MCP Deep Search
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ENV_FILE | Yes | Path to the environment file containing API keys. Must use absolute path (e.g., /Users/yourname/.llm.env or $HOME/.llm.env) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| deep_searchA | Perform deep web searches to find current information, research topics, or answer questions using real-time data from multiple search providers. Use this when you need up-to-date information beyond your knowledge cutoff. |
| comprehensive_researchA | Perform in-depth research on complex topics using AI agents that automatically search multiple sources, analyze findings, and compile comprehensive reports. Ideal for thorough investigations, market research, technical analysis, or any topic requiring deep understanding from multiple perspectives. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have overlapping purposes, both focused on research and searching for information, with 'comprehensive_research' emphasizing analysis and reports and 'deep_search' focusing on real-time data. The boundaries are unclear, as an agent might struggle to choose between them for tasks like market research or technical analysis, leading to potential misselection.
The tool names use a consistent snake_case pattern, but they lack a predictable verb_noun structure. 'comprehensive_research' uses an adjective-noun format, while 'deep_search' uses an adjective-noun format as well, but the verbs are implied rather than explicit, making it readable but not fully consistent in action-oriented naming.
With only 2 tools, the server feels thin for a domain like 'Deep Search' that implies broad research capabilities. This limited set may not adequately cover the scope, as it lacks tools for specific operations like filtering results, saving reports, or handling different data types, making it borderline too few for the apparent purpose.
There are significant gaps in the tool surface for a research domain. The server lacks essential operations such as retrieving saved reports, updating research parameters, deleting old data, or managing search sessions. This incompleteness will likely cause agent failures when trying to perform end-to-end research workflows.