translateTranslate text between languages with the source language auto-detected. Returns the translation plus a confidence note for ambiguous terms. Price: 0.05 USDC per call (x402, Base). Resource: https://api.exo-trust.com/execute/translate. Returns the payment challenge unless already settled.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| source | No | ||
| target | No |
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?
Discloses pricing, the payment challenge behavior, and the outputs returned (translation plus confidence note). With no annotations, it carries the transparency burden and covers the main behavioral expectations without contradiction.
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 front-loaded with the core purpose and includes necessary operational details (price, resource, payment challenge). It is compact and does not contain significant filler.
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?
The description covers the main output and payment behavior, but with no output schema and no explanation of the 'target' parameter's default behavior or accepted language format, an agent may not be able to fully predict the result. Overall sufficient for a simple translation call but with notable gaps.
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?
Schema coverage is 0%, and the description only clarifies that the source is auto-detected. The required 'text' parameter is obvious, but the 'target' parameter is left ambiguous (e.g., language code vs. name, and behavior when null), so the semantics are 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?
Clearly states the action (translate text), the resource (Synergy translate), and the key behavior: source language auto-detection and returning translations with confidence notes. This distinguishes it from sibling tools such as rewrite or proofread.
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?
Provides clear context that this is for translation between languages with automatic source detection, and it mentions the confidence note for ambiguous terms. It does not explicitly enumerate when-not-to-use scenarios or alternative tools, but the use case is evident.
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.
Most tools have clearly distinct purposes, but edgar_financials and edgar_report overlap heavily, with the report apparently building on the same financial data. The three registry lookups (sanctions, VAT, LEI) are distinct, and the text-processing tools are separable.
Naming is inconsistent: some tools use bare verbs (classify, extract, proofread, rewrite, summarize, translate), some use verb_noun (check_sanctions, lookup_lei, validate_vat, code_explain), and others use domain-based names (clinical_dd, edgar_financials, edgar_report, synergy_discovery). No single consistent convention is applied.
14 tools is within the reasonable range and each covers a distinct specialist area, but the set feels slightly broad and includes a few near-duplicates (edgar_financials vs edgar_report). It is not excessive, though tightening could improve focus.
The catalog covers a wide range of domains: sanctions, VAT, LEI, SEC filings, clinical trials, code analysis, text transformation, and discovery. It includes a discovery tool to enumerate specialists, which helps. Minor gaps like missing update/delete-style operations are not expected for a read-only specialist API, so coverage is strong.