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Synapse Layer — Trust Infrastructure for AI Agents

by io.github.SynapseLayer

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Search persisted memory across agent scopes using full-text matching. Governance: requires reason (10–200 chars) because results contain plaintext content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results to return (1–50, default: 20).
queryYesSearch query — natural language or keywords.
scopeNoExplicit search scope. "agent" (default, fail-closed) = limits search to the given agent_id (or the token agent). "tenant" = cross-agent search within the authenticated tenant. If omitted with agent_id present, "agent" is inferred; if both omitted, the token agent is used (verified-token scope).
reasonYesREQUIRED (10–200 chars). Audit policy: MEMORY_EXPORT_GOVERNANCE_V1. Human-readable justification for retrieving PLAINTEXT memory. Without this parameter the call WILL fail with code -32602. Example: "Checking prior migration decisions for the user."
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 (canonical or alias). Used only when scope="agent" or scope is inferred.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
scopeYes
resultsYes

TDQS

A3.8/5.0
Behavior4/5

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

The description discloses that results contain plaintext content and are governed by a reason requirement, adding meaningful behavior beyond the readOnlyHint annotation. It also signals that search operates across agent scopes, which is relevant behavioral context.

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?

Two tight sentences with no filler: the first states the core action and method, and the second front-loads the critical governance constraint. Every element earns its place.

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?

Given the rich schema, output schema, and readOnlyHint annotation, the description covers what an agent needs to call the tool safely. The only meaningful gap is guidance on when to use search versus sibling memory tools, but the rest of the context is well supplied.

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 parameters thoroughly. The description adds no parameter-level detail beyond restating the reason requirement, so it meets the baseline but does not go further.

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-resource pair ('Search persisted memory') and adds method detail ('full-text matching') and scope ('across agent scopes'). It is distinguishable in function, though it does not explicitly differentiate itself from siblings like recall or list_memories.

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 gives clear governance context for when the reason parameter is required, which is useful operational guidance. However, it does not state when to choose this tool over sibling recollection tools, leaving usage selection implicit rather than explicit.

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