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TunnelMind Data API

sigil_traverse

Reconstructs the supply paths for a publisher domain from Sigil's own crawl and returns them ITEMIZED — distinct from sigil_verify_supply_chain (which verifies a schain the caller brings) and from signal_dark_pool_risk (which returns only aggregate counts). Every SSP the publisher declares it sells through is joined to that SSP's identity and classified two-sided against the SSP's sellers.json: corroborated (seat present), contradicted (SSP crawled but seller_id absent — real risk), unchecked (SSP not yet crawled — not risk). Each returned path also carries resells_to, one level of downstream reseller expansion.

The list is ordered riskiest-first (contradicted, then reseller) so a truncated page is still the most useful; the supply_paths counts are always over the FULL set. in_supply_graph:false when the domain is not a known publisher.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax paths returned (default 200, hard cap 500).
domainYesPublisher hostname to traverse.

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are provided, but the description compensates with rich behavioral detail: the three-way classification (corroborated/contradicted/unchecked), the inclusion of `resells_to`, risk-first ordering, and the `in_supply_graph:false` sentinel for unknown domains. It also notes that counts are over the full set even when results are truncated, which is important for interpretation.

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 information-dense but every sentence earns its place: differentiation, classification, ordering, sentinel value, and full-set counts. It is organized into two logical paragraphs with no filler, tautology, or repetition.

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?

With no output schema, the description adequately explains the key return semantics: itemized paths, classification categories, the `resells_to` field, and the unknown-domain flag. It also explains the ordering rationale for truncated results, which is essential for agents acting on partial data. The tool is fully contextualized despite lacking annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents both parameters at 100% (domain and limit), so the description carries less burden. It adds contextual meaning by explaining what the tool does with the domain and how the limit affects ordering, but does not introduce new parameter-level syntax beyond the schema. Baseline 3 is appropriate.

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 uses a specific verb ('Reconstructs') and clearly names the resource ('supply paths for a publisher domain from Sigil's own crawl'). It explicitly distinguishes itself from two sibling tools by naming them and describing their different purposes, making selection unambiguous.

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 explicit exclusions: 'distinct from sigil_verify_supply_chain (which verifies a schain the caller brings) and from signal_dark_pool_risk (which returns only aggregate counts).' This tells the agent exactly when not to choose this tool and explains what the alternatives do, offering clear context for selection.

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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