market-research-report
ENTERPRISE: full market report (TAM/SAM/SOM, trends, competitors) + sources. input=market. [x402: 25.0 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service input |
ENTERPRISE: full market report (TAM/SAM/SOM, trends, competitors) + sources. input=market. [x402: 25.0 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?
No annotations are provided, so the description carries the burden. It does disclose that this is an ENTERPRISE, pay-per-use tool costing 25.0 USDC on Base, and that it returns a report with sources. It does not mention response format, latency, or side effects, but for a reporting tool the key cost behavior is covered.
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 definition is a single compact line with the core purpose and pricing front-loaded. Every segment adds information, though the 'input=market' shorthand is cryptic and could be clearer.
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 one-parameter tool with no output schema, the description is mostly adequate but the ambiguous input instruction prevents full self-sufficiency. It does not explain how to pass a market topic, and the lack of output schema increases the need for a clearer description of the returned report format.
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 input schema's `input` is only documented as 'service input', which is generic. The description adds 'input=market', telling the agent what the parameter should be set to, but it is ambiguous whether the value should be the literal string 'market' or the specific market to research. This is helpful but not fully precise.
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 the tool produces a full market report with TAM/SAM/SOM, trends, and competitor analysis plus sources. This distinguishes it from generic report/research tools even without naming a sibling. The 'input=market' fragment slightly muddies the purpose, but the core deliverable is specific.
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?
There is no explicit when-to-use or when-not-to-use guidance or named alternatives. The description implies it is appropriate when the user wants a comprehensive paid market report, but does not contrast it with market-data, market-intelligence, or competitive-analysis.
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.