MCP Deep Search

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.23
Disambiguation2/5
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.
Naming Consistency3/5
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.
Tool Count2/5
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.
Completeness2/5
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.
Average 4.1/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 18 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it explains that the tool uses AI agents to 'automatically search multiple sources, analyze findings, and compile comprehensive reports.' This clarifies the multi-step, automated nature of the research process. Annotations already indicate it's read-only, non-destructive, open-world, and non-idempotent, so the description appropriately focuses on operational behavior without contradiction.
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 efficiently structured in two sentences: the first explains the core functionality, and the second provides usage context. Every phrase adds value without redundancy, and it's appropriately front-loaded with the main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-step AI research), the description adequately covers the high-level process but lacks details about output format (no output schema exists), potential limitations, or error handling. With annotations covering safety aspects, the description provides reasonable context but could be more complete for such a sophisticated tool.
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?
With 100% schema description coverage, the input schema fully documents both parameters ('query' and 'modelClass' with enum values). The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation without providing extra semantic value.
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 in-depth research on complex topics using AI agents that automatically search multiple sources, analyze findings, and compile comprehensive reports.' It specifies the verb (perform research), resource (complex topics), and method (using AI agents). However, it doesn't explicitly differentiate from its sibling 'deep_search' tool, which likely has overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidance: 'Ideal for thorough investigations, market research, technical analysis, or any topic requiring deep understanding from multiple perspectives.' This suggests appropriate contexts but doesn't explicitly state when to use this tool versus the 'deep_search' sibling or other alternatives, nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable context beyond annotations: it specifies that the tool uses 'real-time data from multiple search providers' and is for 'current information', which clarifies its dynamic, non-idempotent nature hinted by annotations (idempotentHint: false). Annotations cover safety (readOnlyHint: true, destructiveHint: false) and scope (openWorldHint: true), so the description appropriately supplements with operational details without contradiction.
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 front-loaded with the core purpose in the first sentence and usage guidelines in the second. Every sentence earns its place by clearly stating what the tool does and when to use it, with zero wasted words. It is appropriately sized for a tool with four parameters and good annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search with multiple providers and parameters), annotations cover safety and scope well, and schema coverage is 100%. The description adds necessary context about real-time data and usage scenarios. However, without an output schema, the description could briefly mention return types (e.g., search results or answers) to be more complete, though it's not required.
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?
Schema description coverage is 100%, so the schema fully documents all parameters (query, provider, maxResults, includeAnswer). The description does not add specific meaning beyond the schema, such as explaining how 'deep web' relates to parameters or provider capabilities. Baseline 3 is appropriate as the schema carries the burden, but no extra value is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('perform deep web searches', 'find current information', 'research topics', 'answer questions') and resources ('real-time data from multiple search providers'). It distinguishes from the sibling tool 'comprehensive_research' by emphasizing 'deep web searches' for 'up-to-date information beyond your knowledge cutoff', suggesting a different scope or methodology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use this when you need up-to-date information beyond your knowledge cutoff.' This provides clear context for usage and implies an alternative (relying on internal knowledge) without naming specific sibling tools, but the mention of 'deep web searches' and 'real-time data' differentiates it from other potential tools like 'comprehensive_research'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![MCP Deep Search MCP server – quality and maintenance score on Glama](https://glama.ai/mcp/servers/just-every/mcp-deep-search/badges/card.svg)](https://glama.ai/mcp/servers/just-every/mcp-deep-search)

Score Badge

Copy to your README.md:

[![MCP Deep Search MCP server – quality and maintenance score on Glama](https://glama.ai/mcp/servers/just-every/mcp-deep-search/badges/score.svg)](https://glama.ai/mcp/servers/just-every/mcp-deep-search)