A
licenseB
qualityC
maintenanceAgentic pipeline that transforms ideas to revenue — for solo founders and bootstrappers.
2
45
16
4
MIT
Unlock SaaS
Server Details
Playbook engine: non-dev founders land first paying customer in 60 days or refund
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A3.7/5.0
Tool DescriptionsB
Average 3.5/5 across 3 of 3 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool targets a distinct SaaS metric: churn cost, LTV, and revenue projection. There is no overlap or ambiguity in their purposes.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case (get_churn_cost, get_ltv, project_revenue), making them predictable and readable.
Tool Count4/5
With 3 tools, this is within the well-scoped range for a focused SaaS metrics server, though it is on the lower end, leaving room for expansion.
Completeness3/5
The server covers core churn, LTV, and revenue projection, but lacks tools for computing churn rate, customer acquisition cost, or cohort analysis, which are notable gaps for a comprehensive SaaS toolkit.
Available Tools
3 toolsget_churn_costAInspect
Calculate the monthly revenue cost of SaaS churn from MRR, churn rate, and customer count. Returns the dollar value leaking out each month.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| mrr | Yes | Monthly recurring revenue (USD). | |
| customers | No | Active customer count (optional, for per-customer view). | |
| churn_rate | Yes | Monthly churn rate as a decimal (0.05 = 5%). |
Tool Definition Quality
A4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations present, but description fully discloses that the tool performs a calculation and returns a dollar value. No hidden behaviors or 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no redundant information. Front-loaded with action and result.
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?
For a simple calculator with no output schema, the description sufficiently explains return value. Could specify unit or formula, but adequate.
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 100%, so description adds minimal value beyond schema. It mentions grouping of parameters but does not provide additional semantics.
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?
Description clearly states the tool calculates monthly revenue cost of SaaS churn using MRR, churn rate, and customer count. It specifies output as dollar value, distinguishing it from siblings like get_ltv and project_revenue.
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?
Description implies usage when MRR and churn rate are known, but does not provide explicit when-to-use vs alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_ltvBInspect
Calculate customer lifetime value (LTV) for a SaaS business from ARPU, gross margin, and monthly churn. Uses the canonical David Skok formula.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| arpu | Yes | Average revenue per user per month (USD). | |
| gross_margin | Yes | Gross margin as a decimal (0.85 = 85%). | |
| monthly_churn | Yes | Monthly churn rate as a decimal (0.05 = 5%). |
Tool Definition Quality
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose behavioral traits such as whether it is read-only or destructive, nor any side effects or required permissions.
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?
Single concise sentence front-loading the purpose and formula, but no additional structured sections or examples.
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?
Tool is simple with few parameters; description is adequate but lacks output schema context and fails to explain return values or usage constraints.
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 covers all 3 parameters with descriptions; description adds no new meaning beyond listing input names. Baseline score appropriate given high 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 it calculates customer lifetime value (LTV) for SaaS, specifying inputs (ARPU, gross margin, monthly churn) and a known formula, distinguishing it from siblings like get_churn_cost and project_revenue.
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. It mentions 'for a SaaS business' but provides no exclusions or context about when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
project_revenueBInspect
Project SaaS revenue 12 months forward from current MRR and a monthly growth assumption. Returns month-by-month MRR.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| months | No | Projection horizon in months (default 12). | |
| current_mrr | Yes | Starting monthly recurring revenue (USD). | |
| monthly_growth | Yes | Net monthly growth rate as a decimal (0.08 = 8%/month). |
Tool Definition Quality
B3.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It accurately states the projection direction and output but omits important details like modeling assumptions (constant growth), handling of negative growth, or edge cases.
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 one sentence of 16 words, very concise. However, it could be slightly clearer with better structure, e.g., specifying the default horizon explicitly.
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 there is no output schema, the description should more fully describe the return format. It only says 'month-by-month MRR' without specifying if it's an array or object. The complexity is moderate, so more completeness is warranted.
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 already fully describes the three parameters (100% coverage). The description mentions current_mrr and monthly_growth but adds no new semantic information beyond what the schema provides.
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 it projects SaaS revenue forward from current MRR and a growth assumption, and returns monthly MRR. This distinguishes it from sibling tools get_churn_cost and get_ltv, which deal with different metrics.
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 does not provide any guidance on when to use this tool versus alternatives. No prerequisites, limitations, or context for when it is appropriate are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- FlicenseAqualityDmaintenanceDomain-expert SMB sales playbooks for AI agents. Discovery questions, objection handlers, cold email + LinkedIn DM templates, BANT/MEDDIC frameworks, closing tactics. Built by an ex-Criteo (268% quota) / ex-Deel ($12B) / ex-HBO / ex-Bloomberg enterprise AE. Use when your AI SDR needs real human-tested sales artifacts.10
- AlicenseAqualityCmaintenanceBuyer intelligence for technical founders who sell to enterprises — ICP scoring, persona simulation, competitive positioning, deal classification, and 14 more revenue intelligence tools. 50 free queries/month.23402MIT
- FlicenseBqualityCmaintenanceEnables agents to purchase verified growth outcomes such as qualified leads, sales meetings, and SEO content, with payment only for accepted results.9