Skip to main content
Glama
jwaresolutions

Polygon MCP Server

get_daily_open_close

Retrieve daily open and close prices for a specific stock on a given date using Polygon.io financial data.

Instructions

Get daily open/close prices

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesTicker symbol (e.g., AAPL)
dateYesDate (YYYY-MM-DD format)

Implementation Reference

  • The asynchronous handler function that implements the get_daily_open_close tool by calling the Polygon API to retrieve daily open and close prices for a given ticker and date.
    get_daily_open_close: async (args: { ticker: string; date: string }) => {
      try {
        const response = await polygonApi.get(`/v1/open-close/${args.ticker}/${args.date}`);
        return {
          content: [{
            type: "text",
            text: JSON.stringify(response.data, null, 2)
          }]
        };
      } catch (error: any) {
        return {
          content: [{
            type: "text",
            text: `Error getting daily open/close: ${error.response?.data?.message || error.message}`
          }],
          isError: true
        };
      }
    },
  • src/index.ts:312-329 (registration)
    Registration of the get_daily_open_close tool in the ListTools handler, including its name, description, and input schema for validation.
    {
      name: "get_daily_open_close",
      description: "Get daily open/close prices",
      inputSchema: {
        type: "object",
        properties: {
          ticker: {
            type: "string",
            description: "Ticker symbol (e.g., AAPL)"
          },
          date: {
            type: "string",
            description: "Date (YYYY-MM-DD format)"
          }
        },
        required: ["ticker", "date"]
      }
    },
  • Input schema defining the parameters (ticker and date) for the get_daily_open_close tool.
    inputSchema: {
      type: "object",
      properties: {
        ticker: {
          type: "string",
          description: "Ticker symbol (e.g., AAPL)"
        },
        date: {
          type: "string",
          description: "Date (YYYY-MM-DD format)"
        }
      },
      required: ["ticker", "date"]
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't add any context about traits such as data freshness, rate limits, authentication needs, or error handling. This is a significant gap for a tool that likely queries financial data.

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 a single, efficient sentence with zero waste. It's front-loaded and gets straight to the point, making it easy for an agent to parse quickly without unnecessary details.

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 the lack of annotations and output schema, the description is incomplete. It doesn't address what the tool returns (e.g., price values, timestamps, or error cases) or behavioral aspects like data sources or limitations. For a financial data tool, this leaves critical gaps in understanding.

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 input schema has 100% description coverage, clearly documenting both parameters (ticker and date). The description doesn't add any meaning beyond this, as it doesn't explain parameter interactions or provide examples. Baseline 3 is appropriate since the schema does the heavy lifting.

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 action ('Get') and the resource ('daily open/close prices'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its siblings like 'get_latest_quote' or 'get_snapshot', which might also provide price-related data, so it doesn't reach the highest 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 guidance on when to use this tool versus alternatives like 'get_latest_quote' or 'get_aggregates'. It lacks context about specific use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jwaresolutions/polygon-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server