PromptSmith

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap. generate_meta_prompt creates prompts, get_usage_stats retrieves statistics, list_available_frameworks enumerates options, log_execution_feedback captures performance data, and recommend_strategy provides analysis. An agent can easily distinguish between these five distinct functions.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case throughout. The naming is predictable and readable: generate_meta_prompt, get_usage_stats, list_available_frameworks, log_execution_feedback, and recommend_strategy. There are no deviations in style or convention.
Tool Count5/5
With 5 tools, this server is well-scoped for its purpose of prompt generation and management. Each tool earns its place by covering distinct aspects: generation, statistics, listing, feedback, and strategy recommendation. This count is neither too thin nor too heavy for the domain.
Completeness4/5
The tool surface covers the core lifecycle of prompt generation and optimization well, including creation (generate_meta_prompt), analysis (recommend_strategy), monitoring (get_usage_stats, log_execution_feedback), and discovery (list_available_frameworks). A minor gap is the lack of tools for editing or deleting prompts, but agents can work around this by regenerating prompts as needed.
Average 3.3/5 across 5 of 5 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.
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.json to 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 analyzing tasks and generating structured prompts, but lacks details on permissions, rate limits, side effects, or what 'optimized' entails. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured in two sentences. The first sentence states the core purpose, and the second elaborates on the process. There's no wasted text, though it could be slightly more front-loaded with key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (generating prompts with frameworks) and the presence of an output schema, the description is moderately complete. It covers the purpose but lacks behavioral details and usage guidelines. With no annotations and incomplete contextual guidance, it's adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema, mentioning 'task' and 'framework' implicitly but not explaining their semantics further. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate an optimized meta-prompt for the given task.' It specifies the verb ('generate') and resource ('optimized meta-prompt'), and mentions analyzing tasks and selecting reasoning frameworks. However, it doesn't explicitly differentiate from sibling tools like 'recommend_strategy' which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does 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 doesn't mention sibling tools like 'list_available_frameworks' or 'recommend_strategy', nor does it specify prerequisites or contexts where this tool is preferred. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 states what data is returned but doesn't cover important aspects like whether this is a read-only operation, if it requires authentication, rate limits, freshness of data, or error conditions. For a statistics tool with zero annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that each add value: stating the tool's purpose, listing key statistics categories, and specifying metrics. It's front-loaded with the core purpose and avoids unnecessary elaboration. Minor deduction for slightly repetitive phrasing ('distribution by framework and category' could be more streamlined).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has no parameters, has an output schema (so return values are documented elsewhere), and provides basic statistics, the description is minimally complete. However, without annotations and with sibling tools that might overlap (like log_execution_feedback), more context about when this tool is appropriate would improve completeness for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the input requirements. The description appropriately doesn't repeat parameter information, maintaining a baseline of 4 for parameterless tools that don't waste space on nonexistent parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does 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 ('Get usage statistics') and resources ('for PromptCore'), and lists the types of statistics provided (total prompts, distribution by framework/category, average complexity). It doesn't explicitly differentiate from sibling tools, but since no siblings appear to provide similar statistics, this is adequate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does 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 doesn't mention prerequisites, frequency of use, or how it relates to sibling tools like log_execution_feedback or recommend_strategy. The agent must infer usage context solely from the purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool returns a category, complexity_score, recommended framework, and alternatives, but doesn't cover critical aspects like whether this is a read-only analysis (implied but not stated), computational cost, rate limits, or error conditions. This leaves significant gaps for a tool that performs analysis.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with the core purpose stated first. The second sentence efficiently lists return values. However, it could be slightly more structured by explicitly separating purpose from output, and it includes a minor redundancy ('optimal' and 'recommended' overlap), preventing a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (analysis with two parameters), no annotations, and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the purpose and output at a high level, but lacks behavioral details like error handling or performance characteristics, which holds it back from a score of 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema fully documents the two parameters ('task' and 'context'). The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain what constitutes a 'task' or how 'context' influences the analysis), resulting in the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze a task and recommend the optimal reasoning framework.' It specifies the verb ('analyze' and 'recommend') and resource ('task' and 'reasoning framework'), but doesn't explicitly differentiate it from sibling tools like 'list_available_frameworks' or 'generate_meta_prompt', which prevents a 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 Guidelines2/5
Does 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 doesn't mention sibling tools like 'list_available_frameworks' (which might list frameworks without analysis) or 'generate_meta_prompt' (which might use a framework), leaving the agent with no context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the tool logs feedback for tracking and improvement, but doesn't disclose behavioral traits like whether it's idempotent, requires specific permissions, has rate limits, or what happens on failure. For a logging tool with zero annotation coverage, this is a significant gap 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences. The first sentence states the purpose, and the second provides usage context. There's no wasted text, but it could be slightly more front-loaded with key details like the connection to generate_meta_prompt.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (logging with two parameters), 100% schema coverage, and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose and high-level usage, but lacks behavioral details that annotations would normally provide, such as idempotency or error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (task_id and feedback) with descriptions. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Log feedback about how a generated prompt performed' specifies the verb (log) and resource (feedback). It distinguishes from siblings like generate_meta_prompt (creates prompts) and get_usage_stats (retrieves statistics). However, it doesn't explicitly differentiate from all siblings (e.g., recommend_strategy could involve feedback).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: 'Used to track effectiveness and improve framework selection over time' suggests it's for post-generation evaluation. It references generate_meta_prompt via the task_id parameter, but doesn't explicitly state when to use this tool versus alternatives like get_usage_stats or recommend_strategy for tracking purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the tool as a list operation, implying it's read-only and non-destructive, which is helpful. However, it lacks details on behavioral traits like rate limits, authentication needs, pagination, or response format. The description adds basic context but doesn't fully compensate for the absence of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured. There's no wasted text, and it's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, no annotations, and an output schema exists (which handles return values), the description is reasonably complete. It covers the purpose and usage context adequately. However, it could be more comprehensive by addressing potential behavioral aspects like error handling or data freshness, but the output schema reduces the need for extensive detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. A baseline of 4 is applied for tools with no parameters, as there's nothing to compensate for, and the description doesn't introduce confusion.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('List') and resource ('all available reasoning frameworks with their descriptions'). It distinguishes itself from siblings like 'generate_meta_prompt' or 'recommend_strategy' by focusing on enumeration rather than generation or recommendation. However, it doesn't explicitly contrast with 'get_usage_stats' or 'log_execution_feedback', which might also involve listing data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: 'Use this to understand what frameworks are available and when each is best used.' This gives a specific scenario for when to use the tool. However, it doesn't explicitly state when NOT to use it or name alternatives among the sibling tools, such as using 'recommend_strategy' for selection instead of just listing.
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:

[![PromptCore MCP server](https://glama.ai/mcp/servers/BlinkVoid/PromptCore/badges/card.svg)](https://glama.ai/mcp/servers/BlinkVoid/PromptCore)

Score Badge

Copy to your README.md:

[![PromptCore MCP server](https://glama.ai/mcp/servers/BlinkVoid/PromptCore/badges/score.svg)](https://glama.ai/mcp/servers/BlinkVoid/PromptCore)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/BlinkVoid/PromptCore'

If you have feedback or need assistance with the MCP directory API, please join our Discord server