Get current product prices with calculated price_per_kg. Returns EUR pricing for all in-stock products.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Tenant slug | |
| product_slug | No | Specific product slug (optional — omit for all) | |
| response_format | No | Response detail level: "concise" (default, key fields only) or "detailed" (all fields) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / response_formatAdded value: +{
+ "description": "Response detail level: \"concise\" (default, key fields only) or \"detailed\" (all fields)",
+ "enum": [
+ "detailed",
+ "concise"
+ ],
+ "type": "string"
+}Input schema / properties / slugAdded value: +{
+ "description": "Tenant slug",
+ "enum": [
+ "siltums",
+ "woodpoint"
+ ],
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "slug"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden and does disclose key behaviors: it returns only in-stock products, prices are in EUR, and price_per_kg is calculated. It does not explicitly state read-only behavior, but 'Get' strongly implies it, and no side effects are implied.
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, tight sentence with no filler. It front-loads the action and immediately specifies the output characteristics, every word earning its place.
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 simple read with no output schema, the description explains the return scope (all in-stock products), currency (EUR), and computed field (price_per_kg). It does not spell out the exact response structure or default response_format behavior, but the schema covers those; this is adequate.
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 description coverage is 100% for all three parameters (slug, product_slug, response_format), so the schema fully documents parameter meaning. The description adds no extra parameter-level detail, thus baseline 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 uses the specific verb 'Get' with resource 'current product prices', adding scope ('all in-stock products'), currency ('EUR'), and a calculated field ('price_per_kg'). This clearly differentiates it from siblings like search_products, which focuses on product search rather than price retrieval.
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 when to use the tool (when current product prices are needed) but provides no explicit when-not-to-use guidance or reference to alternatives. Sibling tools such as search_products could be relevant, yet no routing or exclusion is stated.
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.
Multiple signal tools (front_run_watch, prediction_signals, wave_signal) serve similar purposes and could be confused, though each has a distinct data source. Similarly, soul_insights and soul_stack_feed both present agent-generated content, creating some ambiguity.
All names use snake_case, but there's no consistent pattern: some are verb-first (get_prices, search_products), some are brand-prefixed nouns (soul_bounties, wave_portfolio), and mixed usage like daloopa_query and soul_verify. The inconsistency is readable but not predictable.
16 tools for a multi-source market data server is reasonable; there are no outright redundant tools, though a few (think, fusion_capabilities) feel auxiliary. The count is slightly above ideal but well within acceptable bounds.
The server provides extensive read-only browsing (soul_bounties, soul_insights) but lacks corresponding action tools like claiming a bounty or purchasing insights, creating dead ends. For a 'market', there are no execute/trade/buy operations, leaving significant gaps for agent workflows.