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finstack-mcp

finstack-mcp

by finstacklabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_sector_peer_context

Retrieve sector and peer context for an NSE stock: peer basket composition, peer rank, valuation vs peers, and sector performance trends.

Instructions

Sector and peer context for a stock.

Shows:

  • likely peer basket

  • peer rank

  • valuation vs peers

  • sector performance context

Args: symbol: NSE symbol (e.g. RELIANCE, HDFCBANK, INFY)

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": {
      "symbol": "<symbol>"
    },
    "name": "get_sector_peer_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
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 of behavioral disclosure. It only mentions that the peer basket is 'likely' (hinting at a heuristic nature) but does not disclose data source, freshness, limitations, or error behavior. This leaves the agent uncertain about the reliability and boundaries of the peer-rank and valuation outputs.

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 brief and front-loaded with the main purpose, followed by a concise bulleted list of outputs and an Args line. There is no redundant or filler information; every element earns its place.

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?

For a single-parameter tool with an output schema, the description covers the essential selection and invocation details, including example symbol formats. However, the lack of usage guidance and behavioral context means an agent must infer edge cases and suitability relative to sibling tools, making the description adequate but not fully complete.

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?

The input schema only declares a string property named 'symbol', but the description's Args section adds crucial domain context by specifying NSE symbols and giving three concrete examples (RELIANCE, HDFCBANK, INFY). Since schema description coverage is 0%, this compensation is valuable and makes the parameter unambiguous.

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 opens with 'Sector and peer context for a stock' and lists four concrete outputs (likely peer basket, peer rank, valuation vs peers, sector performance context), clearly identifying the tool's function and scope. However, it does not explicitly contrast with sibling tools like sector_performance or compare_stocks_tool, so it stops short of a perfect score.

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 explicit when-to-use guidance, alternatives, or exclusions. An agent must infer from the name and output bullets that this tool is for single-stock sector and peer comparison, but there is no statement distinguishing it from related tools such as sector_performance or compare_stocks_tool.

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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