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Synapse Layer — Trust Infrastructure for AI Agents

by io.github.SynapseLayer

process_text

Extract candidate memories from free-form text with governance filters and sanitization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesFree-form text to scan for auto-save triggers.
sourceNoSource identifier (default: mcp).
projectNoForce a specific project (e.g., SYNAPSE_LAYER, OFFLY). Auto-detected if omitted.
agent_idNoAgent identifier. Defaults to "default".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
extractedYes
storedCountYes

TDQS

A3.6/5.0
Behavior3/5

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

The description adds behavioral context with 'governance filters and sanitization', but it does not disclose that the tool may auto-save memories based on triggers, as hinted by the input schema. Annotations only indicate readOnlyHint=false, so the side-effect profile remains vague.

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?

Single, compact sentence of 11 words that front-loads the verb and core action. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite good schema and annotations, the description omits critical behavioral context: whether the tool only extracts candidate memories or also triggers saves. Given the readOnlyHint=false and schema mention of 'auto-save triggers', this ambiguity is a significant gap for an agent deciding how to use the tool.

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 coverage is 100%, so the baseline is 3. The description does not add useful parameter-specific details beyond what the schema already provides, though 'governance filters' hints at project and source handling.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verb 'extract' with resource 'candidate memories from free-form text', clearly distinguishing it from sibling tools like save_memory, recall, and store_memory. It communicates a distinct processing role.

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 usage for extracting memories from free-form text but does not explicitly state when to choose this over alternatives or provide exclusions. It is clear but lacks direct guidance on tool selection.

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