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get_research

Load user research data—observations, findings, personas, themes, metrics—to ground design decisions. Use before composing research-driven content.

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. Research stores grow large — request only the sections you need (default is a summary with per-section counts).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionsNoSections to include in full. Omit for a lightweight overview: summary + per-section counts. Pass the sections you actually need to keep the payload small.
Behavior4/5

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

With no annotations, the description discloses error behavior ('Never throws, returns empty store'), prerequisites (reads from local .memoire/research/), and data population methods. It lacks explicit auth needs but the read-only nature is implied.

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 well-structured with separate paragraphs for prerequisites, returns, error behavior, and usage. Every sentence adds value; no redundancy or fluff.

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 output schema, the description thoroughly explains the return shape including specific fields like evidenceObservationIds and findingIds. It covers error behavior, prerequisites, and how data is populated, making it fully informative.

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?

Schema coverage is 100%. The description adds meaning beyond the schema by explaining the default behavior (lightweight overview) and advising to request only needed sections to keep payload small.

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 explicitly states the verb ('Load and return') and the resource ('project's user research V2 store'), listing specific components like observations, findings, personas, etc. It clearly distinguishes from siblings like analyze_design or compose.

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

Usage Guidelines4/5

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

The description provides explicit usage scenarios: before running compose with research-driven intent, to inspect research context, or verify import/synthesis success. It also suggests combining with compose. While it doesn't explicitly state when not to use, the context is clear.

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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