Server Quality Checklist
- Disambiguation5/5
The two tools have clearly distinct purposes: one lists all available servers (discovery), while the other retrieves a specific connection endpoint (retrieval). No functional overlap exists between listing a catalog and fetching a specific URL.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern using snake_case (get_server_endpoint, list_servers). The verb choices ('get' vs 'list') accurately reflect the read operations and maintain predictable conventions.
Tool Count3/5With only 2 tools, the surface feels minimal/thin despite the server connecting to 18 different API proxies. While functional for pure discovery (list + get), the count is borderline for the implied richness of the domain.
Completeness4/5Covers the core read-only lifecycle for a directory service: discovery (list) and connection (get endpoint). Minor gap in filtering/search capabilities for the 18 servers, though listing all is acceptable at this scale.
Average 3.8/5 across 2 of 2 tools scored.
See the tool scores section below for per-tool breakdowns.
This repository includes a README.md file.
This repository includes a LICENSE file.
Latest release: v1.0.0
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.
This repository includes a glama.json configuration file.
- This server provides 2 tools. View schema
No known security issues or vulnerabilities reported.
This server has been verified by its author.
Add related servers to improve discoverability.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions what is retrieved (SSE endpoint URL) but omits error handling (e.g., invalid slug), side effects, caching behavior, or whether this operation incurs costs or rate limits.
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, efficient sentence that front-loads the action verb. There is no redundant or wasted text; every word contributes to understanding the tool's function.
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?
While adequate for a simple two-parameter lookup tool, the description lacks information about the return value format (critical given no output schema exists) and error conditions. It meets minimum viability but leaves operational gaps.
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?
With 100% schema description coverage, the schema already comprehensively documents both parameters (including examples for 'slug'). The description adds no additional parameter semantics beyond implying the 'slug' parameter via 'by slug', so it meets the baseline expectation.
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 ('Get') and resource ('SSE endpoint URL') and clearly scopes the operation to a specific server identified 'by slug'. This effectively distinguishes it from sibling tool 'list_servers' (which presumably enumerates servers rather than retrieving a specific endpoint).
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 no guidance on when to use this tool versus alternatives. It fails to mention that 'list_servers' should likely be used first to discover valid slugs, or under what circumstances an agent should call this endpoint.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully reveals critical ecosystem constraints: the servers are 'free hosted,' 'proxy a REST API,' and require users to 'bring your own API key.' However, it omits details about response format, caching, or rate limiting.
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 consists of two highly efficient sentences. The first front-loads the core deliverable (the 18 specific servers), while the second adds essential context about the REST proxy architecture and API key requirements. No words are wasted.
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 low complexity (zero parameters, no nested objects) and lack of output schema, the description adequately explains what the tool returns (inventory of 18 servers) and their operational model. It could be improved by hinting at the return structure (e.g., array of server objects), but it is sufficiently complete for agent selection.
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?
Per the evaluation rules, tools with zero parameters receive a baseline score of 4. The input schema is an empty object with no parameters to describe, and the description appropriately focuses on the return value rather than non-existent inputs.
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 'List[s] all 18 free hosted APIFold MCP servers' with specific examples (GitHub, Stripe, Slack, etc.). The plural 'servers' and enumeration of all 18 naturally distinguishes it from sibling 'get_server_endpoint' (singular), making the scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is a discovery/initialization tool ('List all'), but provides no explicit guidance on when to use this versus 'get_server_endpoint' or prerequisites for using the listed servers. The usage context is inferred from the verb 'List' but not stated explicitly.
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:
How to claim the server?
If you are the author of the server, you simply need to authenticate using GitHub.
However, if the MCP server belongs to an organization, you need to first add glama.json to the root of your repository.
{
"$schema": "https://glama.ai/mcp/schemas/server.json",
"maintainers": [
"your-github-username"
]
}Then, authenticate using GitHub.
Browse examples.
How to make a release?
A "release" on Glama is not the same as a GitHub release. To create a Glama release:
- Claim the server if you haven't already.
- Go to the Dockerfile admin page, configure the build spec, and click Deploy.
- Once the build test succeeds, click Make Release, enter a version, and publish.
This process allows Glama to run security checks on your server and enables users to deploy it.
How to add a LICENSE?
Please follow the instructions in the GitHub documentation.
Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear on the server after some time, you can manually trigger a new scan using the MCP server admin interface.
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 description 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.