enterprise-compliance
B2B: company-wide compliance audit across policies + 90d remediation. input=policies+framework. [x402: 75.0 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
B2B: company-wide compliance audit across policies + 90d remediation. input=policies+framework. [x402: 75.0 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?
With no annotations, the description carries the full burden of behavioral disclosure. It does mention scope, remediation horizon, and pay-per-use pricing, but it does not disclose what the tool returns, whether it performs writes or side effects, or what 'remediation' means in terms of execution.
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 a single compact sentence that front-loads the core purpose and scope. There is no filler, though the `[2: 75.0 USD on Base, pay-per-use]` pricing notation is cryptic and assumes domain familiarity.
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 paid, non-trivial audit/remediation tool with no output schema and no annotations, the description omits the return value, side effects, and how it differs from the similar `compliance-audit` sibling. The input hint and pricing are helpful, but an agent still cannot fully predict the tool's behavior or route to the right alternative.
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 schema describes `input` only as 'service input', which is uninformative. The description adds real meaning by specifying `input=policies+framework`, so an agent knows what content to provide. It still lacks format or encoding details, but it substantially improves on the schema.
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 states a clear action ('compliance audit'), resource ('polcies + framework'), scope ('company-wide'), and a time-bound outcome ('90d remediation'). It gives the tool an identifiable purpose, though it does not explicitly contrast itself with the sibling `compliance-audit`.
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 implies the intended use case: B2B, company-wide compliance audits with a 90-day remediation follow-up. However, it provides no exclusion criteria or alternative guidance, and the sibling `compliance-audit` is close enough that an agent could easily pick the wrong tool.
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.