api_seo_metaSEO snapshot of a page: title, description, OG/Twitter meta, h1s, h2 count, canonical, robots meta, lang, word count. ?url= [HTTP x402 price: $0.005]
| 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: +{
+ "url": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+ }
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It does add useful context: the output fields, the ?url= invocation pattern, and the cost of the request. However, it does not describe error behavior, whether the page is fetched live, URL requirements, or other operational characteristics.
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 compact single line that front-loads the tool's purpose and lists delivered fields efficiently. The 'HTTP x402 price: $0.005' fragment is somewhat cryptic but does not waste space. Overall it is appropriately sized and scannable.
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 output schema covers return values, so the description does not need to explain those. However, the description and schema together leave gaps around whether the url parameter is effectively required, what happens without it, and how the URL should be supplied. The cost and basic invocation are covered, making it minimally viable but incomplete for unassisted invocation.
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?
The schema has zero description coverage, so the description must compensate, but it only mentions '?url=' without explaining URL format, encoding, or whether a full absolute URL is required. The parameter name is self-explanatory, but the lack of any additional semantic detail leaves a gap for an agent trying to invoke the tool correctly.
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 produces an 'SEO snapshot of a page' and enumerates the exact fields returned (title, description, OG/Twitter meta, h1s, h2 count, canonical, robots meta, lang, word count). This is specific enough to convey the resource and output, though it does not explicitly contrast with overlapping siblings like api_scrape or api_robots_audit.
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 phrase 'SEO snapshot of a page' implies when to use the tool, but there is no explicit guidance about when not to use it or which alternative tool to choose instead. The description provides a use case rather than a clear decision rule.
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.