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get_rejection_funnel

Why no signal? The pipeline funnel in one glance (public).

Every cycle that does not become a signal died at a specific gate. This returns the cycle count at each gate in order, so your agent can draw a survival funnel and see where reads are being rejected — the direct answer to "BRS rejects almost everything, prove it."

Args: day: A specific UTC day (YYYY-MM-DD). Empty = today. days: Sum over the last N UTC days (e.g. 30). Ignored if day set. since: "launch" for every day on record, or a YYYY-MM-DD start date.

Returns: cycles_total, emitted, signals_sent, per-gate counts, gate_order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayNo
daysNo
sinceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden and does substantial work: it flags 'public' access, documents the parameter precedence rule ('days... Ignored if day set'), and enumerates return contents. It does not explicitly declare read-only/no-side-effect behavior or mention rate limits/freshness, though 'Returns...' strongly implies a pure read.

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?

At roughly 110 words with a clear Purpose → Args → Returns structure, the description is efficient and front-loaded with its core purpose. The 'Why no signal?' hook and the 'prove it' quote are slightly redundant with the following sentence, keeping it just shy of maximally lean.

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?

Because an output schema exists, the description need not fully explain the return shape, yet it still lists the key fields (cycles_total, emitted, signals_sent, gate_order). All three optional parameters are fully documented and access context ('public') is stated, so an agent can select and invoke the tool correctly; only timing/freshness behavior is left unspecified.

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?

Schema description coverage is 0%, yet the description documents all three parameters with formats (YYYY-MM-DD), default behavior ('Empty = today'), the interaction rule ('Ignored if day set'), and the special 'launch' keyword for since. This fully compensates for the bare schema and exceeds what any structured field provides.

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 specifies a precise verb+resource pair: it 'returns the cycle count at each gate in order' so the agent can 'draw a survival funnel and see where reads are being rejected.' The funnel/rejection framing clearly distinguishes it from all 15 sibling analytics tools, none of which target pipeline drop-off.

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

Usage Guidelines4/5

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

The description gives a concrete trigger scenario: it is 'the direct answer to "BRS rejects almost everything, prove it"' and frames the tool as the explanation for 'Why no signal?' This is clear context for when to call it. However, it names no sibling alternatives and gives no explicit when-not-to-use guidance, so it stops short of a 5.

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

A4/5.0
Disambiguation4/5

Tools like get_convergence, get_directional_bias, and get_dashboard are related but clearly scoped: convergence checks sensor agreement, directional_bias gives the trade call, dashboard bundles everything. Mempool fees vs stats are distinct (rates vs pending tx). Some overlap exists between convergence/regime_current, but descriptions disambiguate well.

Naming Consistency5/5

All tools follow a consistent get_verb_noun pattern (get_block_tip, get_funding_divergence, get_system_health). The only exception is query_db, which uses 'query' instead of 'get', but it still follows the verb_noun structure and same snake_case style. No mixed conventions.

Tool Count4/5

15 tools is at the high end of the ideal range, but each serves a distinct function in a complex domain: sensor convergence, regime, funding, gamma, mempool, system health, audit. The Pro/free tier adds some apparent duplication (get_convergence vs get_directional_bias), but they address different questions.

Completeness5/5

The tool set covers the full workflow: convergence check, directional call, regime context, specialized indicators (funding, gamma, stablecoin flows, fee histogram), mempool data, system health, audit trail, and a queryable database. No obvious dead ends; public signal history and counters support verification.

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