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memoryguard_rule_feedback

Record rule outcome feedback to close the loop on bootstrap decisions, enabling follow-up corrections or validations for each receipt.

Instructions

Record explicit evidence for a mandatory-rule bootstrap match. This closes the loop for follow/violate/not_applicable/corrected decisions. One feedback is bound to one receipt_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actorNodeprecated display actor id; source/authority are fixed by MCP transport and never inferred from this value
outcomeYesobserved outcome after bootstrap packet is shown
evidenceNooptional evidence/notes
workspaceNoworkspace path (default: .)
confidenceNoconfidence score 0-1
receipt_idYesreceipt_id returned by memoryguard_context_bootstrap
idempotency_keyNooptional retry key bound to content and actor
agent_instance_idNotrusted identity check

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.7.8

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses a write operation, a one-to-one receipt binding, and a loop-closing purpose. It does not describe side effects, whether feedback can be replaced or duplicated, or any auth/audit implications, leaving some behavioral gaps.

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?

Three short sentences, each with distinct information: the action, the purpose, and the binding constraint. Front-loaded with the primary verb and resource, with no filler or restatement of schema details.

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?

The tool has 8 parameters and no output schema, but the schema fully documents parameters. The description provides essential operational context: when to call (after a bootstrap match) and the one-to-one receipt binding. It could mention the full outcome enum and confidence/idempotency semantics, but those are optional and schema-described, so the gap is minor.

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%, so the baseline is 3. The description adds value by asserting the one-feedback-per-receipt_id constraint, which goes beyond the schema's note about receipt_id. It also previews outcome categories, even though the list is incomplete.

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?

The description identifies a specific action ('Record explicit evidence') and a specific resource ('mandatory-rule bootstrap match'), and clarifies that it closes the loop for outcome decisions. It distinguishes from sibling read/undo tools, though 'bootstrap match' relies on domain jargon and the listed outcome set omits exception/ignored.

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 places the tool after a bootstrap match has been evaluated, for recording the observed outcome. It does not explicitly name alternatives or exclusions, but the context is sufficient to direct an agent away from rule_decision_read or rule_undo.

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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