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Cognitive-Stack

Search Stock News MCP Server

by Cognitive-Stack
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: 'general-search' is for broad web searches, while 'search-stock-news' is specifically for stock-related news. There is no overlap or ambiguity between them, making it easy for an agent to select the appropriate tool based on the query context.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with hyphens: 'general-search' and 'search-stock-news'. They use the same naming convention throughout, making the set predictable and readable without any deviations or mixed styles.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a 'Search Stock News MCP Server'. While the tools cover general and stock-specific searches, the domain suggests potential for more operations like filtering, sorting, or retrieving detailed news, making the count too low for the apparent purpose.

    Completeness2/5

    The tool surface is significantly incomplete for a stock news server. It lacks essential operations such as filtering news by date, source, or stock ticker, retrieving trending news, or accessing detailed article content. This will likely cause agent failures when trying to perform comprehensive stock news analysis.

  • Average 2.9/5 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 status not available

  • This repository is licensed under MIT License.

  • 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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. While 'Perform a general web search' implies a read-only operation, it doesn't address important behavioral aspects like rate limits, authentication requirements, error handling, or what constitutes a 'general' versus specialized search. The mention of Tavily API is helpful but insufficient.

    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 extremely concise - a single sentence that communicates the core purpose efficiently. There's no wasted language or unnecessary elaboration, making it easy to parse quickly.

    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?

    For a search tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what results look like, how relevance scoring works, what the searchDepth levels mean, or provide any context about the Tavily API's capabilities or limitations.

    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 description adds no parameter-specific information beyond what's already in the schema (which has 100% coverage). It doesn't explain what 'general web search' means in relation to the parameters like searchDepth levels or score thresholds. The baseline of 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 ('Perform a general web search') and specifies the resource/API used ('using Tavily API'), which distinguishes it from generic search tools. However, it doesn't explicitly differentiate from its sibling 'search-stock-news', which appears to be a more specialized search tool.

    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. There's no mention of its sibling tool 'search-stock-news' or any other search tools, nor does it indicate appropriate contexts or exclusions for using this general web search.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the Tavily API but doesn't describe key behaviors such as rate limits, authentication needs, error handling, or what the search results include (e.g., headlines, summaries, sources). For a search tool with external API dependencies, this is a significant gap.

    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 with the core purpose and includes the API name for context. Every word earns its place, making it highly concise and well-structured.

    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 complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of articles with fields), how results are ordered, or any behavioral traits like pagination or API constraints. For a tool with rich input schema but missing output and behavioral context, it should do more.

    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 all 5 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples of how parameters interact or typical values). Baseline 3 is appropriate when 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 verb ('Search') and resource ('stock-related news'), and specifies the API used ('Tavily API'). It distinguishes from the sibling 'general-search' by focusing on stock-related content, though it doesn't explicitly mention this differentiation. The purpose is specific and actionable.

    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 'general-search', nor does it mention any prerequisites, exclusions, or contextual triggers. It simply states what the tool does without indicating appropriate scenarios or limitations.

    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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  • Evaluate tool definition quality.

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