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get_research

Load user research from local files to ground design decisions in actual user data. Returns observations, findings, personas, and quantitative metrics for design validation.

Instructions

Load and return the project's user research V2 store — observations, findings, personas, themes, quantitative metrics, and quality metadata.

Prerequisites: None — reads from the local .memoire/research/ directory. Research data is populated by running memi research from-file, memi research from-stickies, memi research from-transcript, memi research web, or memi research synthesize. Returns an empty V2 store if no research has been imported yet.

Returns on success: Research store object with shape { version, sources, observations, findings, themes, personas, quantitativeMetrics, opportunities, risks, contradictions, quality, summary, methods }. Findings include auditable evidence links via evidenceObservationIds and evidenceSourceIds. Themes reference findingIds[].

Error behavior: Never throws — loads gracefully and returns an empty store if files are missing.

Use this tool: before running compose with a research-driven intent (e.g. "generate a dashboard based on user research"), to inspect what research context is available, or to verify that a research import or synthesis succeeded. Combine with compose to ground design decisions in actual user data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Describes read behavior, graceful error handling, and empty store return. No annotations provided, so description carries full burden and meets it well, though could mention non-destructive nature explicitly.

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?

Structured with clear sections for returns, prerequisites, usage instructions. Slightly verbose but each sentence adds value.

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

Completeness5/5

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

Given no parameters or output schema, the description covers purpose, usage, behavioral traits, and return shape comprehensively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist; baseline for 0 params is 4. Description adds no param info but also schema coverage is 100%.

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 loads and returns the user research V2 store, listing its contents (observations, findings, etc.). It distinguishes from sibling research tools that perform different actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases: before compose with research-driven intent, to inspect context, or verify import success. Mentions prerequisites and combination with compose.

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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