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toolstem

Toolstem MCP Server

by toolstem
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TypeScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.2.9

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_company_metrics focuses on deep financial analysis and fundamental metrics, while get_stock_snapshot provides a comprehensive stock overview including real-time price and valuation. There is no overlap in functionality, making it easy for an agent to choose the right tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_company_metrics and get_stock_snapshot), using the same verb 'get' and descriptive nouns. This uniformity makes the tool set predictable and easy to understand.

    Tool Count2/5

    With only two tools, the server feels under-scoped for financial analysis, as it lacks essential operations like searching for companies, comparing metrics, or updating data. While the tools are well-defined, the count is too low to cover a comprehensive financial domain effectively.

    Completeness2/5

    The tool set is severely incomplete for financial analysis, missing critical operations such as listing companies, retrieving historical data, or performing comparisons. Agents will face dead ends when trying to conduct thorough analysis beyond the two provided snapshots.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 7 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • 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.

  • This repository includes a glama.json configuration file.

  • 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

  • Behavior3/5

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

    Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, covering safety. The description adds value by explaining derived signals and output structure, but doesn't disclose additional behavioral traits beyond what annotations provide.

    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?

    Two sentences, front-loaded with key content. Each sentence contributes: first lists included metrics, second explains output and use cases. No unnecessary words.

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

    Completeness5/5

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

    Given the presence of an output schema (handling return values), complete schema coverage, and annotations covering safety, the description provides sufficient context about purpose, usage, and derived signals. It is thorough for a tool of this complexity.

    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 the schema already documents both parameters adequately. The description does not add extra parameter detail beyond what is in the schema, aligning with the baseline of 3.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states it provides deep financial analysis and synthesizes key metrics, ratios, and statements into an agent-ready response. It distinguishes from siblings (compare_companies and get_stock_snapshot) by emphasizing depth and derived signals.

    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?

    Explicitly recommends use for fundamental analysis, financial health checks, or understanding a company's trajectory. While it doesn't directly mention alternatives, sibling tool names and the focus on depth imply when not to use.

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

  • Behavior4/5

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

    Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds behavioral context by explaining the tool synthesizes data from multiple sources, returns derived signals (dcf_signal), and provides curated agent-ready data. This adds value beyond the annotations.

    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, consisting of three focused sentences. The first sentence states the main purpose, the second lists key output components, and the third provides usage guidance. No redundant or irrelevant information.

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

    Completeness5/5

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

    Given the tool's simplicity (one parameter), presence of output schema, and rich annotations, the description sufficiently covers the tool's functionality, output highlights, and usage context. It explains derived signals and the nature of the data, making it complete for an agent to understand and invoke correctly.

    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 for the single required parameter 'symbol' (ticker). The description does not add additional semantic information about the parameter beyond what the schema already provides. With full schema coverage, a baseline score of 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool provides a comprehensive stock snapshot including real-time price, valuation metrics, DCF analysis, and analyst ratings. It distinguishes from siblings by noting it is a quick overview before diving into financials, differentiating from get_company_metrics and compare_companies.

    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 explicitly says 'Use this when you need a quick overview of a stock before digging into financials,' providing clear context for when to use the tool. It implies but does not explicitly state when not to use it or mention alternatives beyond the sibling context.

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

  • Behavior5/5

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

    Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description aligns fully, detailing the read-only operation and output format (derived rankings). No contradictions, and the description adds significant behavioral context (categories of metrics, derived rankings) beyond annotations.

    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?

    Three sentences: first states core purpose, second lists all metric categories, third gives use cases. Front-loaded, no filler, every sentence adds value.

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

    Completeness5/5

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

    Given the tool's complexity (many metrics and derived rankings) and the presence of an output schema, the description is complete. It covers input constraints (2-5 symbols), output nature (derived rankings), and typical use cases. No gaps for an agent to misuse.

    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% (the symbols parameter has a detailed description including example). The tool description restates '2-5 companies' but adds no new semantics beyond the schema. Baseline 3 applies.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description explicitly states the tool performs side-by-side comparison of 2-5 companies across price, valuation, profitability, financial health, growth, dividends, and analyst ratings. It also lists derived rankings (lowest_pe, etc.). This clearly distinguishes from siblings get_company_metrics (likely single company) and get_stock_snapshot (likely a quick overview).

    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 provides explicit use cases: 'Use this for investment comparisons, competitive analysis, or evaluating alternatives in the same sector.' It does not explicitly state when not to use or name alternatives, but the context is clear and actionable.

    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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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.

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