veriloop
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool performs a completely different function: arithmetic evaluation, file reading, and web fetching. There is no overlap, so an agent can easily select the right tool.
Naming Consistency4/5Two tools follow verb_noun pattern (file_read, web_fetch), but 'calculator' is a noun, which is a minor deviation. Still, the names are clear and predictable overall.
Tool Count5/5With only 3 tools, the server is well-scoped as a small utility set. Each tool serves a distinct purpose, and the count feels appropriate for the intended lightweight functionality.
Completeness4/5The tools cover basic arithmetic, file reading, and web fetching, but missing complementary operations like file_write or web_post create minor gaps. These are workable for typical sandbox use cases.
Average 4.1/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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 failing
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.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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the transparency burden. It discloses the HTTP method and the crucial 'offline stub by default' behavior, which is valuable caveat. However, it does not detail response handling, error behavior, or potential side effects, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the primary action and resource. Every word adds value, including the important 'offline stub' caveat. It is appropriately concise with no filler.
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 simple one-parameter tool with an output schema, the description covers the essential purpose and a key behavioral trait. However, it lacks usage guidance and alternative differentiation, and the absence of annotations increases the need for more contextual details about expected behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description for the 'url' parameter (coverage 0%), so the description must compensate. It only says 'Fetch a URL,' which essentially restates the parameter name and adds no information about URL format, encoding, or expected constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Fetch a URL') with a specified resource and method ('over HTTP GET'), which distinguishes it from sibling tools like calculator and file_read. The additional caveat 'offline stub by default' adds useful specificity about the tool's behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions or limitations beyond the stub note. It only implies usage for fetching URLs, but does not offer explicit contextual direction.
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 the burden of behavioral disclosure. It discloses supported operators and parentheses, but does not mention error behavior (e.g., division by zero), precision, or whether the operation is side-effect-free. The word 'pure' hints at a pure function but is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that immediately states the tool's purpose and then provides necessary syntax details. No redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple calculator tool with a single string parameter and an output schema, this description is sufficiently complete. It defines the input format and scope, and the output schema covers return values, so no additional behavior needs to be explained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines 'expression' as a string, with 0% coverage of its format. The description compensates by specifying the allowed operators (+ - * / // % **) and parentheses, giving the agent a clear understanding of the expected input syntax.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Evaluate') and a resource ('pure-arithmetic expression'), and it distinguishes itself from sibling tools (file_read, web_fetch) by clearly indicating a math evaluation function. The operator list further specifies the scope.
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?
It clearly implies when to use it (for arithmetic calculations) and the 'pure-arithmetic' qualifier excludes non-math uses. However, it lacks explicit 'when not to use' or named alternatives, though the sibling tools are obviously different in purpose.
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 present, so the description carries the full burden. It explicitly states this is a read operation (non-destructive) and restricts use to UTF-8 text files inside the sandbox, which is meaningful disclosure. It lacks details on error handling or path traversal, but these are less critical for a simple read tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. Every word contributes meaning, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple, and the description covers the essential aspects: purpose, file type, and scope. An output schema exists, so return values are covered. It doesn't mention error cases, but for a basic read tool this is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description compensates by specifying that the path refers to a UTF-8 text file inside the sandbox. This provides crucial context for the 'path' parameter, making its meaning clear beyond just a bare string.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Read') and resource ('UTF-8 text file') with a clear scope ('inside the sandbox directory'). It distinguishes the tool from siblings (calculator, web_fetch) by focusing on local file reading.
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 that this tool reads files within the sandbox directory, implying it is not for external resources. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5.
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/arvindcr4/veriloop'
If you have feedback or need assistance with the MCP directory API, please join our Discord server