Generate a written, structured research report on a topic: researches the web and synthesizes a 400-600 word report with cited sources. Premium service for when an agent needs analysis, not just links. input=topic. [x402: 0.5 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Tema del informe a redactar |
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, the description carries the full behavioral burden. It meaningfully discloses that the tool 'researches the web', 'synthesizes a 400-600 word report with cited sources', and is pay-per-use with a specific cost. It does not disclose potential failure modes, latency, or exact response structure, but key behavioral traits are transparent.
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, front-loaded with the core purpose, and contains no unnecessary filler. The cost and use-case details each serve the agent's selection decision. The 'input=topic' fragment is somewhat redundant with the schema, which prevents a 5.
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 covers the task, the output length and type, the research process, citations, and the cost implication. Minor gaps: it doesn't explicitly state the exact response format (e.g., text vs structured JSON) or when it should not be used.
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 parameter 'input' has a 100% schema description coverage ('Tema del informe a redactar'), so the schema already explains what it is. The tool description adds the light gloss 'input=topic', but does not add meaningful semantics beyond the schema. Baseline 3 is appropriate.
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?
Provides a specific verb ('Generate'), resource ('written, structured research report') and key characteristics (web research, 400-600 words, cited sources). It is clear what the tool does and broadly sets it apart from link-only tools via 'not just links', though it does not explicitly name a sibling.
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?
Explicitly states when to use: 'when an agent needs analysis, not just links.' This is a clear usage condition that differentiates from look-up or link-return tools. It does not name actual alternatives such as 'research', so it lacks a fully explicit alternate-routing statement.
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.