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BrainDAO

DefiLlama MCP Server

by BrainDAO
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.8

  • Disambiguation5/5

    The two tools serve clearly distinct purposes: `execute` runs JavaScript against the DefiLlama client, while `search_docs` provides documentation lookup. There is no overlap or confusion between them.

    Naming Consistency5/5

    Both tool names are lowercase and follow an imperative style: a single verb (`execute`) and a verb_noun compound (`search_docs`). The naming pattern is simple and consistent across the set.

    Tool Count3/5

    With only two tools, the count is on the low end. However, the pair of research (search_docs) and action (execute) is a coherent minimal design, though it may feel thin compared to more direct data-access tool sets.

    Completeness5/5

    The `execute` tool provides full programmatic access to all DefiLlama services via JavaScript, making the surface functionally complete. The addition of `search_docs` fills the knowledge gap, covering the entire workflow without dead ends.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • 7 of 8 community issues answered or closed in the last 6 months
    • 76 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 ISC 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.

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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

  • Behavior5/5

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

    Beyond the minimal readOnlyHint=false annotation, the description thoroughly discloses behavioral traits: the execution model (async function, JSON-serializable return), console.log output capture, the full client service surface, and sandbox constraints. This provides rich context for predicting tool behavior.

    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 efficiently structured: a front-loaded purpose, a usage template, a compact list of client services, and a set of constraints. Each sentence adds value, and the length is appropriate for the tool's complexity.

    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?

    The description covers core functionality, client capabilities, return format, and limitations, but it omits behavior on errors/timeouts and does not explain the 'intent' parameter. Given the tool's complexity and lack of output schema, these gaps make it not fully 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?

    The input schema has no descriptions for 'code' or 'intent' (0% coverage). The description explains the expected content of 'code' via the `async function run(defillama)` template, but it never clarifies the optional 'intent' parameter. This partial compensation for the schema gap earns a mid score.

    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 with a specific verb and resource: 'Run async JavaScript against a pre-wired DefiLlama client.' It provides a concrete usage template and lists the client's services, making it easy to distinguish from the sibling search_docs tool.

    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 context on how to use the tool, including the required async function signature and constraints like 'no fs, no network outside the client' and 'variables don't persist between calls.' However, it does not explicitly mention when to use this tool over search_docs or any exclusions, so it falls short of full guidance.

    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 declare readOnlyHint=true, so the description's mention of 'search' aligns. It adds value by specifying the kind of results (methods, parameters, example code) and the intended usage context, though it doesn't go into deeper behaviors like rate limits or output format.

    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 concise sentences with no redundancy. The first states the action, the second provides usage guidance, and every word earns its place.

    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 search tool with 2 parameters and no output schema, the description adequately covers purpose, usage, and output hint. The 'detail' parameter is not explained, which is a minor gap given its simplicity.

    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 gives meaning to the 'query' parameter as the search term but entirely omits the 'detail' parameter. Partial compensation only.

    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 searches DefiLlama SDK docs for methods, parameters, and example code, using the specific verb 'search' and resource 'docs.' It distinguishes from the sibling 'execute' tool by framing this as a pre-execution lookup.

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

    Usage Guidelines5/5

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

    Explicitly says to use before writing execute() code when unsure of the API, providing both a clear context and an implicit alternative (execute). This is strong when-to-use guidance.

    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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