mcp-server-ollama-deep-researcher
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| EXA_API_KEY | No | API key for Exa neural search engine (Get yours at https://dashboard.exa.ai/api-keys) | |
| TAVILY_API_KEY | No | API key for Tavily web search service | |
| PERPLEXITY_API_KEY | No | API key for Perplexity AI-powered search service |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| researchC | Research a topic using web search and LLM synthesis |
| get_statusB | Get the current status of any ongoing research |
| configureC | Configure the research parameters (max loops, LLM model, search API) |
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 3 tools
Each tool has a clearly distinct purpose with no overlap: configure sets parameters, get_status checks progress, and research initiates the core workflow. An agent can easily distinguish between setup, monitoring, and execution functions.
All three tools follow a consistent verb_noun pattern (configure, get_status, research), with clear and predictable naming. There are no deviations in style or convention across the set.
With only 3 tools, the server feels thin for a 'deep researcher' domain that might benefit from more granular operations like refining queries or managing results. However, the core workflow is covered, making it borderline appropriate.
The tools cover the basic research lifecycle (configure, execute, monitor), but there are notable gaps such as no way to retrieve or export past research results, modify parameters mid-research, or handle errors. This could limit agent effectiveness in complex scenarios.