api_serp_yandexYandex SERP for RU-market keyword research (region by ?lr=). ?q=&lr=213 [HTTP x402 price: $0.01]
| Name | Required | Description | Default |
|---|---|---|---|
| params | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / params / additionalPropertiesRemoved value: -trueInput schema / properties / params / propertiesAdded value: +{
+ "lr": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ },
+ "q": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ }
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full behavioral burden. It discloses the region parameter via ?lr= and mentions pricing/cost, but it does not state whether results are live, what result types are returned, whether auth is needed, or that this is a read-only operation. This is a meaningful transparency gap for a SERP tool.
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 compact and front-loaded with the core purpose. It includes a useful example and cost hint without wasted words, though the 'HTTP x402 price' phrasing is somewhat cryptic.
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 tool has an output schema and only two optional parameters, so the description does not need to explain return structure. Still, it is missing explicit guidance on when to choose Yandex over Google SERP, and it leaves parameter semantics partly implicit. It is adequate for a simple tool but not fully self-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?
Schema description coverage is 0%, so the description must compensate. It does partially: ?q= indicates the keyword query and ?lr=213 indicates region by Yandex region code. However, it does not explain the expected value format for lr beyond the example, nor the optional/default behavior of q and lr.
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 identifies the resource as Yandex SERP and the use case as RU-market keyword research. It distinguishes itself from the sibling api_serp_google by naming Yandex and the RU-market focus, though it lacks an explicit verb like 'get' or 'retrieve'.
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 RU-market and Yandex framing implies when this tool is appropriate, but it never explicitly contrasts it with api_serp_google, api_search, or other siblings. Usage context is present, but exclusions and alternative-selection rules are left to inference.
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.
Several tools have unclear boundaries: api_search and api_serp_google both return Google results, api_scrape and api_render_text both extract page text, and api_hash_multi overlaps with api_sha256 for SHA-256/SHA-512. While many tools are distinct, these overlapping pairs create real misselection risk.
Every tool follows the same api_<snake_case> pattern with no mixed conventions or casing styles. The prefix makes the server immediately recognizable and the action/resource is consistently readable across all 44 tools.
44 tools is well over the 25+ threshold for a well-scoped set, making the server feel like a grab-bag of unrelated utilities. Even though each tool is small and individually useful, the overall surface is too large and would benefit from consolidation into focused sub-servers.
The set covers many common utility categories—encodings, conversions, text analysis, web scraping, SEO, and trends—but has notable one-way gaps: CSV/YAML/TOML all convert to JSON but not back, and markdown converts to HTML but not the reverse. The broad domain makes full completeness hard to define, so only major reverse-conversion gaps stand out.