digital-brain

by ArpitaSethi-12

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation4/5
Most tools are distinct, but get_activity and get_timeline have overlapping purposes (both show timeline, get_timeline just more detailed). Also, ask_brain and search_history both retrieve history but through different interfaces (Q&A vs keyword search), which could cause ambiguity. Overall, boundaries are clear for most tools.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case: ask_brain, check_status, get_activity, get_stats, get_timeline, search_history. The pattern is uniform and predictable.
Tool Count5/5
With 6 tools, the set is well-scoped for a browsing history assistant. Each tool serves a distinct need (Q&A, health check, daily overview, statistics, detailed timeline, keyword search) without redundancy or bloat.
Completeness4/5
The tool surface covers the main browsing history interactions: query, search, timeline, stats, health. However, there is no dedicated tool to retrieve all events of a specific type without a date or search query, which is a minor gap. Otherwise, the set feels complete for a read-only assistant.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. Only states it returns aggregated statistics (top domains, daily breakdown) and period parameter. No mention of authentication, rate limits, data freshness, or whether it mutates state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise: two sentences plus argument documentation. Front-loads main purpose. Could benefit from slightly more structure or a brief usage note, but efficient.
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 output schema exists, return values need not be documented. However, lacks contextual info like data time range, source scope, or limitations. Acceptable for a simple stats tool but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, description adds critical meaning: lists valid values for 'period' (day, week, month) and explains its purpose. Schema only had type string with default 'week', so description provides essential usage info.
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?
Clearly states 'browsing activity statistics — top domains and daily breakdown', specifically identifying verb and resource. Differentiates from siblings like get_activity and get_timeline which likely deal with raw data or timelines.
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?
No explicit guidance on when to use this tool vs siblings (e.g., get_activity, search_history). Does not provide when-not or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the tool's capabilities but fails to disclose important behavioral traits like accessing personal data (user's browsing history) and potential privacy implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections and examples, but it is verbose. Some sentences could be merged or removed without losing clarity. It earns its place but could be more concise.
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 (5 parameters, 1 required) and no annotations, the description covers purpose, parameters, and examples well. It lacks usage guidelines and behavioral context, but overall it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description excellently compensates by explaining each parameter with examples (e.g., 'RAG pipeline' for query) and clarifying defaults (limit=10). It adds significant value beyond the schema's type constraints.
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 searches Chrome browsing history by keyword, listing the event types it covers (page visits, selections, etc.). Sibling tools have distinct purposes (ask_brain, check_status, etc.), so this tool is well-distinguished.
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 does not explicitly state when to use this tool vs alternatives. It is implied by the unique functionality (searching history), but no guidance on when not to use it or prerequisites is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses use of RAG and requirement of Ollama/OpenRouter, which is helpful. However, it omits details on response behavior, error handling, or performance implications.
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 concise, front-loaded with purpose, then details. Every sentence adds value without redundancy. The Args list is structured and clear.
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?
The tool has an output schema (not shown) so return values need not be described. The description covers input semantics, technology, and requirements. It is complete for basic use, though a note on answer format or limitations would improve it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description's Args section adds meaning: examples for question, allowed values for event_type, domain description, and format for last. This compensates well, though further detail on defaults could elevate it.
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 it answers natural-language questions about browsing history using RAG. It distinguishes from siblings like search_history by emphasizing natural language and AI-powered answers.
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 implies usage for natural language queries but does not explicitly contrast with sibling tools or state when not to use it. No exclusions or alternatives are provided, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It implies a read-only operation ('View') and mentions chronological order, but lacks details on authentication, rate limits, or whether results are paginated. It does not contradict annotations (none exist).
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 extremely concise: two sentences with no redundancy. The first sentence states the purpose, the second explains the parameter. It is front-loaded and every word is informative.
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 simplicity (one optional parameter, output schema exists), the description is largely complete. It covers what the tool does and how to use the parameter. However, it could briefly mention what data is returned (e.g., URLs or summaries) to fully set expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It adds valuable meaning for the only parameter 'date' by specifying allowed values ('today', 'yesterday', 'YYYY-MM-DD'), which goes beyond the schema's type and default. However, it could clarify time zone or format strictness.
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: 'View a chronological timeline of browsing activity for a specific day.' It specifies the resource (browsing activity), verb (view), and scope (chronological timeline, specific day), effectively distinguishing it from sibling tools like search_history or get_stats.
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 basic context (for a specific day and accepted date formats) but does not explicitly state when to use this tool versus alternatives like get_timeline or search_history. No 'when not to use' guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It discloses what is reported but does not mention any behavioral traits like side effects, auth needs, or rate limits. Basic but adequate for a read-only health check.
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?
Two sentences, front-loaded purpose, no redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 0 params and an output schema, the description fully conveys what the tool does and what it returns. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, and schema coverage is 100%. Description does not need to add param info, but it lists reported items which adds value. Baseline 4 is appropriate.
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 checks health status of backend and LLM, listing specific reports. It is distinct from siblings like get_stats or get_timeline which serve different purposes.
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?
While the purpose is clear, no explicit guidance is given on when to use this vs alternatives. Context implies it's for monitoring, but lacks explicit when-to-use or when-not-to-use instructions.
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?
With no annotations provided, the description must convey behavioral traits. It states that the tool returns a 'detailed chronological event feed' with 'full content previews,' implying a read-only operation with no side effects. No contradictions with annotations (none present).
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 concise and well-structured: a clear purpose sentence, a comparison with a sibling tool, a use-case statement, and a parameter list. Every sentence adds value with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description need not explain return values. It adequately covers purpose, usage context, parameter semantics, and comparison with a sibling tool. No gaps remain for an agent to misuse this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides an explicit Args section that explains both parameters beyond what the schema offers. It specifies valid values for 'date' ('today', 'yesterday', or 'YYYY-MM-DD') and explains 'limit' as 'Maximum events to return (default 20).' This is essential given 0% schema description coverage.
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: 'Get a detailed chronological event feed for a specific date.' It distinguishes itself from the sibling tool get_activity by noting that it returns more detail per event, including full content previews.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context on when to use this tool ('useful for reconstructing what happened on a day') and compares it to get_activity. However, it does not explicitly state when not to use this tool or suggest alternative tools for different scenarios.
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:

[![digital-brain MCP server](https://glama.ai/mcp/servers/ArpitaSethi-12/digital-brain/badges/card.svg)](https://glama.ai/mcp/servers/ArpitaSethi-12/digital-brain)

Score Badge

Copy to your README.md:

[![digital-brain MCP server](https://glama.ai/mcp/servers/ArpitaSethi-12/digital-brain/badges/score.svg)](https://glama.ai/mcp/servers/ArpitaSethi-12/digital-brain)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ArpitaSethi-12/digital-brain'

If you have feedback or need assistance with the MCP directory API, please join our Discord server