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Python
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dialectic_claim

Register a claim about the user, optionally with supporting or contradicting evidence and a source, to track context across sessions.

Instructions

Register a new claim about the user. Optionally seed with first piece of evidence — pass the supporting (or contradicting) quote in evidence, set evidence_kind to 'support' (default) or 'contradict', and supply its immutable source reference (for example dialog:<uuid>). Empty/manual means direct input from this session.

domain is free-text; recommended values: 'style','workflow','values','context','skills','other'.

Returns: 'ok id= conf= tier='.

Input Schema

NameRequiredDescriptionDefault
claimYes
domainNo
sourceNo
evidenceNo
memory_scopeNoproject
evidence_kindNosupport

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.19.1
    • addedInput schema / properties / memory_scope
      Added value: +{
+  "default": "project",
+  "title": "Memory Scope",
+  "type": "string"
+}
  2. Changed1 schema field changedv0.19.0
    • addedInput schema / properties / source
      Added value: +{
+  "default": "",
+  "title": "Source",
+  "type": "string"
+}
  3. First observedv0.13.2

TDQS

A3.8/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false, idempotentHint=false, destructiveHint=false, so mutation context is covered. Description adds useful behavioral detail: the seeding workflow, the default for evidence_kind, and the return string format ('ok id=<UCxxx> conf=<level> tier=<tier>'). Doesn't cover auth or rate limits, but the extra context is meaningful.

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?

Front-loads the core action, then details the optional evidence path and return format. Efficient, though the return-string sentence is a bit cryptic and could be slightly trimmed. No significant padding.

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?

Output schema exists, so return values needn't be explained, though the description still summarizes the return string. Covers mutation semantics, evidence seeding, and domain guidance. Missing only explicit when-to-use routing among the many dialectic siblings.

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?

Schema description coverage is 0%, so the description must compensate. It does well: it clarifies `evidence`, `evidence_kind` ('support' default vs 'contradict'), `source` (with dialog:<uuid> example and 'empty/manual' semantics), and `domain` with recommended values. `memory_scope` and `claim` are not elaborated, keeping it from a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: 'Register a new claim about the user'. Clear enough to differentiate from siblings like dialectic_evidence or dialectic_observation_resolve. Doesn't explicitly name an alternative but purpose is unambiguous.

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?

Implies usage by describing the optional evidence seeding workflow, but gives no explicit when-to-use vs when-not guidance. No mention of alternatives like dialectic_evidence, dialectic_review, or note. Context is inferable but not spelled out.

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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