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Synapse Layer — Trust Infrastructure for AI Agents

by io.github.SynapseLayer

memory_feedback

Submit feedback to adjust trust scoring for stored memories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalYesFeedback signal: used (auto), helpful (positive), irrelevant (negative), ignored.
memoryIdNoDeprecated camelCase alias of memory_id — still accepted for backward compatibility.
memory_idNoID of the memory to provide feedback on (canonical snake_case name).
sessionIdNoDeprecated camelCase alias of session_id — still accepted for backward compatibility.
session_idNoOptional session identifier for tracking (canonical snake_case name).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalNo
successYes
memoryIdYes

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already indicate this is a mutating operation (readOnlyHint=false). The description adds that the mutation affects trust scoring rather than memory content, which is useful context beyond the schema. It discloses the core side effect honestly and does not contradict the annotations.

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?

The description is a single, front-loaded sentence with no filler. It earns its place, stating the action and its effect. There is no structural improvement needed.

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 schema is rich with aliases and enums, but the description leaves gaps: it does not explain why memory_id is optional despite referring to 'stored memories,' nor the distinction between session_id and memory_id contexts. The presence of an output schema and full parameter docs reduces the severity, but the ambiguous optionality is a meaningful 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?

All five parameters are fully documented in the schema, so the description carries no additional parameter information. The baseline for 100% schema coverage is 3. No enhancement or clarification of parameter meaning is attempted in the description.

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 states a clear verb ('Submit') and resource ('stored memories') with a concrete outcome ('adjust trust scoring'). It is not a tautology and is distinct from sibling memory operations. However, it does not explicitly name or contrast with sibling tools, so it earns a 4 rather than a 5.

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

Usage Guidelines3/5

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

The description implies this tool is for providing feedback after memory use or storage, but it does not state when to choose it over alternatives like save_memory or recall. No exclusions or prerequisites are given. The usage context must be inferred from the tool name and sibling set.

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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TDQS

B3.1/5.0
Disambiguation2/5

Several tools are redundant: recall_memory is explicitly an alias of recall, and save_memory/save_to_synapse/store_memory all persist memories. This makes the tool boundary unclear for agents choosing between them, even though core operations like health_check and slo_report are distinct.

Naming Consistency2/5

Names are consistently snake_case, but verb usage is inconsistent: save_memory, save_to_synapse, and store_memory use different verbs for the same operation, and recall/recall_memory add an alias rather than following one convention. The pattern is readable but not predictable.

Tool Count3/5

Thirteen tools is not inherently excessive for an agent-memory/trust service, but the presence of multiple aliases inflates the surface and means not every tool earns its place. The effective set is smaller, making the count feel padded.

Completeness3/5

The surface covers initialization, save, list, search/recall, feedback, and handover, but there is no update or delete operation for memories. This leaves lifecycle management incomplete and may force agents to work around missing state-management operations.

Resources