Skip to main content
Glama
geddamsatish

FinanceExpert

by geddamsatish
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation4/5

    Tools are mostly distinct: get_historical_prices provides raw data for charts, get_stock_analysis gives real-time price and fundamentals, and get_stock_price_chart returns a visual chart. However, get_historical_prices and get_stock_price_chart both relate to historical data, causing slight overlap.

    Naming Consistency5/5

    All tools use a consistent get_verb_noun pattern (get_historical_prices, get_stock_analysis, get_stock_price_chart). Naming is clear and predictable.

    Tool Count5/5

    With 3 tools covering historical data, real-time analysis, and charting, the set is appropriately scoped for a finance assistant. No tool feels unnecessary.

    Completeness3/5

    The tools cover basic stock data retrieval and charting but lack fundamental operations like search, comparison, or portfolio management. Gaps exist for a fully comprehensive finance tool.

  • Average 3.3/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    No annotations are provided, so description must disclose behavioral traits. It does not mention whether data is cached, any limits on 'days' parameter, or output format beyond 'closing prices'. No contradiction with annotations.

    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?

    Description is one concise sentence, front-loaded with purpose. No fluff, but could be longer for completeness.

    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 an output schema exists, return values are covered. However, no guidance on parameter ranges or timezone for days, and no usage context for how this relates to chart generation. Incomplete for a tool with zero annotations.

    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 0%, so description should compensate. It adds no detail about 'ticker' or 'days' beyond their names. Baseline is 3 due to absence of param info in description.

    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 fetches historical closing prices for chart generation, specifying the action (fetch), resource (historical prices), and purpose (chart generation). However, it does not differentiate from sibling tools like get_stock_price_chart or get_stock_analysis.

    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 siblings. It does not mention prerequisites, alternatives, or contexts where this tool is preferred.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries full burden. It indicates the tool fetches data (non-destructive), but does not disclose any potential latency, rate limits, or data freshness. The output schema exists but is not referenced; description could hint at what fields are returned beyond what's in schema.

    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 concise at one sentence, front-loading the key output fields. Every phrase adds value, with no waste.

    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?

    Given the tool has one simple parameter and an output schema, the description could be more complete by adding a note about the ticker format or data source. It is adequate but lacks some contextual details like time zone or data provider.

    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 0% (no description for the parameter), so the description must compensate. It does not explain what the ticker parameter expects (e.g., format, examples). However, the parameter has no enums and is simple, so the gap is small. Baseline 3 is appropriate.

    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 it fetches real-time stock price, 50-day average, and analyst recommendations. The verb 'fetches' and the specific resources are well-defined, but it does not distinguish itself from sibling tools like get_historical_prices or get_stock_price_chart.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage for obtaining current price, average, and recommendations, but provides no explicit guidance on when to use this tool versus alternatives. For instance, it does not mention that get_historical_prices might be for historical data or get_stock_price_chart for chart data.

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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses the output format (plain-text Unicode chart) and the two ways to specify time range, but does not mention rate limits, data latency, or whether it requires authentication. The behavior is partially transparent.

    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 two sentences, no waste. It front-loads the key purpose and follows with parameter guidance. Efficient.

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

    Completeness4/5

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

    Given the tool has an output schema, the description does not need to detail return structure. It covers the main purpose, parameter usage, and output format. For a simple chart tool with 3 parameters, this is reasonably complete.

    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 0%, so the description must compensate. It explains 'period' and 'days' parameters by specifying their relationship (period for calendar ranges, days alone). However, it does not describe the 'ticker' parameter or the exact format of 'period'. This adds some meaning but not comprehensive.

    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 specifies a verb-resource-action: it creates a plain-text Unicode chart for a stock. It is distinct from 'get_historical_prices' and 'get_stock_analysis' as it focuses on chart output, though it does not explicitly differentiate from those siblings.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description gives clear guidance on how to specify the time range: use period for calendar ranges or days alone. However, it does not explain when to prefer this tool over its siblings (e.g., when you need a visual chart vs. raw prices or analysis).

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-stock-server MCP server

Copy to your README.md:

Score Badge

mcp-stock-server MCP server

Copy to your README.md:

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/geddamsatish/mcp-stock-server'

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