Server Quality Checklist
- Disambiguation5/5
Each tool has a clearly distinct purpose: check (system capabilities), info (model metadata), recommend (advice), quantize (compression), evaluate (quality testing), and push (distribution). No overlapping functionality or ambiguous boundaries between tools.
Naming Consistency4/5All tools use lowercase single-word commands following a CLI-style convention. While most are verbs (check, evaluate, push, quantize, recommend), 'info' stands out as a noun, representing a minor deviation in an otherwise consistent imperative naming scheme.
Tool Count5/5Six tools perfectly cover the quantization workflow lifecycle: system validation, model inspection, recommendations, execution, quality verification, and publishing. The count is well-scoped with no redundant tools and no obvious missing steps for the core domain.
Completeness4/5Covers the full quantization pipeline from system checks through publishing. Minor gaps include lack of cleanup/delete tools for quantized models and no explicit format conversion between quantization types (GGUF↔GPTQ), but these don't block core workflows.
Average 4.3/5 across 6 of 6 tools scored. Lowest: 3.6/5.
See the tool scores section below for per-tool breakdowns.
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Latest release: v1.0.0
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Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the quality assessment categories (EXCELLENT/GOOD/FAIR/DEGRADED/POOR) and that it returns evaluation metadata, but does not explicitly state whether this is a read-only operation, if it modifies the model, or performance characteristics like evaluation duration.
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 well-structured with a clear summary sentence followed by explanatory context, then organized Args and Returns sections. Every sentence adds value—either explaining perplexity concepts or documenting parameters. It appropriately uses docstring formatting for clarity.
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 has 3 simple parameters (no nested objects) and an output schema exists, the description provides sufficient context. It summarizes the return values (perplexity score, quality assessment, metadata) without needing to replicate the full output schema, making it complete for the complexity level.
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?
Schema description coverage is 0%, so the description must compensate. The Args section successfully documents all three parameters: model_path specifies expected file types (GGUF) or directories (GPTQ/AWQ), format lists valid enum values ('gguf', 'gptq', 'awq'), and bits provides context ('for quality context'). This significantly aids agent invocation.
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 runs 'perplexity evaluation on a quantized model' and measures 'model quality after quantization using perplexity scoring.' It specifies the verb (run/evaluate) and resource (quantized model/perplexity), though it does not explicitly differentiate from siblings like 'check' or 'quantize'.
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 usage context (use after quantization to measure quality) and explains perplexity interpretation ('Lower perplexity = better quality'), but lacks explicit guidance on when to use this versus 'check', 'recommend', or other siblings, and mentions no prerequisites or exclusions.
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. It successfully discloses what information is gathered (specific backend engines, GPU type, RAM), notes the zero-argument nature, and characterizes the operation as 'lightweight', implying minimal resource impact. However, it stops short of explicitly stating idempotency or read-only safety.
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 well-structured with clear information hierarchy: purpose statement first, followed by detailed reporting scope, usage constraints, and return type. Every sentence provides necessary context without redundancy.
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 low complexity (no parameters) and presence of an output schema, the description adequately covers the return value shape (Dictionary) and inspection scope. It could be improved by explicitly stating safety properties (non-destructive) given the lack of annotations, but remains functionally complete.
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?
With zero parameters and 100% schema coverage, the baseline is 4. The description adds value by explicitly confirming 'No arguments required', reinforcing the schema structure for the agent.
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 opens with a specific verb ('Check') and clearly identifies the resource (quantization backends, PyTorch, GPU, RAM). It effectively distinguishes from action-oriented siblings like 'quantize', 'push', and 'evaluate' by focusing on system inspection rather than model manipulation.
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 notes 'No arguments required' and 'Lightweight system check', which implies usage context (quick diagnostic vs heavy operations), but lacks explicit guidance on when to use this versus siblings like 'info' or as a prerequisite to 'quantize'.
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. It successfully discloses key behavioral traits: external API dependency (HuggingFace API), resource requirements (no GPU/lightweight), and return value structure. Minor gap: doesn't mention authentication requirements 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, behavioral notes, args, returns) and front-loaded with the core action. Every sentence adds value. Minor deduction for using formal 'Args:' and 'Returns:' docstring headers which are slightly less conversational than optimal.
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 (1 parameter) and existence of an output schema, the description is appropriately complete. It proactively enumerates the specific metadata fields returned (architecture, parameter count, etc.), adding value beyond the schema. No critical gaps for this complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates. It provides the parameter name, format specification ('HuggingFace model ID'), a concrete working example ('meta-llama/Llama-3.1-8B-Instruct'), and alternative input format ('local path'), giving complete semantic context for the single parameter.
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 opens with a specific verb-resource combination ('Get model info from HuggingFace') and enumerates scope ('parameters, size, architecture'). It clearly distinguishes this metadata retrieval tool from action-oriented siblings like 'push', 'quantize', and 'evaluate'.
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 provides implied usage context ('Lightweight call...No GPU or heavy dependencies required'), indicating when this is appropriate (quick metadata checks). However, it lacks explicit when-not guidance or named alternatives among siblings like 'check' or 'evaluate'.
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. It effectively discloses behavioral traits by explaining the analysis inputs (model size, GPU VRAM, Apple Silicon, system RAM) and output structure (ranked recommendations with reasoning). However, it does not explicitly state whether the tool is read-only or requires network access.
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 uses a standard docstring format with clear sections (summary, analysis logic, Args, Returns). Every sentence provides unique value: the first states purpose, the second explains methodology, and the sections document the parameter and return value without redundancy.
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 has only one parameter (which is well-documented) and an output schema exists (relieving the description of detailed return documentation), the description is contextually complete. It appropriately explains the hardware analysis methodology that drives the recommendations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage (only title and type). The description fully compensates by documenting the 'model' parameter with both semantic meaning (HuggingFace model ID or local path) and concrete syntax examples ('meta-llama/Llama-3.1-8B-Instruct').
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 opens with a specific verb ('Recommend') and clearly identifies the resource (quantization format/bit width). It distinguishes itself from the sibling 'quantize' tool by focusing on analysis and suggestion rather than execution.
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?
While the description implies this is a planning/analysis tool ('suggest the optimal format'), it lacks explicit guidance on when to use this versus siblings like 'check' or 'quantize'. No explicit 'call this before quantizing' guidance is provided.
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 full burden and successfully discloses auth requirements, side effects (generates model card/README.md), and return structure ('Upload result with repository URL'). Missing minor behavioral details like idempotency or overwrite behavior.
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?
Perfectly structured with front-loaded purpose, followed by mechanism, prerequisites, Args, and Returns. Every sentence provides unique value; no repetition of schema structure or tautology.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Comprehensive for a 4-parameter tool with zero schema coverage. The description fully documents all parameters, explains authentication, describes side effects (model card generation), and acknowledges the return value without needing to replicate the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the Args section compensates excellently by documenting all 4 parameters with precise semantics and an illustrative example for repo_id ('username/model-GGUF-4bit'), including optionality notes for 'model'.
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 opens with a specific verb ('Push') and clear resource ('quantized model to HuggingFace Hub'), immediately distinguishing it from the 'quantize' sibling (which creates the model) by specifying this tool handles the upload of already-quantized files.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear contextual prerequisites ('Requires HuggingFace authentication') and implies workflow position ('output directory' suggests use after quantization). However, it lacks explicit comparison to siblings (e.g., 'use this after quantize') or explicit 'when not to use' guidance.
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 warns about resource intensity ('heavy operation'), network activity ('downloads'), side effects ('write output files'), and prerequisites ('backend dependencies'). It misses explicit mention of disk space requirements or error handling when dependencies are missing.
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 well-structured with clear sections (operation summary, behavioral warning, Args, Returns). Information is front-loaded with the core purpose. Every sentence earns its place: the 'heavy operation' warning prevents misuse, dependency note sets expectations, and Args section compensates for schema deficiencies without verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the brief Returns summary is sufficient. The description adequately covers all 5 parameters (including enum constraints and defaults), explains the heavy computational nature of quantization, documents target-specific format constraints, and warns about dependencies. Complete for a model compression tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the Args section provides essential compensation: 'model' includes an example ID and local path option, 'target' explains the forcing logic for different deployment platforms, and all parameters include default value documentation. This adds significant semantic value beyond the raw schema.
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 opening sentence 'Quantize a HuggingFace model to GGUF, GPTQ, or AWQ format' provides a specific verb (quantize), clear resource (HuggingFace model), and distinct output formats. This clearly differentiates from siblings like 'check', 'evaluate', and 'push' which imply inspection or distribution rather than compression.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear operational context ('heavy operation', 'requires appropriate backend dependencies') and crucial target deployment constraints ('ollama/llamacpp/lmstudio force GGUF, vllm forces AWQ') that guide format selection. However, it lacks explicit 'when not to use' guidance or comparison to sibling alternatives like 'evaluate' or 'info'.
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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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.
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- 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.
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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 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.