Skip to main content
Glama
34t34f3

defi-yield-scanner-mcp

by 34t34f3

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct aspect: token risk, yield opportunities, and wallet exposure. No two tools overlap in purpose, making selection unambiguous.

    Naming Consistency4/5

    Two tools use 'scan_' prefix while the third uses 'check_', a minor deviation. All follow a clear verb_noun pattern, so the inconsistency is not confusing.

    Tool Count5/5

    Three tools is a well-scoped count for a DeFi scanner, covering the essential workflows without unnecessary bloat.

    Completeness4/5

    The set covers token risk analysis, yield discovery, and wallet-level exposure. Minor gaps like deeper drill-downs exist, but core workflows are fully supported.

  • Average 4/5 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
  • 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.

  • 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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It discloses the main actions (analyze, checks, provides recommendations) and scope ('Supports any EVM address on Base'), but does not mention read-only nature, data sources, rate limits, or output format. No contradictions found.

    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 exactly two sentences, front-loaded with the primary action, then providing specific details. Every word contributes value; no filler or 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?

    For a single-parameter tool with no output schema, the description gives sufficient information to invoke it correctly: the purpose, the input, and the key features. It doesn't describe the return format, but that is not required given the simplicity.

    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 coverage is 100% with a basic description for wallet_address. The tool description adds the crucial constraint that it supports 'any EVM address on Base', clarifying the accepted input format beyond the schema. This extra context elevates the score above the baseline.

    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 identifies the tool's function: 'Analyze wallet exposure and portfolio risk across DeFi protocols', with specific features (concentrated positions, protocol risks, diversification recommendations). This distinguishes it from siblings like scan_token_risk and scan_defi_yields, which focus on narrower concerns.

    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 explicit guidance on when to use this tool vs its siblings. It lists capabilities but does not state conditions like 'use for portfolio-level risk' or alternatives such as 'for single-token risk, use scan_token_risk'. The intended use case is only implied.

    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, the description carries the burden of behavioral disclosure. It states the tool returns a structured risk report with recommendations, providing some insight into output behavior. However, it does not mention any limitations, side effects, or prerequisites beyond the input. For a read-only analysis tool, this is adequate but not comprehensive.

    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 (two sentences) and front-loaded with the core purpose ('Comprehensive token risk analysis'), then enumerates specific aspects and outcomes. Every sentence adds value 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?

    For a tool with one parameter and no output schema, the description adequately covers what it does, on which chains, and what it returns. It could be enhanced by noting any error conditions or interpretation guidance, but the simple input and read-only nature make this sufficient.

    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 already documents the single 'token_address' parameter at 100% coverage, so the description adds no additional parameter semantics. Baseline of 3 is appropriate since the schema fully describes the parameter.

    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 verb ('scans') and resource ('any token on Base/Ethereum/Arbitrum'), and lists specific analysis dimensions (liquidity, volume, market cap, holder concentration, contract security). It distinguishes itself from siblings like scan_defi_yields and check_wallet_exposure by focusing on token risk.

    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: use this for comprehensive token risk analysis. It does not explicitly mention alternatives or exclusions, but the focus on token risk makes the intended use obvious. However, it lacks an explicit 'when not to use' or comparison with sibling tools.

    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?

    No annotations are provided, so the description carries the full burden. It discloses that the tool returns APY, TVL, and pool data, and that data is updated in real-time from DeFiLlama. The verb 'Scan' implicitly indicates a read-only operation, and the return data description gives a clear behavioral picture. However, it does not explicitly state any limitations or side effects (e.g., rate limits, API dependencies), though for a scanning tool this is less critical.

    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 three sentences, each contributing value: the first states the purpose, the second details the output data, and the third notes the data source and real-time nature. There is no redundant or filler content, and the key information is front-loaded.

    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?

    The tool is simple with two parameters and no output schema. The description covers what the tool does, what it returns, and the data source. Sibling tools are clearly different, and the schema documents the parameters, so the description is complete for an agent to select and use the tool 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?

    Schema coverage is 100% for both parameters (chain and min_tvl), so the baseline is 3. The description does not add any additional meaning beyond the schema; it mentions 'Base chain' in the text, which aligns with the chain parameter default, but does not elaborate on min_tvl or provide usage details beyond what the schema already states.

    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: 'Scan top DeFi yield opportunities across Base chain.' It uses a specific verb ('Scan') and resource ('DeFi yield opportunities'), and includes the chain scope. This distinguishes it from sibling tools like scan_token_risk and check_wallet_exposure, which address different concerns.

    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 implies usage context (finding yield opportunities on Base chain) but does not explicitly state when to use this tool over alternatives or provide exclusions. However, the clear distinction from sibling tools (token risk, wallet exposure) gives the agent sufficient context to select this tool for yield scanning.

    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

defi-yield-scanner-mcp MCP server

Copy to your README.md:

Score Badge

defi-yield-scanner-mcp 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/34t34f3/defi-yield-scanner-mcp'

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