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ai-agents-market-2026

INDUSTRY REPORT: El mercado de AI agents 2026 (deep, cited). input=optional scope. B2B: VCs y equipos de estrategia dimensionan el mercado de agentes IA. [x402: 100.0 USDC on Base, pay-per-use]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesservice input

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the behavioral disclosure burden. It discloses a real cost ('100.0 USDC on Base, pay-per-use'), the depth of output ('deep, cited'), and an input scope ('input=optional scope'). These are genuine behavioral traits beyond what the schema shows. However, 'input=optional scope' conflicts with the schema's required flag, which is a mild transparency concern, but not an annotation contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact, front-loaded with the core 'INDUSTRY REPORT: ...' label, then adds scope, audience, and payment in short segments. Every clause carries information, and there is no filler. It is terse to the point of being cryptic, but structurally it is efficient and appropriately sized for a micro tool description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the report topic, audience, scope input, and payment, which is a good baseline for a single-parameter tool. However, it does not explain the output format, language, or expected structure of the generated report, and the input parameter remains ambiguous ('optional scope' with no example). Given that the tool charges 100 USDC, more guidance on what to provide and what to expect would be valuable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% but the only property is described unhelpfully as 'service input'. The description adds that the input is a 'scope', which gives some meaning beyond the schema. Yet this guidance is thin—no examples of scope, no format details—and its claim of 'optional' contradicts the schema's required field. This is a minimal-value addition over a poorly informative schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'INDUSTRY REPORT: El mercado de AI agents 2026 (deep, cited)' which clearly identifies the resource: an industry report on the 2026 AI agents market. It adds target audience ('VCs y equipos de estrategia') and depth/citation expectations, making it distinguishable from generic sibling report tools. However, the action is implied by the noun phrase rather than stated as an explicit verb like 'generates' or 'provides'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'B2B: VCs y equipos de estrategia dimensionan el mercado de agentes IA' gives a clear context: this tool is for VCs and strategy teams sizing the AI agents market. It implies when to use it and what problem it solves. It does not name alternatives or exclude other cases, but the audience/task framing is specific enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.6/5.0
Disambiguation1/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness3/5

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.

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