MCP Server
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 single tool 'get_docs' has a clear, distinct purpose of searching documentation for specific libraries.
Naming Consistency5/5Since there is only one tool, naming consistency is inherently perfect. The tool name 'get_docs' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.
Tool Count2/5A single tool is too few for a server named 'MCP Server' with a broad documentation search purpose across multiple libraries. This minimal toolset feels thin and under-scoped, limiting functionality to basic search without supporting operations like listing available libraries or managing search parameters.
Completeness2/5The tool surface is severely incomplete for a documentation search server. While 'get_docs' provides search functionality, there are significant gaps: no tools to list supported libraries, filter results, handle pagination, or retrieve metadata about documents. This will likely cause agent failures when more complex interactions are needed.
Average 3.1/5 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
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that it returns '文档中的文本' (text from documents), which gives some insight into output behavior, but lacks details on critical aspects like rate limits, authentication needs, error handling, or whether it's a read-only operation. For a search tool with zero annotation coverage, this leaves significant gaps in transparency.
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 and front-loaded. It starts with the core purpose, followed by supported libraries, parameters, and return value in a clear, bullet-like structure. Each sentence adds value without redundancy, making it efficient. A minor deduction for slightly informal formatting, but overall it's concise and well-structured.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose, parameters, and return value, but lacks details on behavioral traits like error cases or performance limits. Without an output schema, it should ideally explain the return format more thoroughly, but the mention of '文本' (text) provides a basic understanding. This results in an adequate but not fully comprehensive description.
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 description adds meaningful semantics beyond the input schema. With 0% schema description coverage, the schema only provides titles and types. The description explains that 'query' is '要搜索的查询' (the query to search, e.g., 'React Agent') and 'library' is '要搜索的库' (the library to search, e.g., 'agno'), including examples and a list of supported libraries. This compensates well for the low schema coverage, though it doesn't detail all possible library values or query formats.
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: '搜索给定查询和库的最新文档' (search for the latest documents given a query and library). It specifies the verb '搜索' (search) and resource '文档' (documents), and lists the supported libraries. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score of 5.
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?
The description provides minimal usage guidance. It lists supported libraries, which implies when to use it (for those specific libraries), but does not offer explicit when-to-use vs. when-not-to-use scenarios, prerequisites, or alternatives. Without sibling tools, it cannot compare to other options, resulting in a basic level of guidance.
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/GobinFan/python-mcp-server-client'
If you have feedback or need assistance with the MCP directory API, please join our Discord server