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GMPSignal Real-Time Intelligence MCP (59 Tools)

GMPSignal Real-Time Intelligence MCP (59 Tools)

Official
by xpayrcom

startup_vc_dealflow_yc_github_radar

Extract real-time structured dealflow data from YC startups, GitHub trending repos, and Hacker News to surface startup and VC opportunities.

Instructions

YC Startups, GitHub Trending & HN DealFlow Radar: Real-time structured data extraction feed for YC Startups, GitHub Trending & HN DealFlow Radar.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "startup_vc_dealflow_yc_github_radar"
  }
}

Input Schema

NameRequiredDescriptionDefault
maxResultsNoMaximum number of structured items to extract.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.5/5.0
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 behavioral burden, and it discloses almost nothing: no authentication requirements, rate limits, freshness guarantees behind "real-time," or whether the three sources are queried together or merged. Only "real-time" and "structured" add any behavioral signal beyond the name.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, but it spends its only sentence repeating the tool name and the list of sources twice, which wastes space without adding information. It is not padded with noise, but the redundancy means little of it earns its place.

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?

With no output schema and no annotations, the description is the only place an agent could learn what a "structured item" contains, how the three heterogeneous sources are unified, or how results are ordered. None of this is covered, which leaves a multi-source aggregation tool substantially under-described.

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?

There is a single optional parameter, maxResults, with 100% schema description coverage, so the schema already explains its meaning and default. The description adds nothing about how maxResults is applied across the three sources or whether it is per-source or total, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names three concrete sources (YC Startups, GitHub Trending, HN DealFlow) and calls itself a "real-time structured data extraction feed," which hints at an aggregation/extraction tool. However, it largely restates the tool name verbatim and never clarifies what a returned item is or how the three sources relate, so an agent cannot distinguish it confidently from siblings such as hackernews_devto_tech_launch_sentiment_radar.

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?

There is no when-to-use guidance, no stated preconditions, and no mention of when another sibling tool would be the better choice. The word "real-time" implies a live-data use case but this is inference, not 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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