Composed tool chaining competitor-scan → market-data → email-campaign-pipeline → language-detect (pay-per-call, x402). [x402: 0.01 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | pipeline 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 provided, the description carries the full burden, and it does disclose the sequential chaining behavior and the pay-per-call cost ('0.01 USDC on Base, pay-per-use'). However, it does not address output format, side effects, failure behavior, or what happens at each stage beyond the chain order.
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-loads the pipeline chain before cost details. It slightly repeats the pay-per-call concept with both 'pay-per-call' and 'pay-per-use', so it is not entirely waste-free, but it is appropriately brief.
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 multi-stage composite tool with no output schema and no annotations, this description is too thin. It names the chained tools but does not explain what the pipeline input should be, what result is ultimately returned, or why language-detect is the final stage.
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 already covers the single 'input' parameter at 100% with the phrase 'pipeline input', so the baseline is 3. The description confirms the input feeds into the chained pipeline but adds no detail about input format, expected content, or stage-specific usage.
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 names a specific pipeline of tools ('competitor-scan → market-data → email-campaign-pipeline → language-detect'), which makes it clear what the composite tool chains and distinguishes it from sibling combo tools. It lacks a true verb+outcome statement, so it is not a 5, but an agent can understand the tool's composition.
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?
No explicit when-to-use or when-not-to-use guidance is given. The chain implies a competitor/market-analysis-to-email workflow, but the description never explains when to choose this combo over running the individual tools or selecting combo-gtm-email-crypto-2e0abb.
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.