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dialectic_review

Read-only

Filter claims by confidence level and domain to review active claims, and add an as-of date to see historical validity.

Instructions

List active claims filtered by confidence floor and optional domain. Retired/superseded claims are omitted by default.

If as_of is provided (unix seconds or ISO-8601), returns claims whose valid-time interval covered that instant, including currently superseded claims that were valid then. include_validity=True appends state + valid_from/valid_to without changing the default output.

min_confidence: one of 'low','medium','high','disputed'. Note that 'disputed' is treated as its own bucket (not ordered against the others) — passing min_confidence='disputed' returns only disputed.

Format: ' [conf] tier= domain= support=N contradict=N '.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNo
as_ofNo
domainNo
min_confidenceNolow
include_validityNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.17.0
    • addedInput schema / properties / as_of
      Added value: +{
+  "default": "",
+  "title": "As Of",
+  "type": "string"
+}
    • addedInput schema / properties / include_validity
      Added value: +{
+  "default": false,
+  "title": "Include Validity",
+  "type": "boolean"
+}
  2. First observedv0.13.2

TDQS

A4.4/5.0
Behavior5/5

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

The readOnlyHint annotation already establishes safety, but the description adds substantial behavior beyond it: retired/superseded claims are omitted by default, as_of returns historically valid superseded claims, 'disputed' is a separate unordered bucket, and include_validity appends fields without altering default output. This is rich, non-obvious behavioral disclosure.

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 organized into short, purposeful sections: core summary, temporal/validity options, confidence semantics, and output format. Every sentence adds useful information, with no repetition of schema defaults or annotation values.

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?

The description covers the non-obvious temporal filtering, confidence bucket behavior, and output format, while an output schema exists to document return values. The main gap is k/result-count semantics and any pagination or ordering behavior, which are relevant for a listing tool.

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?

With 0% schema description coverage, the description carries the full burden. It thoroughly explains min_confidence values and the special 'disputed' handling, as_of accepted formats and semantics, and include_validity's effect. Domain is only called 'optional', and k is never explained, leaving its meaning (likely result limit) to inference.

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 first sentence names a specific verb ('List') and resource ('active claims') with clear filter dimensions (confidence floor, optional domain). This distinguishes it from mutation/creation sibling tools like dialectic_claim, dialectic_supersede, and review_candidates.

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

Usage Guidelines3/5

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

The description gives detailed conditional behavior for as_of, include_validity, and min_confidence, so an agent knows when those options are relevant. However, it never explicitly names sibling alternatives or states when to choose this tool over the many related dialectic_* and review_* tools, leaving routing mostly 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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