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MaverickMCP

MaverickMCP

by wshobson
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

agents_deep_research_financial

Perform deep financial research on any topic using AI agents. Customize research depth, persona, focus areas, and timeframe for comprehensive analysis.

Instructions

Perform deep financial research on a topic with AI agents.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "research_topic": "<research_topic>"
    },
    "name": "agents_deep_research_financial"
  }
}

Input Schema

NameRequiredDescriptionDefault
research_topicYes
personaNomoderate
research_depthNocomprehensive
focus_areasNo
timeframeNo30d
session_idNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions 'AI agents' but fails to disclose behaviors like required permissions, asynchronicity, data handling, or what happens during research.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence (10 words) but is under-specified rather than concise. It lacks critical information and does not earn its place as a standalone description.

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

Completeness1/5

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

With 6 parameters, no schema descriptions, no annotations, and no output schema details, the description is entirely inadequate. It does not explain the research process, outputs, or how to configure the tool effectively.

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

Parameters1/5

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

The input schema has 6 parameters with 0% description coverage. The description adds no explanation for parameters like research_topic, persona, or research_depth, forcing agents to rely solely on names.

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

Purpose3/5

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

The description states 'Perform deep financial research on a topic with AI agents,' providing a verb and resource. However, 'deep financial research' is vague and does not distinguish this tool from numerous sibling tools like research_company_comprehensive or research_comprehensive_research.

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?

No guidance on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context compared to other research tools.

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

Deploy Server

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