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add_insight_feedback

Destructive

Record disagreement with a run insight. Feedback attaches to the agent that owns the run (not the run itself), so it conditions all FUTURE insight generations for the same agent — not just this one. By default, this also triggers an immediate forced regeneration of the current run so the user sees the corrected insight right away. Other runs pick up the correction the next time their insights are regenerated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentIdNoThe agent/prompt ID to attach feedback to when correcting a secondary-agent insight. Defaults to the run's entry agent.
contentYesThe user's free-text disagreement / correction (required)
customerIdNo(Converra superadmin only) Target customer ID to act on behalf of
findingKeyNoThe stable content-derived key of the finding being corrected (tool:<name> / artifact:<hash> / rule:<quote>), when known. Anchors the verdict to the exact finding so it trains against the right target.
verdictTypeNoThe evidence-bound verdict: 'confirmed_issue' (real issue), 'false_positive' (wrong finding), 'by_design' (correct but intended behavior), or 'wont_fix' (real issue acknowledged but deferred). Defaults to by_design.
rejectedClaimNoThe specific insight bullet or phrase the user disagreed with (optional but recommended)
conversationIdYesThe run whose insight is being corrected (required)
expectedPublicationNoRequired when resolving a human-review request. Bind the verdict to the exact immutable publication returned by the insight read.
triggerRegenerationNoWhether to immediately regenerate insights for this run with the new feedback applied. Defaults to true.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=true), it discloses genuinely non-obvious behavior: propagation to all FUTURE generations for the agent, default immediate forced regeneration of the current run, and delayed pickup by other runs. This is exactly the kind of richer context annotations cannot carry.

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 core action is front-loaded in the first sentence, followed by scope and default side effect. Four sentences with little waste, though the final sentence slightly repeats the propagation point made earlier.

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?

For a mutation tool with nested params and no output schema, the description adequately covers purpose, attachment scope, and side effects. It does not describe the response shape or permission requirements (e.g. the customerId superadmin path), leaving a minor gap.

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%, so the schema already documents all 9 parameters, giving a baseline of 3. The description adds only the default regeneration behavior, which is already stated in the triggerRegeneration schema text, so it does not meaningfully extend parameter meaning.

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 opens with a specific verb+resource ('Record disagreement with a run insight') and immediately clarifies the non-obvious scope: feedback attaches to the owning agent, not the run. This distinguishes it from sibling tools like add_optimization_feedback and regenerate_run_insights by explaining the target of the correction.

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?

It clearly frames the usage context (disagreeing with an insight) and explains the default side effect, but it never explicitly names an alternative tool or states when-not to use it — e.g. vs regenerate_run_insights or refresh_agent_insights. Context is clear without explicit alternatives/exclusions.

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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