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Synapse Layer — Trust Infrastructure for AI Agents

by io.github.SynapseLayer

recall_memory

Read-only

Recall persisted memory by query. Alias of recall.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoRecall routing mode.
limitNoMaximum memories to return (1–50, default: 10).
queryYesWhat to recall — natural language query for memory retrieval.
scopeNoExplicit memory scope. "agent" (default, fail-closed) = limits recall to the given agent_id (or the token agent). "tenant" = cross-agent recall 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. Without this the call WILL fail with code -32602. Example: "Checking user project status."
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
memoriesYes

TDQS

A3.6/5.0
Behavior3/5

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

The readOnlyHint annotation already covers the safe read-only nature. The description adds only the alias relationship, which is minor behavioral context. It does not mention fail-closed scoping or the required audit reason, though those are documented in the parameter schema rather than the description.

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 extremely concise: two short sentences, each adding some value. The core purpose is front-loaded, and the alias note is a useful routing hint without unnecessary elaboration.

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 input schema, annotations, and presence of an output schema, the minimal description is mostly sufficient. The alias identification helps avoid confusion with the recall sibling. It could add a sentence on when to prefer this over search, but that gap is minor given the structured metadata already present.

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%, and parameter descriptions are already detailed, including enums, defaults, scope behavior, and failure conditions. The description itself adds no parameter information, but with complete schema coverage the baseline of 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 states the action clearly: 'Recall persisted memory by query,' identifying the resource (persisted memory) and the operation. The 'Alias of recall' note further clarifies that it is equivalent to the recall sibling. It does not explicitly distinguish itself from search or list_memories, but the core purpose is unambiguous.

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?

'Recall persisted memory by query' implies the tool is for natural-language memory retrieval, which gives the agent a basic sense of when to use it. The alias note signals interchangeability with recall, but there is no explicit guidance on when to choose this over search or list_memories, and no exclusion criteria.

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