summarize
Summarize any text into clear, faithful bullet points. Condense documents or threads. input=text. [x402: 0.003 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | texto a resumir |
Summarize any text into clear, faithful bullet points. Condense documents or threads. input=text. [x402: 0.003 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | texto a resumir |
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?
No annotations are provided, so the description carries the full burden. It discloses the output style ('bullet points'), the quality intent ('faithful'), and the pay-per-use pricing on Base. However, it does not mention input size limits, failure modes, or other operational behaviors.
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 short and front-loaded, with no obvious filler. It briefly includes both the core capability and pricing context. There is slight redundancy between 'Summarize any text' and 'Condense documents or threads', but overall the structure is efficient.
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 one-parameter tool with no output schema, the description gives enough to invoke it correctly: it specifies the input as text, the output as bullet-point summary, and includes pay-per-use context. It could be more complete with size limits, but nothing critical is missing for basic usage.
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?
Both parameters are covered by the schema: the input property is described as 'texto a resumir' and is a required string. The description repeats some of this with 'input=text' but adds no additional meaning such as length limits, accepted formats, or range constraints.
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 states a clear action and resource: 'Summarize any text into clear, faithful bullet points' and adds context with 'Condense documents or threads.' It is specific about the output format, but it does not explicitly differentiate itself from similar siblings like compress or destilar.
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 provides clear usage context: it applies to 'any text' and specifically mentions documents or threads. It does not state when not to use it or point to alternative tools, so it lacks explicit exclusion guidance.
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.