NEXUS AGI

Evaluate hypothesis

evaluate_hypothesis

Re-score a hypothesis against all of its stored evidence, optionally adding one new piece of evidence first, and save the result.

Scoring: if any stored evidence is 'contradicting', confidence drops by 0.2
per contradicting item from its current value, and the status becomes
rejected below 0.2, otherwise testing. With no contradicting evidence,
confidence moves halfway toward the average evidence score, and the status
becomes supported above 0.8, otherwise testing. The new confidence and
status are written to the hypothesis and its version is incremented.
The call is not idempotent: every call re-applies the scoring to the
current confidence, and passing evidence adds another evidence record.
Returns 404 if the hypothesis does not exist.

Args:
    hypothesis_id: UUID of the hypothesis (see list_hypotheses).
    evidence: Optional new evidence to store before scoring:
        {"evidence_type": "confirming" | "contradicting" (default "confirming"),
         "score": float 0-1 (default 0.5), "details": dict (optional)}.

Input Schema

Name	Required	Description	Default
`evidence`	No
`hypothesis_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 8, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare idempotentHint=false, and the description reinforces this with a concrete explanation of re-application and evidence accumulation. It also discloses the exact scoring algorithm, status transitions, version increment, and 404 behavior—rich context well beyond the annotations.

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 purpose, then scoring rules, then args, which is a logical structure. It is slightly long because the scoring algorithm is spelled out step by step, but that detail earns its place by clarifying non-obvious behavior.

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?

For a mutation tool with no output schema, the description covers what is written and the 404 case, so an agent knows the side effects. It does not state what the call returns (e.g., the updated hypothesis or a status object), which is a minor remaining gap.

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?

With 0% schema description coverage, the description fully compensates: it identifies hypothesis_id as a UUID and documents the nested evidence object with allowed evidence_type values, defaults, score range, and optional details. This is more than the bare schema provides.

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?

States a specific verb (re-score) and resource (hypothesis) with clear scope: re-score against all stored evidence, optionally adding one new item, and save. It also distinguishes itself from the sibling list_hypotheses by pointing there only for ID lookup.

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 makes clear when to call it (to re-score a hypothesis, with or without new evidence) and references list_hypotheses for obtaining the required ID. It stops short of explicitly naming when not to use it or listing alternative scoring paths, but the context is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All NEXUS AGI tools Inspect

Try in Browser

Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?