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Veris

by vighriday
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TypeScript
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report_execution

Record verification results in Veris's append-only, hash-chained ledger, ensuring later passes cannot overwrite earlier failures; batch transactions reject invalid entries.

Instructions

Posts verification results back to Veris. Evidence is append-only and hash-chained: a later record for the same target is added alongside the earlier one, never replacing it, so a failure cannot be overwritten by a subsequent pass. Each record carries a trust class — 'agent-asserted' (default; the caller's own claim, counts at half weight), 'harness-observed' (an external runner observed it), or 'veris-derived' — and a producer identity. Accepts a batch, written in one transaction: if any entry is invalid, none are recorded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
executionsYesBatch of results.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv3.0.1
    • addedInput schema / properties / executions / description
      Added value: +"Batch of results."
    • addedInput schema / properties / executions / items / properties / producer
      Added value: +{
+  "description": "Who produced this result, e.g. 'github-actions:test' or an agent name.",
+  "type": "string"
+}
    • addedInput schema / properties / executions / items / properties / tier / description
      Added value: +"Tier label, e.g. 'Tier 1 - Structural Verification'. Must match a planned tier."
    • addedInput schema / properties / executions / items / properties / trustClass
      Added value: +{
+  "enum": [
+    "veris-derived",
+    "harness-observed",
+    "agent-asserted"
+  ],
+  "type": "string"
+}
  2. First observed

TDQS

A3.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so well. It explicitly discloses append-only and hash-chained behavior, atomic batch semantics, trust-class defaults, and the fact that failures cannot be overwritten. This gives an agent accurate expectations about side effects and data integrity.

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?

The description is compact and front-loaded with the primary purpose, followed by important behavioral constraints. It avoids redundant wording, though the dense explanation of trust classes and atomicity could be slightly streamlined without losing meaning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers critical side effects, trust-class semantics, and batch atomicity, but does not explain how nodeId relates to an existing verification plan or when this tool should be invoked relative to other verification workflow steps. It is sufficient for basic use but lacks some surrounding workflow context.

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?

The description clarifies the top-level executions batch, tier labels must match a planned tier, and producer identity. However, key nested properties like nodeId, directive, workflowId, and result values are not explicitly explained beyond the schema's types and enums, leaving some ambiguity for callers.

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 opening sentence, 'Posts verification results back to Veris', uses a specific verb and object, clearly identifying the tool's function. It also distinguishes this from planning/analysis tools by focusing on recording execution results rather than generating plans or detecting drift.

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

Usage Guidelines2/5

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

The description does not explicitly state when to use this tool versus alternatives like generate_verification_plan or identify_unverified_behaviors. It implies use for recording results, but lacks direct guidance or conditions for choosing it over 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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