Skip to main content
Glama

TunnelMind Data API

by ai.tunnelmind

search

Searches both the domains table and the entities table simultaneously. Returns matching domains (by domain name) and entities (by name or slug) in a single response. Minimum 2 characters, maximum 100 characters.

Use this tool when:

  • You have a partial name and need to identify what tracker or entity it belongs to.

  • You want to find all TunnelMind records related to a company name like "Google" or "Oracle".

  • You are resolving an ambiguous domain (e.g., does criteo.com appear in the tracker DB?).

Do NOT use this tool when:

  • You know the exact domain — use get_domain instead (faster, more complete).

  • You know the exact entity slug — use get_entity instead.

  • You want to browse by category or industry — use list_domains or list_entities.

Inputs:

  • q (query, required): Search string, 2-100 characters. Matched against domain names and entity names/slugs.

Returns:

  • domains: array of matching domain records (list item format).

  • entities: array of matching entity records (list item format).

  • Both arrays may be empty if no matches found. No pagination — results are capped at 20 per type.

Cost:

  • Free tier: included in 50 req/day. Pro/enterprise: included in plan.

Latency:

  • Typical: <200ms, p99: <500ms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYes

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries full burden and delivers rich behavioral detail: simultaneous table search, matching logic, character limits, return arrays with a 20-item cap and no pagination, plus cost and latency expectations. This goes far beyond basic safety traits and gives the agent a clear mental model of behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (summary, usage, inputs, returns, cost, latency) and front-loaded with a concise one-sentence summary. However, the character constraint is repeated twice (intro and Inputs) and some sections (Cost, Latency) could be trimmed, making it slightly longer than necessary but still efficient.

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?

The description is comprehensive: no output schema exists, so it explains return arrays and their item format, clarifies empty array behavior and result caps, and adds operational context (cost tier, latency). This is complete for an agent to invoke the tool confidently without further clarification.

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 input schema only provides constraints (minLength, maxLength, example) with no description. The tool description adds crucial semantic meaning: 'q' is matched against domain names and entity names/slugs, and the character limits are repeated for prominence. This fully compensates for the 0% schema description coverage.

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 ('Searches') and clearly identifies the resource ('both the domains table and the entities table simultaneously'), with explicit mention of what fields are matched. It distinguishes itself from siblings by naming alternatives like get_domain and get_entity, making its unique scope clear.

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 includes dedicated 'Use this tool when' and 'Do NOT use this tool when' sections, listing concrete scenarios (partial name, ambiguous domain) and explicit alternative tools to use instead (get_domain, get_entity, list_domains, list_entities), providing excellent guidance on selection.

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