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TunnelMind Data API

get_demand_aggregate

Weekly public aggregate of demand: what callers asked this API for that it did not have or rejected (ghost routes, rejected arguments, ghost MCP tools, tools/list counts). One ISO week, Monday 00:00 UTC to the next Monday 00:00 UTC (?week= picks the week containing a date; default is the current week). Aggregate rows only: a row is published only when at least n_threshold (3) distinct actors produced it in the window. Rows below that are omitted, and their count is published in suppressed ({rows, events}). No actor identifier, IP, raw path, query string, timestamp or per-row user agent is ever returned. name and path_shape are caller-supplied request text, marked caller_supplied: true; they are not TunnelMind statements. valid_until equals window_end. Add ?receipt=true for a signed Receipt v1.0 over the same payload. Totals cover published rows only. ghost_tool_per_list_ratio is ghost tool asks per tools/list, because only unknown-tool calls are recorded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
weekNoAny date (YYYY-MM-DD) inside the wanted ISO week. Defaults to the current week.
receiptNoWhen true, attach a signed Receipt v1.0 committed to the transparency log. Additive — a signing failure never costs you the observation (ADR-014).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: n_threshold suppression of rows below 3 distinct actors, the suppressed {rows, events} count, the explicit privacy guarantees (no actor id, IP, raw path, query, timestamp, or per-row UA), caller_supplied marking of name/path_shape, valid_until = window_end, and the signed Receipt v1.0 opt-in. This is exactly the behavioral context an agent needs.

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?

Front-loaded with the core purpose, and the dense clauses mostly earn their place by conveying privacy and suppression semantics. It reads as one long run-on paragraph, however, which slightly hurts scannability for such a detail-heavy description.

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

Completeness4/5

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

With no annotations and no output schema, the description must stand alone, and it largely does: it explains row publication rules, the suppressed count, caller_supplied fields, valid_until, and the optional receipt. It stops short of fully enumerating the returned payload, but the key semantics are present.

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?

Schema coverage is 100%, so both parameters are already documented in the schema, making 3 the baseline. The description reinforces the week semantics (any date inside the ISO week, default current week) and the receipt flag, but adds no syntax or format detail the schema lacks.

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

Purpose4/5

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

States a specific verb and resource: a weekly public aggregate of demand covering what callers asked for that was missing or rejected (ghost routes, rejected args, ghost MCP tools, tools/list counts). The scope is precisely defined, but it does not explicitly position itself against siblings like get_stats or tollbooth_stats, so an agent must infer the distinction.

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?

The description explains the mechanics of the aggregate thoroughly but never says when to reach for this tool versus adjacent stats/receipt tools. There are no exclusions or named alternatives, leaving usage context entirely to inference.

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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