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Finance MCP

Finance MCP

by FlowLLM-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

extract_entities_code

Extract financial entities and their codes from natural language queries to identify stocks, bonds, funds, cryptocurrencies, and other investment instruments for financial analysis.

Instructions

Extract financial entities from the query, including types such as "stock", "bond", "fund", "cryptocurrency", "index", "commodity", "etf", etc. For entities like stocks or ETF funds, search for their corresponding codes. Finally, return the financial entities appearing in the query, including their types and codes.

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": {
      "query": "<query>"
    },
    "name": "extract_entities_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesNatural language query about financial entities.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
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 describes the extraction and code search behavior but lacks details on permissions, rate limits, error handling, or output format (e.g., structure of returned entities). For a tool with no annotations, this is a significant gap in behavioral disclosure.

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 concise and front-loaded, stating the core purpose in the first sentence. Both sentences earn their place by adding specific entity types and code search details. It could be slightly more structured but avoids redundancy and is efficiently sized.

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

Completeness2/5

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

Given no annotations and no output schema, the description is incomplete. It explains what the tool does but omits critical behavioral aspects like output structure, error conditions, or limitations. For a tool with 1 parameter and no structured support, more context is needed to be fully helpful to an AI agent.

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

Parameters3/5

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

The schema description coverage is 100%, with the single parameter 'query' documented as a natural language query about financial entities. The description adds context by specifying the types of financial entities (e.g., stock, bond) and the code search for stocks/ETFs, but doesn't provide additional syntax or format details beyond what the schema implies. Baseline 3 is appropriate given the high schema coverage.

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's purpose: extracting financial entities from a query, identifying their types, and searching for codes for stocks/ETFs. It specifies the resource (financial entities) and verb (extract, search, return). However, it doesn't explicitly differentiate from sibling tools like dashscope_search or tavily_search, which might also process queries.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions (e.g., when not to use it), or compare it to sibling tools like dashscope_search or tavily_search, which might handle similar queries. Usage is implied only by the purpose statement.

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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