Python MCP Template
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'new_endpoint' stands alone with a distinct purpose, so an agent cannot misselect between multiple options.
Naming Consistency5/5A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns. The name 'new_endpoint' follows a clear verb_noun structure, but consistency cannot be assessed across a set of one.
Tool Count2/5A server with only one tool feels thin and under-scoped for most purposes, especially given the generic 'Python MCP Template' name that suggests broader functionality. One tool is typically insufficient for meaningful agent interactions, indicating a mismatch with the apparent scope.
Completeness1/5The tool surface is severely incomplete, as a single 'new_endpoint' tool does not cover any domain meaningfully. There are obvious gaps in CRUD operations, lifecycle management, or any coherent workflow, making it impossible for agents to perform useful tasks beyond a trivial response.
Average 1.7/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
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This repository includes a README.md file.
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glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
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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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but provides minimal behavioral information. It documents response formats for success (200) and validation error (422) cases, which gives some insight into possible outcomes, but doesn't describe what the tool actually does, whether it's read-only or mutative, authentication requirements, rate limits, or other behavioral characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is poorly structured - it leads with a tautological title, then dives into response format details without first explaining the tool's purpose. While not excessively verbose, the content is misprioritized and doesn't efficiently communicate essential information. The response documentation could be useful but comes before establishing basic understanding.
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?
For a tool with 1 parameter, 100% schema coverage, and an output schema, the description is incomplete. It documents response formats but fails to explain what the tool does, its purpose, or when to use it. The presence of an output schema means return values are documented elsewhere, but the description should still provide context about the tool's function and behavior.
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 fully documents the single 'name' parameter. The description adds no parameter information beyond what's in the schema - it doesn't explain how the name parameter affects the response or provide additional context about parameter usage. Baseline 3 is appropriate when schema does all the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5Does the description clearly state what the tool does and how it differs from similar tools?
The description is tautological - 'New Endpoint' restates the tool name without explaining what it does. It provides no verb or resource specification, no indication of functionality, and fails to distinguish from siblings (though none exist). The description focuses entirely on response formats rather than the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool. The description contains only response documentation with no context about appropriate use cases, prerequisites, or alternatives. There's no mention of when this tool should be invoked versus other approaches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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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