Indian Stock Exchange API2 MCP Server

mutual_funds

Retrieve mutual fund data including NAV, returns, and details from Indian stock exchanges to analyze investment options.

Instructions

Mutual Funds Endpoint: `/mutual_funds` Method: GET Description: Retrieve the latest data for mutual funds, including net asset value (NAV), returns, and other details. Example Request: `http GET /mutual_funds` Example Response: ```json { \

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "mutual_funds"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Implementation Reference

server.py:56-61 (registration)
The "mutual_funds" tool is not explicitly defined as a Python function, but is registered automatically by FastMCP.from_openapi() using the definition provided in the OPENAPI_SPEC variable.
```
mcp = FastMCP.from_openapi(
    openapi_spec=openapi_dict,
    client=client,
    name="indian_stock_exchange_api2",
    version=__version__
)
```

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Mar 25, 2026

TDQS

C2.8/5.0

Behavior2/5

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

No annotations are provided, so the description must carry full behavioral disclosure. While it notes the GET method (implying read-only), it omits critical details: dataset scope (all funds or top N?), pagination behavior, rate limits, and authentication requirements. The truncated example response ('{ \"') further reduces transparency.

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?

Description suffers from formatting clutter (markdown headers '###', '**Endpoint:**') that adds noise for AI consumption. The embedded example response is truncated mid-JSON, indicating poor structuring. Content is not front-loaded; HTTP method details appear before the semantic description.

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?

Without an output schema, the description should explain return values, but the example response is cut off. The zero-parameter schema suggests this returns a large dataset, yet no pagination or filtering guidance is provided. Sibling tool relationships remain unexplained.

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?

Input schema has zero parameters. Per evaluation rules, zero parameters warrants a baseline score of 4. The description does not need to compensate for missing parameter documentation.

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 clear verb ('Retrieve') and resource ('mutual funds'), specifying data types returned (NAV, returns). However, it fails to differentiate from the 'mutual_fund_search' sibling tool, leaving ambiguity about when to use list vs search.

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?

Contains no guidance on when to use this tool versus alternatives like 'mutual_fund_search' or 'historical_data'. No prerequisites, filtering capabilities, or usage constraints are mentioned despite the zero-parameter schema implying a broad data dump.

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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