io.github.Ravicha2/lit-review-council

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conduct_literature_review

Conducts a multi-agent literature review, synthesizing academic and practitioner perspectives into grounded reports, and saves an OKF markdown bundle.

Instructions

Executes a multi-agent literature review on the provided research question and topics.

Args:
    question: The overarching research question driving the literature review.
    topics: A list of topic dictionaries. Each topic must have:
        - slug: (str) a short hyphenated identifier
        - description: (str) explanation of the topic
        - search_keywords: (list[str]) 2-4 highly specific search queries
        - rationale: (str, optional) why this topic was chosen
    output_dir: (str) Directory to save the final OKF markdown bundle. Defaults to current directory.
    
Returns:
    A success message with the path to the generated OKF bundle directory.

Input Schema

TableJSON Schema

Name	Required	Default
`topics`	Yes
`question`	Yes
`output_dir`	No	.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.6Sep 1, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses that execution is multi-agent, writes output to a configurable directory, and returns a success message with the bundle path. This is meaningful behavioral context beyond a simple 'runs a review' statement.

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 front-loaded with a one-sentence purpose summary, then organized into Args and Returns sections. Each sentence provides necessary information with no filler or repetition.

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 description covers all three parameters, required versus optional fields, topic structure, default behavior, and return format. It is missing minor context such as what 'OKF' means or any expectations about runtime/cost, but for invocation purposes it is sufficiently complete.

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?

Schema coverage is 0%, but the description fully compensates by documenting question, output_dir with default, and the required structure of each topics entry including slug, description, search_keywords, and optional rationale. This gives an agent everything needed to construct valid arguments.

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 states a specific action ('Executes a multi-agent literature review') with clear inputs (research question, topics) and output (OKF markdown bundle). Even without siblings, it is unambiguous about what the tool does.

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 opening sentence makes the use case clear, and the Args section details what callers must supply. There are no siblings to differentiate from, so explicit alternatives are not required, though the description stops short of stating when not to use this tool.

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

Install Server

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