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TunnelMind Data API

by ai.tunnelmind

snapshot_manifest

P4 corpus replication, the OPA "push data into the PDP" pattern. A daily snapshot of the domain corpus (domain, score, category, fingerprinting, entity) is published as deterministic JSONL with a manifest carrying row_count, sha256 over the exact bytes, a diff summary vs the previous day, and an Ed25519-signed Receipt v1.0 committed to the transparency log — a PDP that replicates the data can verify offline that it loaded exactly what was published.

date is YYYY-MM-DD or latest. Retention: 14 days. Fetch the rows from data_url, apply increments from diff_url ({"op":"+"|"~"|"-"} per line), re-pull the full file when the manifest marks the diff truncated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes

TDQS

B3.2/5.0
Behavior4/5

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

Despite no annotations, the description discloses several behavioral traits: manifest contains row_count, sha256, diff summary, and a signed receipt; date supports 'YYYY-MM-DD' or 'latest'; retention is 14 days; diff format is specified; and it notes to re-pull when truncated. This is substantial behavioral context, though the exact return format (JSON object?) is implied but not explicit.

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?

The description is a single dense paragraph mixing background, usage, and technical details. It could be split into clearer sections (purpose, usage, details) but is not overly long. Every sentence has information, but the structure reduces readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the manifest content, date argument, retention, and diff handling. However, it does not specify the exact JSON structure of the manifest or mention any error cases. Without output schema, more detail on the return value would improve completeness.

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?

The input schema only has 'date' with no description. The description adds meaning by specifying the format ('YYYY-MM-DD' or 'latest') and implying it selects the snapshot day. This compensates well for the 0% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states that the tool provides a manifest for a daily snapshot of a domain corpus, but it is buried in jargon about replication patterns. The verb is not explicitly stated (e.g., 'fetch manifest'), and it does not clearly distinguish from sibling tools like snapshot_data or snapshot_diff. The purpose is inferable but not immediately clear.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. It describes how to use the manifest (fetch rows, apply diffs), but does not say when to choose snapshot_manifest over other snapshot-related tools. Siblings are not mentioned or differentiated.

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