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Social Search MCP

by shubhamekapure
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

Social Search MCP Server

A Model Context Protocol (MCP) server that empowers LLMs to search across specific social media platforms using the industry's best search engines.

Overview

This server provides a search_social tool that accepts a query and an optional list of platforms (facebook, reddit, linkedin, etc.). It filters results exclusively to those domains and returns them directly to the LLM context.

Related MCP server: OneSearch MCP Server

Search Providers

You can configure the backend by setting the SEARCH_PROVIDER environment variable. By default, it uses SearXNG since it is free and open-source.

1. SearXNG (Default)

SearXNG is a free, open-source internet metasearch engine.

  • SEARCH_PROVIDER=searxng

  • SEARXNG_URL=http://localhost:8080 (Defaults to localhost, specify a remote public instance if you don't host your own, but note that public instances often limit automated JSON requests).

2. Serper.dev

A powerful Google Search wrapper API. Highly recommended for accurate results.

  • SEARCH_PROVIDER=serper

  • SERPER_API_KEY=your_key (Get one from Serper.dev)

3. Google Custom Search

The official Google Custom Search API.

  • SEARCH_PROVIDER=google

  • GOOGLE_API_KEY=your_key

  • GOOGLE_CX=your_cx_engine_id

Setup

Ensure you have Python 3.10+ installed.

# Clone or place in a directory, then:
python -m venv venv
source venv/bin/activate
pip install -e .

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "social-search-mcp": {
      "command": "/ABSOLUTE/PATH/TO/venv/bin/social-search-mcp",
      "args": [],
      "env": {
        "SEARCH_PROVIDER": "searxng",
        "SEARXNG_URL": "http://localhost:8080"
      }
    }
  }
}

Replace /ABSOLUTE/PATH/TO/ with the actual path to this folder. Restart Claude Desktop after updating the config.

Available Tools

1 tool
search_socialC

Perform a web search focused ONLY on specific social media platforms.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query (e.g. 'rent listings', 'used bike')
platformsNoList of platforms to search. Options: facebook, instagram, twitter, reddit, linkedin, snapchat, tiktok, pinterest. Defaults to searching all if omitted.
max_resultsNoMax number of results to retrieve (default: 10, max: 30)
time_filterNoOptional time filter. Options: 'day', 'week', 'month', 'year'. Leave empty for relevance sorting.
glNoOptional geolocation code (e.g. 'in' for India, 'us' for USA).
hlNoOptional language code (e.g. 'hi' for Hindi, 'en' for English).

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It states the tool performs a 'web search' but does not explain how results are retrieved, formatted, or limited (e.g., pagination, rate limits, authentication needs). The description lacks details on error handling, response structure, or operational constraints, leaving significant gaps in behavioral understanding.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy for an AI agent to parse quickly. This optimal conciseness earns the highest score.

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?

Given the tool's complexity (6 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits, usage context, and output expectations. Without annotations or an output schema, the description should provide more comprehensive guidance to help the agent understand how to invoke and interpret results, but it falls short.

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, providing clear details for all 6 parameters. The description adds no additional parameter semantics beyond implying a focus on social media platforms, which is somewhat redundant with the schema's 'platforms' parameter. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate with extra insights.

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 clearly states the tool's purpose: 'Perform a web search focused ONLY on specific social media platforms.' It specifies the verb ('search'), resource ('social media platforms'), and scope ('web search focused ONLY on specific...'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a score of 5.

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 alternatives, prerequisites, or exclusions. It mentions the focus on social media platforms but does not clarify use cases, limitations, or comparisons to other search tools. This lack of contextual guidance limits its utility for an AI agent.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.1
    • First observedsearch_social
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TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

The single tool name follows a consistent verb_noun pattern (search_social), and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and predictable.

Tool Count2/5

A single tool is too few for a server named 'Social Search MCP', which implies a broader scope of social media search operations. This minimal set feels thin and inadequate for the apparent domain, limiting functionality.

Completeness2/5

The tool surface is severely incomplete for social media search, as it only offers a generic search without coverage for specific platforms, filtering, or advanced operations like trending topics or user lookups. This will likely cause agent failures due to significant gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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