VeyraX
OfficialServer Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: get_flow retrieves a specific workflow, get_tools lists available tools and flows, and tool_call executes a method from another tool. The descriptions explicitly differentiate their roles, making misselection unlikely.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern (get_flow, get_tools, tool_call) using snake_case throughout. This predictable naming scheme enhances readability and usability for agents.
Tool Count3/5With only 3 tools, the server feels thin for its apparent scope of managing workflows and dynamic tools. While the tools cover core operations, the low count may limit functionality, such as lacking update or delete capabilities for workflows.
Completeness3/5The tool set covers basic retrieval and execution (get_flow, get_tools, tool_call) but has notable gaps for a workflow management domain. Missing operations include creating, updating, or deleting workflows, which could hinder agent workflows that require full lifecycle management.
Average 3.3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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.jsonto 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns dynamic tools and flows based on user access, which adds some context about permissions and scope. However, it doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, error conditions, or response format details, leaving significant gaps for a tool that fetches system metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise at four sentences, but it could be more front-loaded. The first sentence states the core purpose, but subsequent sentences mix usage guidance and additional return details without a clear hierarchical structure. Some redundancy exists (e.g., mentioning tool list retrieval multiple times).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (fetching dynamic system metadata with access control), no annotations, and no output schema, the description is incomplete. It lacks details on return format (e.g., structure of tool/flow lists), error handling, authentication needs, or how the parameters influence results, making it inadequate for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the two required parameters (question and tool). The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining how these parameters affect the search or results. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'retrieve a list of available tools from the Veyrax API' and mentions it also returns flows. It specifies the verb ('retrieve') and resource ('list of available tools'), but doesn't explicitly differentiate from sibling tools like get_flow or tool_call, which would require a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some implied usage guidance: it suggests using this tool to get tool information before calling tool_call, and mentions it returns dynamic tools and flows. However, it lacks explicit when-to-use rules, alternatives, or exclusions compared to siblings like get_flow, leaving room for ambiguity.
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?
With no annotations provided, the description carries full burden of behavioral disclosure. It states this is a retrieval operation but doesn't mention whether it requires authentication, has rate limits, what happens with invalid IDs, or what the return format looks like. The description adds some context about what a workflow contains, but lacks critical behavioral details for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with three sentences. The first sentence clearly states the purpose, the second provides helpful context about workflows, and the third gives usage guidance. Each sentence earns its place, though the second sentence could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter retrieval tool with no output schema and no annotations, the description provides adequate basic information but lacks details about return values, error conditions, and behavioral constraints. It covers the core purpose and usage but doesn't fully compensate for the missing structured information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single 'flowId' parameter. The description adds minimal value beyond the schema by mentioning that flow IDs can come from 'user directly OR using get-tools method', but doesn't provide format examples or validation rules. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'retrieve a specific workflow by its ID' with additional context about what a workflow is. It distinguishes from sibling 'get_tools' by focusing on retrieving a single workflow rather than listing tools. However, it doesn't explicitly differentiate from 'tool_call' which might execute workflows.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'once you have a flowId' and mentions 'get-tools method' as an alternative way to obtain flow IDs. It doesn't explicitly state when NOT to use it or provide detailed alternatives, but the guidance is sufficient for basic usage.
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 mentions that parameters are 'based on get-tools tool response,' which hints at dependency, but doesn't disclose critical behavioral traits like error handling, authentication needs, rate limits, or what happens if invalid tool/method names are provided. For a meta-tool that executes other tools, this lack of transparency is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that are front-loaded with the main purpose. Every sentence earns its place by explaining the tool's function and prerequisite. However, it could be slightly more concise by combining ideas, and the structure is simple but effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity as a meta-execution tool with no annotations and no output schema, the description is moderately complete. It covers the purpose and prerequisite but lacks details on behavioral aspects like error handling or return values. For a tool that dynamically invokes other tools, more context on execution flow and results would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description adds minimal value beyond the schema by mentioning that parameters are 'required for that method' and 'based on get-tools tool response,' but doesn't provide additional syntax, format, or examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'execute a specific method of another tool with the provided parameters based on get-tools tool response.' It specifies the verb ('execute'), resource ('method of another tool'), and prerequisite ('based on get-tools tool response'). However, it doesn't explicitly differentiate from sibling tools like 'get_flow' or 'get_tools' beyond mentioning the latter as a prerequisite.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: after using 'get_tools' to discover available tools and methods. It implies usage by stating 'based on get-tools tool response,' which guides the agent to first call 'get_tools.' However, it doesn't explicitly state when NOT to use it or name alternatives, such as directly calling other tools if their methods are already known.
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:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/VeyraX/veyrax-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server