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starskrime

MCP Action Firewall

Simulate Research Query

simulate-research-query

Simulates multi-stage research operations to gather, analyze, and synthesize information on specified topics, with optional clarification requests for ambiguous queries.

Instructions

Simulates a deep research operation that gathers, analyzes, and synthesizes information. Demonstrates MCP task-based operations with progress through multiple stages. If 'ambiguous' is true and client supports elicitation, sends an elicitation request for clarification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesThe research topic to investigate
ambiguousNoSimulate an ambiguous query that requires clarification (triggers input_required status)
Behavior3/5

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

With no annotations provided, the description must carry full disclosure burden. It successfully explains the multi-stage progress behavior and the elicitation mechanism when 'ambiguous' is true. However, it omits critical behavioral details: what data format is returned (no output schema exists), whether operations are idempotent, or what 'simulated' means in terms of data sources.

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?

Three sentences efficiently structured: purpose first, demonstration context second, conditional behavior third. Every sentence earns its place, though the middle sentence referencing 'MCP task-based operations' is slightly implementation-specific.

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

Completeness3/5

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

Given no output schema and no annotations, the description adequately covers the simulation process and elicitation behavior but fails to describe what the tool returns (critical since no output schema exists). Missing return value specification and side-effect details prevent a higher score.

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%, establishing a baseline of 3. The description adds significant value by explaining that the 'ambiguous' parameter triggers an 'elicitation request for clarification' beyond the schema's basic description, providing crucial behavioral context for the interactive capability.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Simulates a deep research operation' with specific actions (gathers, analyzes, synthesizes), distinguishing it from siblings like 'echo' or 'get-sum'. However, the 'Simulates' qualifier and mention of 'Demonstrates MCP task-based operations' suggest this is a demo tool, which could more explicitly clarify whether this is for testing vs. production use.

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

Usage Guidelines2/5

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

Provides no explicit guidance on when to select this tool versus alternatives like 'get-resource-reference' or 'get-structured-content' for actual information retrieval. While 'Demonstrates MCP task-based operations' implies educational use, there are no clear 'when to use' or 'when not to use' statements.

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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