PAID (0.005 USDC). Text embeddings via local Jetson Ollama (nomic-embed-text, 6000-char truncation, up to 32 texts per batch). Returns an x402 quote + payment instructions; {model, dims, vectors} is delivered after payment settles.
| Name | Required | Description | Default |
|---|---|---|---|
| texts | Yes | List of strings to embed (max 32). |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden and does so excellently. It discloses the cost (0.005 USDC), the local execution environment, truncation at 6000 chars, max batch of 32, and the two-step flow (quote + payment, then result delivery). This goes well beyond a basic functional description and preempts questions about payment and data limits.
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 two sentences long and front-loaded with the most critical alert: 'PAID (0.005 USDC)'. Each clause carries unique information (model, limits, payment flow, output shape). No fluff or repetition. This is a model of efficient, structured documentation.
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?
Given the tool's complexity (payment-gated, batch processing, truncation), the description covers the essential operational flow: the agent receives a quote and payment instructions, then receives {model, dims, vectors} after payment. It does not explain the x402 payment protocol mechanics, but with an output schema present and the sibling context, this is a minor gap. Overall, the description is well-rounded for a paid API.
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% (the sole parameter 'texts' is described as 'List of strings to embed (max 32)'). The description adds value by specifying a 6000-character truncation limit per text, which is not in the schema. It also reinforces the max 32 with an additional usage note, and mentions output dimensions implicitly via 'dims'. This exceeds the baseline 3 for high schema 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 clearly states the tool's function: 'Text embeddings via local Jetson Ollama' with a specific model (nomic-embed-text). It also includes key limits and payment context, distinguishing it from all sibling tools which address different tasks (search, summarization, notarization, etc.). The verb 'embed' and the resource 'batch' are explicit.
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 provides clear context: this is a paid tool requiring x402 quote + payment before output delivery. It mentions batch size and truncation limits, which help an agent decide if input fits. While it does not explicitly name alternatives or state when not to use it, the distinct purpose and payment requirement make usage conditions clear. This aligns with 'clear context, no exclusions'.
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.