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Metis — Record Research Finding

record_research_finding

Record a research finding or belief about an entity with evidence and confidence level to preserve your reasoning chain across sessions.

Instructions

Record a timestamped research belief or finding about an entity.

Use this whenever you reach a conclusion, update a previous belief, or
encounter evidence that changes your view. The timeline preserves the full
chain of reasoning across sessions.

Args:
    entity: What this claim is about. Use a consistent name across sessions
        (e.g. "RDT sensitivity in low-burden areas", "Disease X elimination study",
        "DHIS2 tracker performance").
    claim: Your current belief or finding in 1-3 sentences.
    evidence: What supports this claim — paper citation, data result, meeting
        discussion. Brief reference is enough.
    confidence: "low", "medium", or "high".
    source_type: "session", "paper", "meeting", "data_analysis", "literature_review".
    source_ref: Specific reference — DOI, file path, meeting date.
    supersedes_id: If this replaces a previous claim, pass that claim's id.
        Set to 0 if this is a new claim with no predecessor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYes
entityYes
evidenceNo
confidenceNomedium
source_refNo
source_typeNosession
supersedes_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description fully covers behavior: records timestamped beliefs, preserves a timeline, and explains the supersedes_id mechanism. It lacks details on permissions or error handling but discloses the core traits.

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?

The description is front-loaded with a summary and usage context, followed by a structured parameter list. It is slightly long but each sentence adds value; no wasted words.

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 7 parameters and no annotations, the description covers all inputs comprehensively, explains the timeline feature, and mentions superseding. It does not discuss output format, but an output schema exists.

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

Parameters5/5

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

The description provides detailed explanations for all 7 parameters, including usage guidance (e.g., consistent entity names, confidence values, source types). This compensates for the 0% schema coverage, adding significant meaning.

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 'Record a timestamped research belief or finding about an entity,' using a specific verb and resource. It distinguishes from siblings like 'record_decision' and 'capture_idea' by focusing on research findings and the timeline of reasoning.

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 clear usage context: 'Use this whenever you reach a conclusion, update a previous belief, or encounter evidence that changes your view.' It does not explicitly contrast with siblings, but the purpose is well-defined.

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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