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Scan for multi-attester disagreement

emem_memory_contradictions
Read-onlyIdempotent

Reveals disagreements in the corpus where different sources assign conflicting values to the same place, band, and time, providing a severity score and citations for each disputed fact.

Instructions

Surface where the corpus DISAGREES with itself (algebra: competing evidence). When two or more independent sources signed different values for the same place + band + time, this returns that disagreement with a 0–1 severity score and citations to every disputed fact, instead of silently picking one value and hiding the conflict. The opposite of a confident single answer: it tells you when not to trust one.

When to use: Call this when trust matters before you rely on a number — 'is there disagreement about X', 'do the sources corroborate this', 'audit this claim', or 'find contradictory observations in region Y'. Use it to decide whether a fact is well-corroborated or contested. Narrow with cell_prefix (e.g. "defi.zb5") for a region and band for one family; min_severity filters out trivial differences. Severity is per band kind: scalar = spread over the band's range, vector = 1 − mean cosine, categorical = 1 − mode share. The receipt cites every disputed CID — follow up with emem_diff to quantify a pair, or (with the refinement loop on) read the emitted disagrees_with edge via emem_edges_recall.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bandNoBand key filter (e.g. `indices.ndvi`). Omit to include all bands.
limitNoMax contradictions to return.
cell_prefixNoBytewise prefix on cell64 (e.g. `defi.zb5f9`). Omit to scan the whole corpus up to the scan cap.
min_severityNoSeverity floor in [0, 1]. 0 = report every disagreement, 1 = only flagrant. Severity scoring is per band kind: scalar (max-min over band range), vector (1 - mean cosine), categorical (1 - mode share).
window_unix_sNo[lo, hi] inclusive Unix-seconds filter on attestations' signed_at — all disagreeing attestations must fall in the window.
Behavior5/5

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

Adds context beyond annotations: explains severity scoring per band kind, notes that conflicts are surfaced instead of hidden, and details the receipt structure. All align with the readOnlyHint and idempotentHint flags.

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?

Well-structured with front-loaded purpose and clear sections. Slightly verbose but every sentence adds meaningful information; no 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?

Despite no output schema, the description details return format (severity score, citations), usage scenarios, parameter guidance, and follow-up tools. Fully addresses agent needs for correct invocation.

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 coverage is 100%, but the description enriches parameters with usage context (e.g., 'Narrow with cell_prefix for a region') and explains severity computation, adding value beyond the schema descriptions.

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 opens with a clear verb-resource pair: 'Surface where the corpus DISAGREES with itself.' It distinguishes from siblings like emem_recall by emphasizing conflict detection rather than confident recall.

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?

Explicitly states when to use: 'Call this when trust matters before you rely on a number...' and provides examples like 'is there disagreement about X'. It also directs to follow-up tools (emem_diff, emem_edges_recall), giving clear alternatives.

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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