n0brains

Anti-Predictive Cells

get_anti_predictive_cells

Read-only

[RECEIPTS] Cells from cell_stats.json with inverse_flagged=true. These are (signal_type × direction × regime) buckets where the empirical win-rate is below the inverse_thresholds floor with sufficient sample. Signals in these cells get calibration_inverted_in_cell=true and have confidence nulled in customer-facing serialization.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Default
`note`	No	Cells flagged anti-predictive: empirical win-rate below floor with sufficient sample. historical_edge.enrich_signal will set calibration_inverted_in_cell=true on signals in these cells.
`as_of`	No
`cells`	Yes
`cell_stats_path`	No	/app/data/cell_stats.json

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, covering the safety profile. The description adds meaningful context about what makes cells anti-predictive (inverse_flagged=true, win-rate below threshold) and downstream effects (calibration inversion, confidence nulling in serialization), going beyond the structured metadata.

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 concise and well-structured, with three sentences that front-load the key identifier ('Cells from cell_stats.json with inverse_flagged=true') and elaborate logically into definition and consequences. Every sentence earns its place without redundancy.

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?

Given the tool has no parameters, a read-only annotation, and an output schema, the description fully covers the conceptual meaning and implications of the returned data. It is complete for an agent to understand what is being retrieved and how to interpret it.

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 tool has zero parameters, so the baseline per rubric is 4. The description provides significant semantic detail about the returned data, compensating for the lack of parameter documentation.

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 clearly states the tool returns cells from cell_stats.json with inverse_flagged=true, defining them as (signal_type × direction × regime) buckets with below-threshold win rates. This specific verb+resource combination distinguishes it from sibling get_* tools.

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 does not mention any exclusion criteria or point to sibling tools for different use cases, such as get_actionable_signals for actionable signals.

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

Most tools have clearly distinct purposes with detailed descriptions, but there are clusters of similar concepts (e.g., get_liquidity_map vs get_liquidation_map, get_state vs get_state_brief, multiple signal-related tools) that could cause misselection despite thorough documentation.

Naming Consistency5/5

All tools follow a consistent lowercase verb_noun pattern, predominantly get_* nouns, with only a few non-get verbs like list_signals, rank_trades, log_trade, etc., but the style is uniform.

Tool Count2/5

With 52 tools, the surface is extremely heavy for an agent to navigate. While the server's scope is broad, the count far exceeds the typical 3-15 range and falls into the 'too many' category.

Completeness4/5

The tool set covers the full lifecycle for journaling, signals, market analysis, and proof, with no major dead ends. Minor gaps exist, such as no dedicated get_trade_by_id (workaround via get_journal) and no get_market_state tool despite being referenced in get_state.

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