Skip to main content
Glama
Speccy-Agent

x402-mcp

by Speccy-Agent
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are completely distinct in purpose: one fetches Polymarket prediction markets, the other executes Python code. There is no realistic risk of an agent confusing them.

    Naming Consistency5/5

    Both tool names follow the same lowercase snake_case verb_noun pattern: get_prediction_markets and exec_python. The naming is predictable and consistent.

    Tool Count3/5

    Two tools is on the thin side, and the tools are unrelated, making the server feel like a loose collection rather than a focused toolkit. The count is not unreasonable for a paid utility server, but it is borderline.

    Completeness2/5

    The tools have no shared domain and each is a single isolated operation. get_prediction_markets only returns top markets with no drill-down or follow-up actions, and exec_python is a one-off sandbox execution primitive. The broader x402 workflow is unclear, leaving significant gaps.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 9 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 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.

  • 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 behavioral disclosure burden. It usefully reveals a $0.01 USDC cost per call and that x402 settlement happens in the background. However, it says nothing about side effects, rate limits, or response behavior, though the verb 'get' implies a read operation.

    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 concise sentence that front-loads the core purpose and then adds the most important operational detail, cost. Every word earns its place, and there is no redundant or filler content.

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

    Completeness3/5

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

    For a simple tool with two optional parameters, the description plus schema is mostly sufficient, and the cost disclosure is valuable. But there is no output schema and no mention of what the returned markets look like, how pagination behaves, or how 'top' is determined for non-volume sort options.

    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 and their defaults/enums. The description adds minimal parameter meaning beyond the tool's overall purpose; the word 'top' hints at sorting but does not explain how the sort parameter affects results.

    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 states the tool gets top Polymarket prediction markets, which is a specific verb and resource. It does not explicitly distinguish itself from the sibling exec_python, but the domain and purpose are clear enough that no confusion is likely.

    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 its sibling exec_python, or any context about appropriate use cases. It only mentions a cost, which implies it should not be called unnecessarily, but it does not state that explicitly.

    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?

    With no annotations provided, the description carries the full behavioral disclosure burden and does so thoroughly: it covers execution isolation, cost per run, no network access, read-only filesystem, timeout, and output cap. This is exemplary transparency for a code execution tool.

    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?

    One compact, front-loaded sentence conveys all critical constraints with no filler. Every clause adds meaningful information an agent needs before invoking the tool.

    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, this is nearly complete: it specifies environment, limits, cost, and constraints. The only minor gap is that it does not explicitly describe the return format (e.g., stdout/stderr), though the output cap strongly implies a returned payload.

    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?

    The schema already fully documents the sole parameter ('code'). The description adds execution-relevant constraints—timeout, output cap, network restrictions, filesystem restrictions—that inform how the agent should write code. This goes beyond the schema's basic type and description.

    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 action ('Run Python code') and the execution environment ('isolated Docker sandbox'). It is immediately distinguishable from the sibling tool get_prediction_markets, which is about retrieving market data rather than executing code.

    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 does not explicitly explain when to use this tool versus alternatives, nor does it state when not to use it. It implies use for arbitrary Python execution, but there is no routing guidance relative to get_prediction_markets or any other tool.

    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

x402-mcp MCP server

Copy to your README.md:

Score Badge

x402-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/Speccy-Agent/x402-mcp'

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