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Synapse Layer — Trust Infrastructure for AI Agents

by io.github.SynapseLayer

store_memory

Idempotent

Store structured memory with metadata and trust scoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTags for categorization.
contentYesThe memory content to store securely.
subjectNoESCOPO1 (optional): opaque 8-128 char token ([A-Za-z0-9._:-]) that isolates memory to a single person within a shared connect-token. Omit for token-scoped behavior. Never include PII/email.
agent_idNoAgent identifier for memory isolation. Defaults to "default".
force_intentNoMANUAL OVERRIDE ONLY — use apenas quando você (humano) quer forçar explicitamente a classificação de intenção. Valores reconhecidos: preference | fact | procedural | bio | critical | operational | strategic | compliance | security | system_directive | clinical | general | ephemeral. Se não tiver certeza, NÃO preencha este campo — o sistema classifica automaticamente. Valores não reconhecidos são ignorados silenciosamente e retornados em warnings.
intent_reasonNoAuditable reason for forcing intent classification. Max 500 characters. Ignored if force_intent is absent.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
scopeYes
storedYes

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already indicate readOnlyHint=false and idempotentHint=true, so the description does not need to restate safety. It adds the context of 'metadata and trust scoring' as behavioral features, but does not disclose how trust scoring works, whether existing memories are overwritten, or what side effects occur beyond storing.

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 one clean sentence with no filler, and the core action is front-loaded. It is efficient, though it is so terse that some important context is left to the schema and annotations.

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 rich input schema and output schema carry much of the burden, but the description alone does not explain trust scoring semantics or how this tool differs from the similarly named 'save_memory'. Given the number of memory-related siblings, the description is minimally adequate but not complete for confident tool selection.

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 six parameters thoroughly. The description's phrase 'metadata' loosely maps to tags, subject, and agent_id, but it adds no concrete parameter semantics beyond what the schema provides. Baseline 3 is appropriate.

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 names a specific verb ('Store') and a specific resource ('structured memory') with a distinguishing feature ('metadata and trust scoring'). However, it does not differentiate from the sibling 'save_memory', which appears to be a near-synonym, so the agent cannot reliably pick between them from the description alone.

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?

There is no guidance about when to use this tool versus alternatives such as 'save_memory', 'recall_memory', or 'list_memories'. The one-sentence description gives no use-case context, preconditions, or exclusions, leaving tool selection to inference.

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