AI research agents with social data: give agents real numbers

November 8, 2026

An AI research agent is only as good as its sources. Ask a model which creators review standing desks and it will name channels from memory, some of which went quiet two years ago. Give it tools that return live public data and it can search, check follower counts, read transcripts and quote comments, with a link for every claim.

MCP, the Model Context Protocol, is an open-source standard for connecting AI applications to external systems. A social data MCP server turns each endpoint into a tool the agent can call from Claude Code, Cursor, Codex or any other MCP client.

Who runs research agents, and for what

• Marketing teams: "Find 20 mid-size creators in home office gear and summarize what their audiences ask about."
• Product managers: "What do people on Reddit and YouTube complain about in our category this month?"
• Analysts and founders: "Compare how three competitors post on TikTok, Instagram and LinkedIn."
• Agencies: one repeatable brief per client per week, with the same structure each time.

The data a research brief needs

Most briefs follow the same four moves: discover, profile, sample recent posts, then read what people said. Here is the brief "creators reviewing standing desks, and what viewers complain about":

Worked example: what a month costs

One brief is 68 credits. At $1.88 per 1,000 credits on the $47 pack, that is about $0.13.

• Five briefs a week is about 22 a month: 22 × 68 = 1,496 credits, or $2.81.
• The 25,000-credit pack covers about 367 briefs (25,000 / 68).
• The 100 free signup credits cover one full brief, so you can test the workflow before paying.

Agents retry. Failed calls, private accounts, not-found answers and cached repeats are not charged, so a confused agent that asks for the same profile twice inside the cache window does not pay twice. Still set a budget, because an agent that pages through 40 pages of search results will spend 40 credits doing it.

A budget guard in code

If you run your own agent loop instead of an MCP client, wrap every tool call in a guard. The response envelope reports credits_used, so the guard can stop the run before it overspends.

import os, requests

class Budget:
    def __init__(self, limit):
        self.limit, self.spent = limit, 0

    def call(self, path, **params):
        if self.spent >= self.limit:
            return {"error": "budget reached, answer with what you have"}
        r = requests.get("https://scrapeio.com" + path, params=params,
                         headers={"x-api-key": os.environ["SCRAPEIO_API_KEY"]}, timeout=60)
        body = r.json()
        self.spent += body.get("credits_used", 0) or 0
        return body

b = Budget(limit=100)
hits = b.call("/v1/tiktok/search/keyword", query="standing desk review", sort_by="most-liked")
for v in hits["data"]["items"][:10]:
    print(v["author"]["handle"], v["plays"], v["url"])
print("spent", b.spent)

Returning a plain message instead of raising an error matters. The model reads it and writes its answer from what it already has, instead of retrying.

Metrics that tell you the agent is working

• Credits per answer. Track it per brief type. If it doubles, the agent is paging or retrying too much.
• Citation coverage = claims with a source URL / all numeric claims. Aim for 100%. Anything lower means the model filled gaps from memory.
• Freshness. Every number should carry the date it was fetched. Follower counts from last quarter are a different fact.
• Tool error rate = failed calls / all calls. A high rate usually means bad inputs, such as full URLs where a handle was expected.

Pitfalls

• Silent gap filling. When a call fails, some models invent the missing number. Put "if a tool fails, write unknown" in the system prompt.
• Cross-platform comparisons. A TikTok play and a YouTube view are counted differently. Compare within a platform, or normalize per follower. See how reel play counts work.
• Runaway pagination. Tell the agent the maximum pages per search, usually two.
• Context bloat. Forty full comment pages will fill the context window. Ask the agent to summarize each page before fetching the next.
• Scope. Research public creators, brands and topics. Do not point an agent at private individuals.

Run it from an AI agent

Add the hosted server once:

claude mcp add --transport http scrapeio https://scrapeio.com/mcp --header "x-api-key: $SCRAPEIO_API_KEY"

Setup for Cursor, Codex and other clients is on the agents page. For ready-made prompts, install the skills with npx skills add xhoantran/scrapeio-agent. Then give the agent a brief with limits built in:

Research creators who review standing desks.
Budget: 100 credits. Max 2 pages per search.
Use tiktok_search_keyword and youtube_search to find candidates,
then tiktok_profile / youtube_channel for each of the top 20.
Read transcripts and comments for the 10 most viewed videos.
Every number needs the source URL and today's date. If a tool fails, write unknown.

For more on picking a server, see social media MCP servers compared, and for a deeper single-platform workflow, Instagram reels research with an agent. Per-platform tool lists are on pages such as TikTok MCP server and YouTube MCP server.

How to do it with ScrapeIO

ScrapeIO's MCP server exposes 194 read-only endpoints across 33 platforms with the same field names everywhere (handle, followers, plays, likes), so one prompt works across TikTok, YouTube, Reddit and the rest. Most tools cost 1 credit, credits never expire, and 100 free credits cover your first brief.

Sources

Checked on 8 November 2026 from each vendor's own pages. Prices and limits change; follow the links before you decide.

• https://modelcontextprotocol.io/introduction