Research a topic on the web and return a synthesis with cited sources (titles + URLs). Pay-per-query research for when an agent needs current information with sources. input=topic to research. [x402: 0.02 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Tema o pregunta a investigar |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It discloses web access (research on the web), the cited-return contract, and a pay-per-use cost of 0.02 USDC on Base. It does not cover failure modes or latency, but the main externally-observable behaviors are disclosed.
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 main purpose. The only waste is the minor redundancy of 'pay-per-query' being stated twice (once in prose, once in the pricing tag) and 'input=topic', which repeats the schema.
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 single-parameter tool with no output schema, the description is nearly complete: purpose, usage condition, cost, and return format are all covered, so agents can invoke it correctly. It only lacks edge-case info like no-results behavior, but that is minor for such a simple tool.
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%, with the single 'input' param already described as 'Tema o pregunta a investigar'. The description's 'input=topic to research' adds marginal reinforcement but no new meaning, so 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?
Description states a specific verb + resource ('Research a topic on the web') and a concrete output shape ('synthesis with cited sources (titles + URLs)'). The mention of pay-per-query research for current information clearly differentiates it from siblings like fact-check, summarize, or explain.
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 gives an explicit usage context: 'when an agent needs current information with sources.' It does not name specific alternatives or when-not-to-use, but the use-condition is clearly stated, and the cost note implies a judgment criterion before invoking.
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.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.