CONSUMER: compress a prompt to dense language keeping meaning (<=50%). input=text. B2B: save LLM tokens in pipelines. [x402: 0.001 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
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 disclosure burden and does it well: it specifies the compression target (<=50%), the preservation of meaning, and adds the pay-per-use cost of 0.001 USDC on Base. It does not contradict any annotations and provides meaningful behavioral context beyond a generic action.
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: it states the core action, the constraint, the input type, and the token-saving use case in a few short clauses. The pricing line is additional but useful, and every part earns its place.
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 text transformation tool with no output schema, the description covers the operation, input, output constraint, and commercial context. It is sufficiently complete, though it could briefly clarify what the caller receives in the response.
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 covers 100% of the parameter structure, so the baseline is 3. The description adds only a minimal clarification that the input is text, while the schema's own description is generic ('service input'). This is helpful but not substantial enough to raise the score.
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 a specific operation: compressing a prompt into dense language while keeping meaning and reducing length to at most 50%. It is clear and actionable, but it does not distinguish this tool from the similarly named sibling 'compress' or related tools like 'summarize' and '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 gives two usage contexts: consumer prompt compression and B2B token cost savings in pipelines. This implies when the tool is useful, but it provides no explicit guidance on when to prefer comprimir over alternatives such as 'compress', 'summarize', or 'destilar'.
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.