CyberQuant MCP Server

by slb-cn

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

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

Latest release: v0.1.7
Disambiguation5/5
Each tool has a clearly distinct purpose: configuration, listing routes, getting route details, querying data, bulk metadata, user profile, cache clearing. No overlapping functionality.
Naming Consistency4/5
Most tools follow verb_noun pattern (list_routes, get_route_detail, query_data, etc.), but 'configure' is a single verb without a noun, causing a slight inconsistency.
Tool Count5/5
7 tools is well within the ideal 3-15 range. The count feels appropriate for a data query API server without being too heavy or too light.
Completeness5/5
The tool set covers the full lifecycle: setup (configure), discovery (list_routes, get_route_detail, get_routes_metadata), query (query_data), account info (get_user_profile), and maintenance (clear_routes_cache). No obvious gaps.
Average 4.3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 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
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 carry behavioral transparency. It states the scope ('available to current user') but does not disclose any behavioral traits such as authentication requirements, rate limits, or whether it refreshes data. For a read-like operation, it lacks detail.
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?
Single sentence efficiently conveys purpose, scope, and content without wasted words.
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?
For a simple list tool with no parameters and no output schema, the description is largely complete: it specifies what fields are included, what is excluded, and the user scope. It could explicitly state it returns a list, but that is implied.
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?
Tool has zero parameters; schema coverage is 100% (empty schema). The description adds no parameter information, which is acceptable since there are none. Baseline is 4 for zero parameters.
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 it lists available data route directories, specifies it's lightweight with limited fields (routeSlug, name, description, category), and explicitly excludes input/return fields. This distinguishes it from a detailed tool like get_route_detail.
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?
Description implies usage for a quick summary view by stating 'lightweight' and listing excluded fields, but does not explicitly state when to use this versus siblings like get_route_detail or query_data.
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 provided, so the description must fully disclose behavioral traits. It implies a read operation but does not explicitly state it's read-only or mention any side effects.
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?
Single sentence, front-loaded with purpose, no superfluous 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 no output schema, the description mentions return field details but lacks specifics about the return format or structure, leaving it incomplete.
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 100%, but the description adds context by explaining the parameter meaning and providing an example and a method to obtain valid values, exceeding what the schema alone provides.
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 the verb (query), resource (single route), and scope (input parameters and return field details, plus value format). Distinguishes from siblings like list_routes and query_data.
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?
Implies usage by referencing list_routes to get the routeSlug, but does not explicitly state when to use this tool over 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 behavior. It states returns CSV format but omits details on rate limits, authentication, error handling, or whether it is read-only. The prerequisite step is helpful, but more behavioral context would improve transparency.
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 unnecessary words. The first sentence states purpose, the second provides essential prerequisite. Front-loaded and efficient.
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 dynamic params object and no output schema, the description adequately tells the agent the return format (CSV) and the cross-reference to get_route_detail for parameter details. It could mention error conditions, but the core usage is covered.
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?
The schema covers 100% of parameters with descriptions, but the description adds value by directing users to get_route_detail for understanding the dynamic 'params' object, which goes beyond the schema's generic 'key-value pass-through' explanation.
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 queries data of a specified route by parameters and returns CSV format. It distinguishes itself from siblings like list_routes (listing) and get_route_detail (detail) by focusing on data retrieval with dynamic parameters.
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 instructs users to first call get_route_detail to learn input/return field descriptions before organizing params. This provides a clear workflow, though it could mention when not to use (e.g., if only need route metadata).
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?
No annotations are provided, so the description bears full burden. It mentions the output is '压缩 JSON' (compressed JSON) and lists the included fields. It implies a read-only query but does not explicitly state the absence of side effects or authentication requirements. The description is largely transparent but could mention that no modifications occur.
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. The first sentence immediately states the purpose and return fields, while the second specifies usage context. No unnecessary words or repetition. It is well-structured and front-loaded.
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 no output schema, the description adequately explains the return value by listing key fields (email, subscription level, available markets, expiration time, account validity, rate limit config). It also mentions compressed JSON format. However, it does not describe error responses or the exact structure, but for a simple read-only tool this is mostly complete. A minor improvement would be to note that it returns a single JSON object.
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?
There are no parameters, so schema coverage is 100%. The description naturally cannot add parameter semantics. According to the rules, with 0 parameters the baseline score is 4, and there is no need for additional param info. The score 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 it queries account and permission info for the current API key, listing specific fields (email, subscription, markets, expiry, validity, rate limits). The verb '查询' (query) and resource '账户与权限信息' (account and permission info) are specific. It is clearly distinct from sibling tools like get_route_detail (routes) and configure (mutation).
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 states when to call: '仅在需要核对账户状态、可用市场范围或查看限流配额时调用' (only when needing to check account status, available markets, or rate limits). This provides clear context but does not explicitly mention when not to use or list alternatives. However, given the simple scope, the guidance is sufficient.
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?
Without annotations, the description carries full burden. It discloses that the tool returns compressed JSON, incurs large token cost, and lists output fields. No destructive behavior is mentioned, which is appropriate for a read-only operation.
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, each serving a distinct purpose: specifying output and providing usage guidance. No unnecessary words.
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 description explains output content and usage scenario. For a tool with no parameters and no output schema, it is mostly complete but could add details on decompression or exact JSON structure.
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 no parameters and 100% schema coverage, the description adds significant meaning by explaining the tool's purpose and output content (compressed JSON with specific fields).
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 specifies the verb '返回' (return) and resource '所有路由的完整元数据' (complete metadata of all routes), listing included fields. It clearly distinguishes from sibling tools like list_routes and get_route_detail.
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 states when to use: only when needing complete field definitions at once, citing high token cost. It implies alternatives (lighter tools) but does not explicitly name them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it clears a local file, does not make network requests, and forces a refresh on next query. This is comprehensive and transparent.
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 concise sentences in Chinese, front-loading the action and file path, then usage conditions and effect. Every sentence adds value without redundancy.
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?
For a simple cache-clearing tool with no output schema, the description adequately covers purpose, usage, and behavior. It could mention return status, but the core information is present.
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?
The input schema has zero parameters, so schema coverage is 100% by default. The description adds no parameter information as none are needed, achieving the baseline for a parameterless tool.
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 clears local route metadata daily cache, specifying the exact file path. It distinguishes from siblings like list_routes and get_route_detail 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use: when user permissions/accessible routes change and daily cache hasn't auto-refreshed. It doesn't list alternatives or when not to use, but the context is clear for a maintenance tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behaviors: query mode is read-only, does not write file, does not echo secret; write mode updates config file. It covers all parameter behaviors and side effects.
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, well-structured paragraph that front-loads purpose, then explains modes and parameters efficiently. Every sentence adds value; no redundancy.
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 moderate complexity (3 params, no output schema), the description is complete. It covers all modes, all parameters, defaults, and file location. No gaps remain for an agent to understand usage.
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?
Schema coverage is 100%, but description adds significant context: first-required vs optional, default values, retention behavior. This goes beyond the schema descriptions, providing complete parameter semantics.
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 configures API Key for data service, with two distinct modes: query (no params) and write (with params). It mentions the config file location and distinguishes from sibling tools which are about routes, queries, etc.
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 provides explicit guidance on when to use query mode (no params) vs write mode (any param). It explains first-time required vs optional parameters, default values, and behavior when parameters are omitted.
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:

[![cyberquant-mcp MCP server](https://glama.ai/mcp/servers/slb-cn/cyberquant-mcp/badges/card.svg)](https://glama.ai/mcp/servers/slb-cn/cyberquant-mcp)

Score Badge

Copy to your README.md:

[![cyberquant-mcp MCP server](https://glama.ai/mcp/servers/slb-cn/cyberquant-mcp/badges/score.svg)](https://glama.ai/mcp/servers/slb-cn/cyberquant-mcp)

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/slb-cn/cyberquant-mcp'

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