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ronyv89

screener-mcp

by ronyv89
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one for searching/locating companies, the other for retrieving detailed financial data for a specific company. There is no overlap in functionality.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: 'search_company' and 'get_company_data'. The naming is predictable and follows standard conventions.

    Tool Count3/5

    With only 2 tools, the server feels minimal. While this is borderline, the narrow scope (searching and retrieving data from screener.in) justifies a small set, but it may be slightly thin for a general-purpose financial data server.

    Completeness4/5

    The server provides the essential workflow: search for a company then retrieve its data. Gaps include no ability to list top companies or compare multiple companies, but these are not critical for the core purpose of fetching screener.in data.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is failing

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  • This repository includes a README.md file.

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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?

    With no annotations, the description carries the burden of disclosing behavior. It accurately states the output (list of matching companies with URLs) but does not mention any potential limitations, authorization requirements, or discrepancies like exact-match vs partial-match results. The behavior is straightforward, but the description adds minimal context beyond the basic functionality.

    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, well-structured sentence that immediately states the action, scope, and expected result. There is no redundant information, and every word contributes to clarity.

    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?

    For a simple tool with one parameter and no output schema, the description is mostly complete: it explains what the tool does, what input it takes, and what it returns. It lacks guidance on how this tool fits into a larger workflow (e.g., pairing with get_company_data), but for its standalone functionality, it is adequate.

    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 schema covers 100% of parameter descriptions, with the 'query' parameter fully documented as 'Company name or NSE/BSE ticker symbol'. The description mirrors this information, adding only the context of 'Indian listed companies on screener.in'. Since schema coverage is high, the baseline is 3, and the description does not significantly enhance understanding beyond the schema.

    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's purpose: searching for Indian listed companies by name or ticker symbol on screener.in, and returning a list of matches with URLs. The verb 'Search' is specific, and the resource is well-defined, distinguishing it from the sibling tool 'get_company_data' which presumably fetches detailed data.

    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 clear context for when to use the tool: when you need to find companies by name or ticker. However, it does not explicitly mention alternatives or exclusions, such as 'use get_company_data for detailed information after searching'. The context is clear but lacks direct guidance on tool selection.

    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 the full burden. It states that data is fetched from an external source (screener.in) and hints at large response sizes via the 'fields' guidance, but it does not disclose whether the operation is read-only, potential rate limits, or any other behavioral traits that might surprise an agent. This is a minimal but not full disclosure.

    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 two sentences, front-loaded with the main purpose, and every sentence earns its place. The second sentence provides actionable guidance without redundancy.

    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?

    With no output schema, the description could do more to explain the return format, but for a simple fetch tool with 3 parameters (1 required) and covered schema, the description is sufficiently complete. It clearly states the source and gives parameter guidance, though it could mention the 'consolidated' parameter explicitly.

    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?

    Schema description coverage is 100% (baseline 3). The description adds meaning beyond the schema by explicitly noting 'Use the fields parameter to request only what you need and reduce response size' and 'Omit fields to get all', which provides practical, behavioral context for the parameter beyond its enum values.

    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 uses specific verb+resource: 'Fetch financial data for a company from screener.in.' This clearly distinguishes it from the sibling 'search_company' which presumably searches for companies, while this tool retrieves specific financial data for a given symbol.

    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 clear context that this tool is for fetching financial data, implying when to use it (when financial data is needed) versus search_company. It also gives explicit usage guidance on the 'fields' parameter to reduce response size. However, it does not explicitly mention alternative tools or exclusions, so it stops short of a 5.

    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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