VERTICAL(saas): churn diagnosis + retention levers. input=churn data. B2B: CS teams prioritize retention actions. [x402: 15.0 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses the pay-per-use cost and the general output type ('diagnosis + retention levers'), but it does not explain output format, how input should be structured, whether data is modified, or any limitations. This is thin behavioral coverage for an unannotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely compact and well structured, with the domain, purpose, input, audience, and pricing each clearly labeled. Every component earns its place and the key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-input tool with no output schema and no annotations, the description leaves too much unspecified: the expected input format, the concrete output structure, and how an agent should interpret 'retention levers' are all missing. An agent may know what domain it addresses but not how to invoke it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter is merely described as 'service input' in the schema, but the tool description adds the meaningful constraint that it expects churn data. However, it does not specify whether the input should be raw metrics, a CSV, a text summary, or a URL, so the added semantics are helpful but incomplete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the domain (SaaS), the function (churn diagnosis + retention levers), and the required input (churn data). It distinguishes this from the many market-report and pricing siblings by focusing on churn-specific diagnosis and retention prioritization, though it lacks a direct imperative verb like 'analyze' or 'diagnose'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear intended use context: B2B customer success teams use this to prioritize retention actions. It does not explicitly state when not to use it or name alternatives, but the vertical and audience targeting are enough to guide selection among the broad sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.