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NagrajADesai

mcp-indian-stocks

by NagrajADesai

Get Historical Data

get_historical_data

Fetch historical price and metric data for Indian stocks by stock name and period, with optional filters like PE, sales, or EV/EBITDA.

Instructions

Get historical price and metric data for a stock.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoMetric to filter by (options: 'price', 'pe', 'sm', 'evebitda', 'ptb', 'mcs')price
periodYesTime period (options: '1m', '6m', '1yr', '3yr', '5yr', '10yr', 'max')
stock_nameYesName of the stock (e.g., 'RELIANCE')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden and provides only a minimal restatement of purpose. It says nothing about whether data is delayed, how results are ordered, or any rate/permission constraints; the existence of an output schema offsets return-format concerns only partially.

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?

A single, front-loaded sentence with zero waste. It is arguably too terse for the information it omits, but as a structure it is clean and efficient.

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?

An output schema exists, so return-value explanation is not required, and the parameter surface is simple (3 params, 2 required). Still, with no annotations and no usage context, the definition is only minimally viable for an agent trying to decide between this and the many sibling data-retrieval tools.

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?

Schema description coverage is 100%, so each of the three parameters is already fully documented including the enumerable filter and period options. The description's phrase 'price and metric data' loosely gestures at the filter dimension but adds no syntax or semantics beyond the schema, matching the baseline 3.

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?

States a specific verb ('Get') and resource ('historical price and metric data for a stock'), which is clear enough to distinguish it from siblings like get_stock_forecasts or get_stock_details. However, it does not explicitly contrast itself against those siblings, so it falls short of a 5.

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 versus get_stock_forecasts, get_stock_details, or get_price_shockers, and no prerequisites or exclusions stated. The agent must infer usage from the name alone.

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