volthq-mcp-server

Server Quality Checklist

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

Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: price checking, savings analysis, spending retrieval, routing recommendations, and budget alerts. The descriptions specify unique functions, making misselection unlikely.
Naming Consistency5/5
All tool names follow a consistent 'volt_verb_noun' pattern (e.g., volt_check_price, volt_get_savings). This predictable structure enhances readability and usability for agents.
Tool Count5/5
With 5 tools, the server is well-scoped for cost optimization and monitoring in AI model routing. Each tool serves a specific, necessary function without bloat or redundancy.
Completeness4/5
The toolset covers core cost management workflows: monitoring (spend, savings), optimization (price comparison, routing), and alerts (budget). A minor gap exists in direct cost control actions (e.g., adjusting routes or budgets), but agents can work around this with recommendations and alerts.
Average 3.1/5 across 5 of 5 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.
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.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 states the tool retrieves a spending summary, implying a read-only operation, but doesn't address critical aspects like authentication requirements, rate limits, error handling, or the format of the returned summary. For a 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly conveys the core functionality without any wasted words. It is front-loaded with the main action and scope, making it easy to parse quickly. Every part of the sentence earns its place by specifying key details like the summary breakdown and time ranges.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a read operation with one parameter) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the spending summary includes (e.g., cost breakdowns, units), how results are structured, or any limitations (e.g., data latency, access controls). Without an output schema, the description should provide more context on return values, but it fails to do so.
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 description mentions the time range options ('today, 7 days, or 30 days'), which aligns with the input schema's parameter 'time_range' and its enum values. Since schema description coverage is 100%, the schema already fully documents the parameter, so the description adds minimal value beyond restating the enum options. This meets the baseline for high schema coverage.
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: 'Get spending summary by provider and model for today, 7 days, or 30 days.' It specifies the verb ('Get'), resource ('spending summary'), and scope ('by provider and model'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'volt_get_savings' or 'volt_check_price', which might also involve spending data.
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 mentions the time ranges but doesn't explain why one would choose this tool over siblings such as 'volt_get_savings' or 'volt_recommend_route', nor does it specify any prerequisites or exclusions. This lack of contextual guidance leaves the agent to infer usage scenarios.
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 mentions setting a threshold and alerts, but doesn't cover critical aspects like whether this is a one-time setup or recurring, what permissions are required, how alerts are delivered, or if changes are reversible. This is inadequate for a mutation tool.
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 extremely concise with two short sentences that are front-loaded and waste no words. Every part contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, error conditions, response format, and how it integrates with sibling tools. The high schema coverage helps, but doesn't compensate for the missing operational context.
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 thoroughly. The description adds minimal value by implying the parameters are used together for budget alerts, but doesn't provide additional context beyond what's in the schema descriptions.
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 ('Set') and resource ('budget threshold'), and specifies the alert functionality. However, it doesn't differentiate from sibling tools like 'volt_get_spend' or 'volt_check_price', which might be related to monitoring spending.
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?
No guidance is provided on when to use this tool versus alternatives. The description mentions alerts when exceeded, but doesn't specify prerequisites, exclusions, or how it relates to sibling tools for checking spend or savings.
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 the tool 'shows' savings, implying a read-only operation, but doesn't clarify if it requires specific permissions, has rate limits, or what the output format looks like (e.g., detailed report vs summary). For a tool with no annotations, this is a significant gap, as it lacks details on behavioral traits like data freshness or computational cost.
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 extremely concise and front-loaded: two sentences that directly state the tool's purpose and output. There is zero waste—every word contributes to understanding what the tool does. This efficient structure makes it easy for an AI agent to parse and use, earning a top score of 5.
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 (moderate, involving comparison and savings analysis), no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It explains the core function but lacks details on output format, error handling, or integration with siblings. Without annotations or output schema, more context on behavioral aspects would improve completeness, but it meets the basic threshold for a score of 3.
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 description adds no parameter semantics beyond what the input schema provides. The schema has 100% description coverage, clearly documenting the single parameter 'time_range' with its enum values and default. Since the description doesn't elaborate on parameter usage (e.g., how time_range affects the comparison), the baseline score of 3 is appropriate, as the schema adequately handles parameter documentation.
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: 'Compare actual spend against optimal routing' specifies the verb (compare) and resources (actual spend vs optimal routing). It distinguishes from siblings like 'volt_get_spend' (which likely shows raw spend) and 'volt_recommend_route' (which likely suggests routes). However, it doesn't explicitly differentiate from all siblings, such as 'volt_check_price' or 'volt_set_budget_alert', keeping it at a 4 rather than a 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 mentions 'Shows savings achieved and savings missed' but doesn't specify contexts, prerequisites, or exclusions. For example, it doesn't indicate if this is for post-analysis or real-time monitoring, or when to choose it over 'volt_get_spend' or 'volt_recommend_route'. This lack of explicit when/when-not/alternatives results in a score of 2.
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 mentions the tool 'Shows savings vs your current cost,' which implies a read-only comparison function, but lacks details on permissions, rate limits, data freshness, or what happens if no recommendations match criteria. This is inadequate for a recommendation tool with multiple parameters.
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, stating the core purpose in the first sentence and adding a secondary benefit in the second. Both sentences earn their place by clarifying the tool's function and output, with no wasted words or redundancy.
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 (6 parameters, no annotations, no output schema), the description is moderately complete. It covers the primary function and output type but lacks details on behavioral traits, error handling, and result format. Without annotations or output schema, more context on what the recommendation includes would be beneficial for an agent.
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 by hinting at optimization criteria and savings estimation, but does not provide additional syntax, format details, or usage examples for parameters. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 the optimal provider recommendation') and resources ('for a model'), specifying optimization criteria ('based on cost, latency, reliability, or balanced optimization'). It distinguishes from sibling tools like 'volt_check_price' by focusing on recommendations rather than price checks or savings calculations.
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 like 'volt_check_price' or 'volt_get_savings'. It mentions the tool's function but does not specify scenarios, prerequisites, or exclusions for usage, leaving the agent to infer context from the tool name alone.
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 mentions that the tool returns sorted offerings with quality and reliability data, which adds some context about output behavior. However, it lacks details on critical aspects such as whether this is a read-only operation, potential rate limits, authentication needs, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its operational traits.
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 a single, efficient sentence that front-loads the core purpose ('Compare pricing across providers for a given model') and immediately follows with the return value. There is no wasted wording, repetition, or unnecessary elaboration, making it highly concise and well-structured for quick understanding.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is partially complete. It clearly states the purpose and output format, but lacks behavioral details (e.g., safety, limits) and usage guidelines. Without an output schema, it does explain the return structure, which helps, but overall it falls short of being fully comprehensive for an agent to use the tool confidently in all contexts.
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%, meaning the input schema fully documents both parameters (model and max_results). The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain the semantics of 'model' or 'max_results' further). According to the rules, with high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't need to given the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 ('compare pricing') and resource ('across providers for a given model'), and distinguishes it from siblings by focusing on price comparison rather than savings analysis, spending tracking, recommendations, or alerts. It explicitly mentions what the tool returns ('offerings sorted by price with quality and reliability data'), making the purpose distinct and well-defined.
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 its siblings (e.g., volt_get_savings, volt_recommend_route). It does not mention any prerequisites, exclusions, or alternative tools, leaving the agent to infer usage based on purpose alone. While the purpose is clear, the lack of explicit context or comparisons reduces its helpfulness for tool selection.
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:

[![volthq MCP server](https://glama.ai/mcp/servers/newageflyfish-max/volthq/badges/card.svg)](https://glama.ai/mcp/servers/newageflyfish-max/volthq)

Score Badge

Copy to your README.md:

[![volthq MCP server](https://glama.ai/mcp/servers/newageflyfish-max/volthq/badges/score.svg)](https://glama.ai/mcp/servers/newageflyfish-max/volthq)

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/newageflyfish-max/volthq'

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