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TunnelMind Data API

verify_receipt

Tamper-detection verification for TunnelMind surveillance receipts. Submit the receipt ID, the SHA-256 content hash, and the Ed25519 signature from the receipt document. The registry compares these against what was recorded at issuance time. Returns VALID if both match exactly, INVALID with a specific mismatch reason otherwise.

Use this tool when:

  • You received a surveillance receipt document and want to verify it hasn't been altered.

  • You are programmatically checking receipt authenticity in an agent workflow.

  • You want to prove to a third party that a receipt is genuine.

Do NOT use this tool when:

  • You only want to check existence — use get_receipt instead (no body required).

Inputs:

  • receipt_id (body, required): The receipt's ID field from the document.

  • content_hash (body, required): SHA-256 hex hash of the receipt JSON. Max 256 chars.

  • signature (body, required): Ed25519 signature from the receipt document. Max 512 chars.

Returns:

  • valid: boolean. True only if both hash and signature match exactly.

  • status: VALID or INVALID.

  • message: human-readable explanation. On INVALID, specifies whether the hash mismatched, the signature mismatched, or both.

Cost:

  • Free. No API key required.

Latency:

  • Typical: <100ms, p99: <300ms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signatureYesEd25519 signature from the receipt document
receipt_idYes
content_hashYesSHA-256 hex hash of the receipt JSON content

TDQS

A5/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses the verification mechanism (compares against issuance record), the output semantics (VALID/INVALID with specific mismatch reason), and additional operational details (free, no API key, latency). This is a complete and transparent behavioral disclosure.

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 (overview, when-to-use, when-not-to-use, inputs, returns, cost, latency). Every sentence provides necessary information; there is no fluff or redundancy. The front-loaded purpose sentence allows quick comprehension.

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 (3 required params, no output schema, no annotations), the description covers all necessary contexts: input semantics, output format, mismatch behavior, cost, latency, and alternatives. It is fully self-sufficient for an agent to select and invoke the 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?

The description adds meaning beyond the schema by explaining each parameter's source ('from the receipt document'), specifying 'body' placement, reiterating requiredness, and providing max lengths. It compensates for the 33% of schema parameters lacking descriptions, especially `receipt_id`, which only has an example 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?

The description opens with a clear verb and object: 'Tamper-detection verification for TunnelMind surveillance receipts.' It specifies exactly what the tool does (verify authenticity against issuance record) and distinguishes it from the sibling `get_receipt` (existence check without body). This makes it unambiguous and sibling-differentiated.

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 guidance, including a specific alternative (`get_receipt`). This satisfies the highest bar for usage guidance with both positive and negative cases.

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.

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