PAID (0.05 USDC). Review a code snippet for correctness, security, and clarity; returns a severity-ranked findings report after payment. Returns an x402 quote.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The source code to review. | |
| language | No | Programming language (or 'auto' to detect). | auto |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 cost, the payment requirement ('after payment'), the nature of the output ('severity-ranked findings report'), and the return of an x402 quote, effectively conveying the multi-step payment flow.
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 three sentences, each with a distinct purpose: cost, action/output, and protocol quote. It is front-loaded with the payment warning and contains no 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?
With an output schema available, the description covers the essential flow: payment, review, and report. It also adds the important detail that findings are severity-ranked, which is valuable for agent expectation-setting. The description is sufficient for the tool's complexity.
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 100% for both parameters, so the description doesn't need to elaborate on parameter syntax. It adds context about review focus ('correctness, security, and clarity') but doesn't clarify parameter-specific details beyond the schema. The baseline of 3 applies.
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 states the action ('Review a code snippet') with specific aspects ('correctness, security, and clarity'), which distinguishes it from sibling tools like fact_verify or summarize. The verb-resource pairing is unambiguous and directly aligned with the tool name.
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 usage for code review when payment is acceptable, with explicit cost ('PAID (0.05 USDC)') and the expected outcome. It lacks explicit alternatives or exclusions, but the context is clear enough that agents can decide when to use it.
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, with memory-specific tools like mnemos_store, mnemos_search, and mnemos_get_context differentiated by their descriptions. The main ambiguity is between mnemos_search and mnemos_get_context, which both retrieve memories but with different output formats, and summarize_demo is a deliberate clone of summarize though clearly marked as a demo.
Naming conventions are inconsistent: some tools are prefixed with mnemos_ (mnemos_as_of, mnemos_store) while others use generic verb_noun names (code_review, fact_verify, notarize, summarize). This mix of styles makes it harder to predict functionality from tool names alone.
15 tools sits at the upper boundary of the typical range, but the server is advertised as memory-as-a-service while also offering many unrelated paid services (code review, fact verification, research briefs, summarization, trust checks, embeddings). This breadth makes the tool set feel overweighted and unfocused relative to the server's stated purpose.
The memory domain is well covered: provisioning, storing, searching, context retrieval, and bi-temporal reads are all present, with no obvious dead ends. The non-memory tools are single-purpose and do not require additional CRUD operations, so no critical gaps are apparent for their individual tasks.