PTC-MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a clearly defined singular purpose of executing Python programs with MCP integration.
Naming Consistency5/5The single tool follows a clear verb_noun naming pattern (execute_program). Since there are no other tools to compare against, consistency is inherently perfect.
Tool Count2/5One tool is too few for a server's apparent purpose of program execution with MCP integration, as it lacks complementary tools for tasks like listing available programs, managing execution environments, or handling errors. This feels thin and incomplete for the domain.
Completeness2/5The tool surface is severely incomplete for the inferred domain of program execution and MCP tool orchestration. There are obvious gaps, such as no tools for program management, debugging, or result handling, which will limit agent capabilities and cause failures in complex workflows.
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
- 0 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
- Behavior4/5
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 and does so effectively. It explains key behavioral traits: tool calls are dispatched to MCP servers, only stdout from print statements is returned (not intermediate results), and all tool functions require await. It doesn't cover potential limitations like execution timeouts or error handling, but provides substantial operational context.
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 efficiently structured with zero wasted sentences. It front-loads the core purpose, then explains behavioral constraints, followed by clear usage guidelines. Every sentence adds essential information about how the tool works and when to use it.
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?
For a tool with no annotations, no output schema, and a single parameter, the description provides comprehensive context about the tool's behavior, constraints, and appropriate usage. It explains the execution model, output limitations, and programming requirements. The main gap is lack of information about return format or error conditions, but overall it's quite complete for this complexity level.
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 'code' parameter. The description adds some context about how MCP tools are accessed within the code (namespaced names) and the requirement to use print() for output, but doesn't provide significant additional parameter semantics beyond what the schema indicates.
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's purpose with specific verbs ('execute a Python program') and resources ('with access to MCP tools as async functions'). It distinguishes this tool's unique capability of running multi-step scripts with tool integration from direct tool calls, even though there are no sibling tools to differentiate from.
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?
The description provides explicit guidance on when to use this tool ('when a task involves 3+ tool calls, loops, filtering, aggregation, or conditional logic based on intermediate results') and when not to use it ('for single tool calls, call the tool directly'). It clearly defines the appropriate context and alternatives.
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.
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