Revenium MCP Server

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by revenium

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

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Latest release: v0.2.11
Disambiguation5/5
Each tool targets a distinct area: cost analytics, alerts, metering, Slack integration, diagnostics, setup, and introspection. There is no functional overlap despite some shared themes like anomaly detection in both analytics and alerts.
Naming Consistency3/5
Tool names use snake_case but mix patterns: some start with 'manage_' (verb_noun) while others are noun_noun (e.g., 'slack_management', 'system_diagnostics'). This inconsistency could confuse an agent about naming conventions.
Tool Count5/5
With 7 tools, the server covers the essential domains (costs, alerts, metering, Slack, system, introspection) without bloat. Each tool earns its place, and the count is well-scoped for the stated purpose.
Completeness4/5
The tool surface is comprehensive for AI spending management, including cost analytics, alerting, metering submission/system, Slack integration, and system upkeep. Minor gaps (e.g., user/role management) exist but do not hinder core workflows.
Average 2.6/5 across 7 of 7 tools scored. Lowest: 1.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior but fails to do so. It does not indicate whether the tool is read-only, has side effects, or requires permissions. The default action 'get_capabilities' suggests querying, but this is not stated, potentially misleading an agent about side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, but it omits essential details. Conciseness is not beneficial when it leads to under-specification; the description should be longer to include parameter and behavioral context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, an output schema, and no annotations, the description is severely incomplete. It does not explain what actions are possible, how to use filters, or what the output contains, leaving the agent unable to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any of the three parameters ('action', 'tool_name', 'tool_type'). The description adds no meaning beyond the schema, leaving parameter semantics completely unspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool provides 'detailed description of each tool's function', which conveys a vague purpose of introspection. It does not specify the verb (e.g., 'retrieve', 'describe') or clearly distinguish from sibling tools like 'system_diagnostics', making the purpose ambiguous.
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 on when to use this tool versus alternatives. The description lacks context about situations where introspection is needed, and no comparison with sibling tools is provided, leaving the agent without decision-making support.
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 exist, so the description must disclose behavioral traits. It mentions combining configuration, OAuth, and setup, but does not specify side effects, permissions, or whether actions are read-only or destructive. The agent lacks crucial behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (two sentences) and lists actions, but lacks front-loading of critical information. It is not verbose, but the structure could be improved by highlighting the action parameter's role first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no output schema, no parameter descriptions), the description is severely incomplete. It fails to explain how actions map to parameters, return values, or expected behavior, leaving the agent with insufficient information to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to any of the 7 parameters. Parameters like config_id, return_to, dry_run, and skip_prompts are completely unexplained, forcing the agent to guess.
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 states it is a 'Unified Slack integration management' and lists key actions like list_configurations, OAuth, and setup. This gives a clear purpose, though it does not strongly differentiate from sibling tools like system_setup or tool_introspection.
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 only usage guidance is 'Use get_capabilities() for complete action list', which implies a starting point. No explicit when-to-use, when-not-to-use, or comparison with alternatives is provided.
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 must disclose behavioral traits but only mentions 'diagnostics' without stating whether the tool is read-only, destructive, or has side effects. No mention of authentication requirements, rate limits, or data handling.
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, using two sentences to convey purpose and a pointer to more details. However, it could be better structured by grouping related information (e.g., separating purpose from parameter guidance).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 13 parameters, no output schema, and no annotations, the description is severely incomplete. It fails to explain return values, parameter usage, or any constraints, making it nearly impossible for an AI agent to invoke correctly without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any of the 13 parameters. Keywords like environment_variables are listed but not linked to the input schema, leaving the AI agent with no understanding of how to set parameters like action, format_output, or search_term.
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 this tool is for unified system diagnostics, combining configuration analysis, auto-discovery debugging, and log analysis. It lists key actions and points to get_capabilities for a complete list, but doesn't specify the output format or what the tool returns, preventing a perfect score.
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 explicit guidance on when to use this tool versus siblings like business_analytics_management or slack_management. The only hint is 'Use get_capabilities() for complete action list,' but no context on prerequisites or scenarios where this tool is appropriate.
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 provided, and the description does not disclose behavioral traits such as side effects, authentication requirements, rate limits, or data modification potential. The phrase 'enhanced statistical anomaly detection' hints at analysis but lacks detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise but includes a long list of actions that could be streamlined. It front-loads the general purpose but adds redundant specifics, making it longer than necessary.
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?
Without annotations or output schema, and with 15 parameters, the description fails to provide a complete picture. It does not explain what actions do, how parameters affect results, or return values, leaving gaps for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains a few parameters (min_impact_threshold, include_dimensions, detect_new_entities, min_new_entity_threshold) but leaves most of the 15 parameters unexplained, providing insufficient guidance for correct invocation.
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 states 'Business analytics and cost analysis with enhanced statistical anomaly detection and new entity detection,' giving a clear general purpose. However, it does not differentiate this tool from sibling tools like manage_alerts or manage_metering, reducing clarity.
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 explicit guidance on when to use this tool versus its siblings. The description only refers to internal sub-actions (get_examples, get_capabilities) without contextualizing when the tool is appropriate.
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 exist, and the description fails to disclose behaviors like idempotency, error handling, or side effects. It mentions integration guides but not 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise but includes a potential inconsistency (python/typescript vs. Python/JavaScript) and redundant enumeration of actions.
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?
With 35 undocumented parameters, no output schema, and no annotations, the description is insufficient for an agent to invoke the tool correctly. It relies on get_capabilities() for full guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description provides no explanation of any of the 35 parameters. It only mentions high-level actions, leaving parameter usage completely unspecified.
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 it submits and looks up AI transaction metering metadata, listing key actions. It distinguishes from siblings by focusing on AI metering, but the name 'manage_metering' is vague.
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?
It provides some guidance for specific actions (e.g., lookup_transactions requires IDs, lookup_recent_transactions browses without), but lacks when-not-to-use or alternatives relative to sibling tools.
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 must fully disclose behavior. It lists actions like show_welcome and validate_email, but does not mention side effects, permissions, or state changes (e.g., whether email validation is persistent). The behavior is partially inferred but not transparent.
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 with two sentences, no fluff, and front-loads the purpose. However, it could be slightly more structured to separate parameter guidance, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high complexity (9 optional parameters, no enums, no output schema, no annotations), the description is severely lacking. It does not explain parameter usage, return values, or interaction effects, making it insufficient for correct tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 9 parameters with no descriptions (0% coverage). The description only mentions the 'action' parameter implicitly through key action names, but ignores the other 8 parameters entirely. It fails to add meaning to most parameters, leaving the agent guessing.
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 'Unified system setup and onboarding' and lists key actions, making the tool's purpose unambiguous. It is distinct from sibling tools like manage_alerts or system_diagnostics, which serve different functions.
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 use for setup and onboarding, but does not explicitly state when to use or when not to use. It references get_capabilities() for a complete action list, offering partial guidance, but lacks clear alternatives or exclusion criteria.
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 must carry the full burden. It focuses on creation patterns but does not disclose side effects, permissions, rate limits, or other behavioral traits. The tool likely supports CRUD operations, but the description only mentions creation.
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 with three sentences, front-loading the purpose and using numbered lists for clarity. No wasted words, though the reference to get_examples() is a minor inefficiency.
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 (30 parameters, no output schema, no annotations), the description is incomplete. It only covers creation aspects, ignoring other likely actions like listing, updating, or deleting alerts. Many parameters remain unexplained.
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 coverage is 0%, so description must compensate. It explains key parameters like action values (create_threshold_alert, etc.), alertType, triggerAfterPersistsDuration. However, many parameters (e.g., filters, page, size, email) are not addressed, leaving gaps.
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 it manages AI spending alerts and anomaly monitoring, listing three alert types with specific creation methods. However, it does not differentiate from sibling tools like business_analytics_management or manage_metering.
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 provides guidance on when to use each alert type (spike detection, budget threshold, relative change) and references get_examples() for more help. It does not mention when not to use this tool or compare with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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