaded with the core purpose. Every sentence adds operational value—no filler. The structure makes it easy to scan and ingest.

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 tool's complexity, absent annotations, and lack of output schema, the description is remarkably complete: it covers inputs, output format, HTTP headers, chain verification logic, authentication behavior, rate-limit cost, and typical latency. There are no significant gaps for an agent to select and invoke this tool 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 description coverage is 0%, so the description must compensate. It does: every parameter (`from`, `to`, `limit`) is explained with format, default, and constraints (e.g., 'default 7 days ago', '1–5000, default 1000'). This gives the agent full comprehension without needing to infer from 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?

The description opens with a specific verb and resource: 'Returns NDJSON (one JSON object per line) of audit log entries.' It goes on to detail what each entry records, making the tool's purpose unmistakable. It also distinguishes itself from siblings by explicitly saying 'use `get_task` instead' for task status, which differentiates it from at least one alternative.

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 provides an explicit 'Use this tool when:' and 'Do NOT use this tool when:' section, including a concrete alternative (`get_task`). It also states the authentication requirement, clarifying a key condition for use. This is exemplary usage 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