contradiction-mcp

contradiction.claims.list

Read-onlyIdempotent

Query stored factual assertions and claims from connected sources. Filter by subject, predicate, source, environment, or value type to explore known facts and inspect extracted data.

Instructions

Queries stored factual assertions and claims extracted from connected sources with flexible filtering by subject, predicate, source, environment, and value type. Read-only operation. Use this tool to explore known facts, find subjects with multiple assertions, or inspect extracted data.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum number of claims to return (default: 50)
`offset`	No	Pagination offset (default: 0)
`subject`	No	Filter by claim subject entity name (e.g. "UserAuthService", "API Gateway")
`sourceId`	No	Filter by originating source identifier
`predicate`	No	Filter by property/predicate name (e.g. "node_version", "min_ram", "http_port")
`valueType`	No	Filter by value type (e.g. "quantity", "version", "status", "date", "price", "configuration")
`environment`	No	Filter by environment context (e.g. "production", "development")

Output Schema

TableJSON Schema

Name	Required	Description
`count`	No	Number of claims returned in current page
`total`	No	Total count of claims matching filters
`claims`	No	List of retrieved factual claims

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.3.3Sep 18, 2026

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint false), and the description's 'Read-only operation' merely restates that. It does add that the data comes from connected sources and that filtering is flexible, but otherwise leaves pagination/behavioral details to the schema.

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?

Two compact sentences front-load the core purpose, state the safety property, and give example use cases without filler. Every sentence earns its place.

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?

Given the output schema and strong annotations, the description gives enough context on purpose and typical use. It could mention when to choose list over claims.get, but this is a minor gap given the schema captures the rest.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and every parameter has a description, so the baseline is 3. The description adds a summary of filterable dimensions but no new parameter-level semantics beyond the schema.

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 clear verb and resource ('Queries stored factual assertions and claims...') and names the filtering dimensions, making the listing purpose obvious. It does not explicitly contrast with siblings like claims.get or claims.analyze, so it stops short of full differentiation.

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?

Provides clear usage context ('explore known facts, find subjects with multiple assertions, or inspect extracted data'), which signals typical scenarios. It doesn't name alternatives or exclusions, so an agent must infer when to prefer the sibling tools.

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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