Skip to main content
Glama
jongall45

Frontrun MCP Server

by jongall45

frontrun_account_activity

Analyze venture capital activity on X by tracking account follows, sector distribution, and follow velocity to identify investment trends and behavior patterns.

Instructions

Get activity profile for a tracked account: follow velocity, sector distribution, snapshot coverage, and recent follows with classification. Use this to analyze a specific VC's recent behavior.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameYesTwitter/X username of the tracked account
sinceNoTime window: "7d", "30d", "90d", or ISO date. Default: "30d"

Implementation Reference

  • The 'frontrun_account_activity' tool handler implementation. Makes a GET request to /v1/vc/:username/activity endpoint to retrieve a tracked account's activity profile including follow velocity, sector distribution, and recent follows.
    server.tool(
      'frontrun_account_activity',
      'Get activity profile for a tracked account: follow velocity, sector distribution, snapshot coverage, and recent follows with classification. Use this to analyze a specific VC\'s recent behavior.',
      {
        username: z.string().describe('Twitter/X username of the tracked account'),
        since: z.string().optional().describe('Time window: "7d", "30d", "90d", or ISO date. Default: "30d"'),
      },
      async ({ username, since }) => {
        const params = new URLSearchParams();
        if (since) params.set('since', since);
        const qs = params.toString();
        const result = await apiCall('GET', `/vc/${encodeURIComponent(username)}/activity${qs ? '?' + qs : ''}`);
        return { content: [{ type: 'text', text: JSON.stringify(result, null, 2) }] };
      }
    );
  • Input schema definition using Zod for 'frontrun_account_activity' tool. Defines two parameters: 'username' (required Twitter/X username) and 'since' (optional time window for filtering activity data).
    {
      username: z.string().describe('Twitter/X username of the tracked account'),
      since: z.string().optional().describe('Time window: "7d", "30d", "90d", or ISO date. Default: "30d"'),
    },
  • The apiCall helper function used by the tool handler to make HTTP requests to the Frontrun API. Handles authentication, timeouts, rate limiting, and error responses.
    async function apiCall(method, path, body = null) {
      const url = `${API_URL}/v1${path}`;
      const options = {
        method,
        headers: {
          'X-API-Key': API_KEY,
          'Content-Type': 'application/json',
        },
      };
      if (body) {
        options.body = JSON.stringify(body);
      }
    
      const controller = new AbortController();
      const timeout = setTimeout(() => controller.abort(), 60000);
      options.signal = controller.signal;
    
      let response;
      try {
        response = await fetch(url, options);
      } catch (err) {
        clearTimeout(timeout);
        if (err.name === 'AbortError') return { error: 'Request timed out (60s). Try a narrower query.' };
        return { error: `Network error: ${err.message}` };
      }
      clearTimeout(timeout);
    
      if (response.status === 429) {
        const retry = response.headers.get('Retry-After') || '60';
        return { error: `Rate limited. Retry in ${retry}s.` };
      }
      if (response.status === 401) {
        return { error: 'Invalid API key. Check FRONTRUN_API_KEY.' };
      }
      if (response.status === 402) {
        const data = await response.json();
        return { error: 'Insufficient balance', ...data };
      }
      if (!response.ok) {
        const text = await response.text();
        return { error: `HTTP ${response.status}: ${text.slice(0, 500)}` };
      }
    
      return response.json();
    }
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 describes what the tool returns (activity profile with specific metrics) but does not disclose critical behavioral traits such as whether this is a read-only operation, authentication requirements, rate limits, error handling, or data freshness. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational characteristics.

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 appropriately sized and front-loaded, consisting of two sentences that efficiently convey the tool's purpose and usage. The first sentence lists key outputs, and the second provides context, with no wasted words or redundant information. Every sentence earns its place by adding value.

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?

Given the tool's complexity (analyzing account activity with multiple metrics), lack of annotations, and no output schema, the description is incomplete. It specifies what the tool does but fails to cover behavioral aspects, return format details, or error conditions. While it adequately states the purpose, it does not provide enough context for safe and effective use without additional assumptions.

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 100% description coverage, with clear documentation for both parameters ('username' and 'since'). The description does not add any meaningful semantic details beyond what the schema provides, such as explaining how 'follow velocity' is calculated or what 'sector distribution' entails. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 specific verbs ('Get activity profile') and resources ('tracked account'), listing concrete outputs like 'follow velocity, sector distribution, snapshot coverage, and recent follows with classification'. It distinguishes from siblings by focusing on analyzing a specific VC's recent behavior, unlike tools like 'frontrun_snapshot' or 'frontrun_new_follows' that might handle different aspects.

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 this tool ('to analyze a specific VC's recent behavior'), but does not explicitly state when not to use it or name alternatives among siblings. It implies usage for activity profiling rather than other operations like tagging or rule management, but lacks explicit exclusions or comparisons.

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

Install Server

Other Tools

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/jongall45/frontrun-mcp-server'

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