safe-agent-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion between tools. The name 'run_command' clearly describes its function, eliminating any ambiguity.
Naming Consistency5/5The single tool follows a consistent verb_noun pattern ('run_command'). There are no other tools to create inconsistency, so the naming is perfectly consistent.
Tool Count3/5The server has only one tool, which feels thin for a typical tool set. While it may cover the core purpose of safe command execution, the surface is minimal and could benefit from additional related tools.
Completeness5/5The server's stated purpose is to execute shell commands in a sandbox, and 'run_command' fully covers this operation by returning stdout, stderr, and exit code. No obvious gaps exist for the intended scope.
Average 4.4/5 across 1 of 1 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 status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return payload (stdout, stderr, exit code) and the sandbox context, which implies isolation. However, it does not mention potential side effects (e.g., resource usage, network access) or whether the command runs with any restrictions. Since annotations are absent, more behavioral detail would be helpful.
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 concise, consisting of one main clause and one clarifying note. It avoids unnecessary detail and is well-structured for quick comprehension.
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?
Given the tool’s simplicity and lack of output schema, the description adequately explains the return format and provides usage guidance. It does not cover edge cases like error handling or timeouts beyond the schema’s timeout parameter, but for a basic shell execution tool this is sufficient.
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 already defines both parameters with descriptions, providing 100% coverage. The description adds a meaningful note about the command parameter (not JSON-wrapped), which supplements the schema without redundancy. Thus a score slightly above baseline is warranted.
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 the tool executes a shell command in a sandbox, with a specific verb and resource. It also provides an example command, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Despite no sibling tools, the description includes a direct usage guideline: instructing the user to provide only the command itself without JSON wrapping. This clarifies the expected input format and preempts common misuse.
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.
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