compliance-audit
PREMIUM: compliance/legal-risk audit by severity. input=document/policy. [x402: 20.0 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
PREMIUM: compliance/legal-risk audit by severity. input=document/policy. [x402: 20.0 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 provided, so the description carries the behavioral disclosure burden. It does disclose the significant cost trait: '20.0 USDC on Base, pay-per-use.' It also indicates output is organized 'by severity.' However, it does not say anything about data handling, input limits, or whether the operation is strictly read-only.
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 one compact line with no filler: it opens with the premium/cost signal, states the purpose, and names the input type. Every segment contributes information, and the cost warning is placed prominently.
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-parameter tool with no output schema, the description covers the essential operational facts: what to pass, what kind of result to expect, and that it is a paid call. It lacks an explicit return-format description and alternative routing, but the low complexity keeps this close to sufficient.
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's only parameter is described merely as 'service input,' which is uninformative. The description repairs this by saying 'input=document/policy,' giving the agent the actual semantic expectation. This adds meaningful guidance beyond the schema despite the 100% schema description coverage.
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 specific function: 'compliance/legal-risk audit by severity.' This adds the legal-risk scope and severity output on top of the tool name. It is not a pure tautology, though it does not explicitly distinguish itself from the sibling 'risk-analysis' tool.
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 one usage hint, 'input=document/policy,' but does not state when to prefer this tool over alternatives like risk-analysis, contract-draft, or fact-check. There is no when-to-use or when-not-to-use guidance, so an agent must guess which sibling covers which scenario.
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.