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Python
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dialectic_synthesis

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Brief agents with a terse synthesis of accumulated user beliefs by domain, separating validated facts from patterns and hypotheses. Filter by domain or date, or include history for context.

Instructions

Terse rendering of accumulated beliefs about the user, grouped by domain. Used as brief() input. Excludes low/disputed claims. By default only active claims are rendered; as_of switches to valid-time filtering, and include_history=True also includes superseded claims with their validity interval. Returns at most 12 lines.

Tier markers in the output: ★ validated — load-bearing; act on it without asking · observed — pattern with backing; reference, mention if used ? hypothesis — currently testing (only shown if no observed/validated in same domain, to avoid surfacing weak guesses next to load-bearing facts)

If domain is provided, restricts to that domain (no group headers in that case).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNo
domainNo
include_historyNo

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_history
      Added value: +{
+  "default": false,
+  "title": "Include History",
+  "type": "boolean"
+}
  2. First observedv0.13.2

TDQS

A4.8/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the readOnlyHint annotation: it excludes low/disputed claims, defaults to only active claims, returns at most 12 lines, and explains the tier markers (validated, observed, hypothesis) including the rule that hypotheses are only shown when no stronger claims exist. This gives the agent an accurate model of what the output will contain and how to interpret it.

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 tightly written and front-loaded with the core purpose. The tier-marker table and the final domain note each add necessary operational detail without fluff, so every sentence earns its place.

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 that an output schema exists, the description does not need to detail return fields, and it still covers output limits, claim-quality filtering, history behavior, domain filtering, and tier semantics. The parameter set is small and fully explained; no critical calling information is missing.

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%, but the description fully compensates by explaining each parameter: as_of switches to valid-time filtering, include_history adds superseded claims with validity intervals, and domain restricts output and removes group headers. This adds real meaning beyond the bare schema types and defaults.

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's function: a terse rendering of accumulated beliefs about the user, grouped by domain. It also positions the tool within the larger system by noting it is 'used as brief() input,' which helps distinguish it from sibling tools like brief or context.

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 clear context for when to use the tool: it is a compact belief summary feeding brief() input, with explicit controls for time filtering, history inclusion, and domain restriction. It does not name alternative tools to use instead, but the intended integration and parameter-specific behaviors are clear enough for an agent to select it appropriately.

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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