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temurkhan13

openclaw-output-vetter-mcp

verify_action_outcome

Compare an agent's claimed outcome against actual before/after state snapshots to detect misreported actions.

Instructions

v1.1+ — Compare an agent's stated outcome against actual before/after state snapshots. Catches the [@chiefofautism, 158↑] failure mode: agent runs rm -rf / git push --force and then says 'I cleaned up the project structure' — bash-vet catches the destructive command, this checks the misreport about what got done. Also catches the Codex-CoT sandbox-escalation pattern: agent acknowledges read-only constraint, then writes anyway (pass read_only: true in the before snapshot). Pure function — caller captures snapshots; server is stateless. Returns ActionOutcomeReport with verdict (CLEAN / PARTIALLY_GROUNDED / FABRICATED / UNVERIFIED) + per-mismatch evidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesThe agent's stated outcome — verbatim. Examples: 'I cleaned up the project structure', 'tests pass', 'committed and pushed', 'created auth_v2.py'.
before_snapshotYesCaller-captured state BEFORE the agent acted. Recognized keys: files (list[str]), git_status, git_tip / git_head / git_log_tip (str SHA), tests_status / test_status, read_only (bool — asserts no-write constraint). Other keys are tracked but not matched against claim.
after_snapshotYesCaller-captured state AFTER the agent acted. Same key conventions as before_snapshot.
expected_changesNoOptional caller-supplied list of expected changes. Recognized formats: 'file:foo.py:added', 'file:bar.py:removed', 'git:committed', 'git:clean', 'tests:pass'. Each missing entry becomes a MISSING_EXPECTED_CHANGE finding.
Behavior5/5

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

With no annotations, the description fully discloses behavior: pure function, stateless, returns structured verdict. It details the verdict categories and limitation of snapshot capture, providing complete transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed and informative, but somewhat verbose. It front-loads the purpose but includes extensive examples and context that could be condensed without losing value.

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 the complexity (4 params, nested objects, no output schema), the description is highly complete. It explains the return type, verdict values, and use cases, leaving no major gaps for an agent to understand usage.

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 coverage is 100% and schema descriptions are present. The description adds valuable context (examples, recognized keys, formats) that goes beyond the schema, aiding parameter understanding.

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 compares agent's stated outcome against before/after snapshots to detect misreporting, with specific failure modes and a clear verdict output. It distinguishes effectively from sibling tools.

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 explains when to use the tool and provides context (pure function, stateless), but does not explicitly state when not to use it or offer direct comparisons to sibling tools, which slightly reduces clarity.

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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