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danielsimonjr

Enhanced Knowledge Graph Memory Server

reconstruct_memory

Navigate the reconstructive memory graph to answer queries by accumulating evidence through multi-step reasoning, stopping when a target is reached or budget runs out.

Instructions

Answer a query via active multi-step traversal of the reconstructive (Cue–Tag–Content) memory graph. Returns accumulated evidence, the step-by-step trajectory, and whether the loop stopped early on a satisfied condition vs. budget.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesQuery to reconstruct an answer for
maxStepsNoMax reasoning turns (default 8)
perStepBudgetNoMax content nodes routed per step (default 10)
evidenceTargetNoStop once this many distinct evidence items accumulate (default 12)
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool performs an 'active multi-step traversal', returns 'accumulated evidence', 'step-by-step trajectory', and indicates whether the loop stopped 'early on a satisfied condition vs. budget'. This provides reasonable insight into the tool's internal process and termination behavior, though it does not mention potential side effects (e.g., if it modifies memory).

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 that efficiently conveys the tool's action, resource, and return values. No redundant or filler content.

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?

The tool lacks an output schema, but the description explains what is returned: accumulated evidence, step-by-step trajectory, and stop condition. Given the complexity of multi-step traversal, this is fairly complete. However, it does not mention any prerequisites, rate limits, or whether the tool is read-only, which would enhance completeness.

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% with all parameters described. The description mentions 'budget' and 'stop condition', which loosely match parameters like 'maxSteps' and 'evidenceTarget', but adds no semantic details beyond what the schema already provides. Baseline 3 is appropriate given complete schema coverage.

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 clearly states the tool's purpose: 'Answer a query via active multi-step traversal of the reconstructive (Cue–Tag–Content) memory graph.' It specifies the resource (reconstructive memory graph), the action (answer via traversal), and the return values (evidence, trajectory, stop condition). This distinguishes it from sibling tools like 'search_nodes' or 'reconstructive_memory_stats' which serve different functions.

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 complex queries requiring multi-step traversal, but does not explicitly state when to use this tool versus alternatives like 'search_nodes' or 'query_natural_language'. No exclusions or prerequisites are given, relying on the agent to infer from the tool's name and context.

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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