Neuro MCP V2

by Arks-06

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: emotion analysis, file reading, memory recall, memory store, web search, and file writing. No overlap in functionality.
Naming Consistency4/5
All tools follow a verb_noun pattern (e.g., analyze_text_emotion, read_workspace_file). The only minor deviation is 'tavily_web_search' which includes a brand name, but it still fits the pattern.
Tool Count5/5
Six tools is well within the ideal 3-15 range. The tool count matches the server's purpose of providing a capable assistant with memory, file operations, web search, and emotion analysis.
Completeness4/5
Core operations are covered: file read/write, memory recall/store, web search, and text analysis. Minor gaps include lack of file deletion or memory deletion, but the set enables key workflows.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits 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
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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 are provided, so the description carries full burden. It mentions 'live web' and 'highly optimized real-time' but fails to disclose behavioral traits like rate limits, authentication, response size, or whether it returns raw data or processed summaries. This is insufficient for an agent to anticipate behavior.
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 conveys the core purpose without redundancy. It is front-loaded with the action and resource.
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?
The tool has a simple signature (1 param) and an output schema (not shown), so description doesn't need to explain returns. However, it lacks details on result structure, pagination, or error handling, which are useful for completeness. Still adequate for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is one parameter 'query' with 0% schema description coverage. The description does not elaborate on the query format, length limits, or whether it supports natural language or specific syntax. The agent must infer meaning from 'Search the live web', which is inadequate.
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 verb 'Search' and the resource 'live web using the Tavily API' for real-time technical answers, documentation, news, or updates. It distinguishes itself from siblings which are unrelated (emotion analysis, file operations, memory).
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 real-time technical queries but lacks explicit guidance on when not to use it or alternatives. It does not specify limitations (e.g., not for non-technical topics) or context for choosing this over other tools.
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 provided, so description carries burden. Describes permanence but lacks details on overwrite behavior, idempotency, or potential effects on existing memories.
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 with purpose, no unnecessary words. Efficient and well-structured.
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 no annotations and an output schema, description should mention return value or confirmation. Missing details on effect and response, though tool complexity is low.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. Description adds no meaning to 'concept' or 'details' beyond what is in the schema. Fails to explain what these parameters represent or how to use them.
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?
Description clearly states the action (store), resource (long-term semantic context/user preferences), and permanence (in vector database). It effectively distinguishes from sibling tool recall_persistent_memory.
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?
Explicitly states when to use: when user asks to remember or key insights are uncovered. Could be improved by noting when not to use, but provides clear guidance.
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?
The description adds behavioral context beyond the bare schema by noting automatic directory creation. However, it does not mention that content is overwritten without confirmation or discuss error handling, which would be useful given no annotations.
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, ~25 words) and front-loaded with the primary action. Every sentence is informative with no unnecessary words.
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 simplicity, the description covers the main purpose and a key side effect. However, it lacks detail on overwrite behavior and return values (though an output schema may fill that gap). Overall adequate but not rich.
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 0% schema description coverage, the description must compensate. It mentions file_path and content implicitly but does not add details like path format or content encoding, so it provides only marginal added meaning.
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 action (write/overwrite text content), the resource (specific file), and the location (sandboxed workspace folder). It distinguishes from the sibling tool read_workspace_file.
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?
It provides explicit context for when to use the tool ('save notes, code, or outputs') and mentions the automatic directory creation behavior. However, it does not explicitly exclude scenarios or compare with alternatives.
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 must disclose all behavioral traits. It states the file is read as full text and is restricted to the workspace folder, but does not mention error handling (e.g., file not found, permissions), encoding, or size limits. This leaves gaps for an AI agent.
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, no redundant words, and the most important information is front-loaded. Every sentence adds value.
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 parameter, output schema exists), the description covers the main purpose and scope. It lacks details on error behavior and security restrictions beyond 'sandboxed', but these are acceptable for a straightforward read 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?
The input schema has one parameter 'file_path' with 0% description coverage. The description adds the constraint that the path must be 'strictly inside the sandboxed workspace folder', providing context beyond the schema. However, it does not specify path format (relative/absolute) or constraints, so it partially compensates.
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 specifies the action ('read the full text contents'), the resource ('a specific file'), and the scope ('strictly inside the sandboxed workspace folder'). It also mentions typical use cases ('get context from previous documents or code files'), distinguishing it from sibling tools like write_workspace_file.
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 explicitly says 'Use this to get context from previous documents or code files', which gives a clear when-to-use. It does not directly state when not to use or name alternatives, but the sibling tools are distinct enough that the usage context is clear.
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, the description carries the full burden. It discloses that the pipeline runs locally, which is a key behavioral trait, and lists the emotion labels. It does not detail performance or limitations, but the information is sufficient for a simple classification tool.
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?
Two sentences that front-load the main action and purpose. The first sentence is slightly long but clear. No superfluous information.
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 presence of an output schema, the description does not need to explain return values. It covers the input, the emotion categories, and a usage scenario. Basic edge cases are not addressed, but the tool's simplicity makes this acceptable.
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 single parameter 'text' has no schema description, and the schema coverage is 0%. The description adds context by referring to 'a block of text', but does not specify format, length, or encoding. It provides minimal added value beyond the type.
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 runs a deep learning pipeline to detect semantic emotional markers, lists the specific emotions (Joy, Sadness, Anger, Fear, Surprise, Disgust, Neutral), and distinguishes it from sibling tools that handle file operations, memory, or search.
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?
Explicitly suggests usage for adapting tone or understanding user sentiment, but does not provide explicit when-not-to-use or alternatives. The context from sibling tools makes its purpose distinct.
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?
The description indicates it queries a persistent vector database, implying a read-only operation, but does not disclose limitations, privacy implications, or behavior when no results are found. With no annotations provided, the description carries the full burden; it provides basic context but lacks depth.
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 two sentences, front-loading the core action and usage guidance. Every word serves a purpose, achieving high conciseness without sacrificing clarity.
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 and the presence of an output schema, the description adequately covers the tool's purpose and usage. It does not explain edge cases or result handling, but these are partially addressed by the output schema. The description is nearly complete for a recall tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, requiring the description to compensate for parameter meaning. However, the description does not mention the 'query' or 'n_results' parameters, leaving the agent to infer from names alone. This is insufficient when coverage is low.
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 identifies the tool as querying a persistent vector database to retrieve past context, preferences, or project details. It distinguishes itself from sibling tools like store_persistent_memory and tavily_web_search by specifying its recall function.
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?
Explicitly tells the agent when to use this tool: 'Use this to check if you have existing knowledge on a topic the user mentions.' This provides clear guidance on invocation context, differentiating it from storage or search tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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