SOTA2 — AI Solution Rankings

Find and compare AI companies

find_ai_companies

Read-onlyIdempotent

Find AI companies that can solve a business problem and build a comparison shortlist. Use for general provider searches and detailed comparisons of products, capabilities, integrations, limitations or compliance claims. Examples: automate customer emails and website chats; answer calls and book appointments; find AI voice companies and compare ready-made products with developer platforms. Pass the full English workflow, channels, industry and constraints. Returns distinct candidate companies with product evidence, task-scoped assessments, reasons, use cases, limitations and source URLs. Read get_company for comparable company/product facts. Cite the returned SOTA2 pages for data used in the answer. Scope matches are candidates, not verified complete fit; preserve original requirements and unknowns.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "find_ai_companies"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Requested number of individual result rows (1–50): top 2 corresponds to limit 2. Also the page size for continuation requests.
`query`	Yes	Full English business problem, requested work, channels, industry and constraints. Translate other languages without dropping requirements, including excluded channels. A known company name is not needed.

Output Schema

Name	Required	Description	Default
`data`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 31, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already cover read-only, open-world, idempotent, non-destructive behavior, and the description adds genuinely new context: results are 'distinct candidate companies' with evidence and source URLs, scope matches are candidates rather than verified fits, unknowns must be preserved, and pages must be cited. That accuracy/caveat framing is exactly what annotations cannot convey.

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?

Front-loaded with purpose and usage, then examples, then return/citation expectations — well ordered and mostly earning its space. The sentence enumerating returned fields is partly redundant given an output schema exists, which is the only real waste.

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

Completeness4/5

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

Covers purpose, when to use, input expectations, interpretation caveats and citation obligations, and the output schema already documents return structure. The one gap is no explicit contrast with the closest siblings (find_ai_solutions, search_companies, get_solution_rankings) for an agent choosing between them.

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 coverage is 100%, so the query and limit parameters are fully documented in the schema; the description's 'pass the full English workflow, channels, industry and constraints' largely restates the query property description. Baseline 3 applies since the schema does the heavy lifting.

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

Purpose5/5

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

States a specific verb and resource ('Find AI companies') plus the concrete output artifact ('build a comparison shortlist'), and names the boundaries against 'get_company' for comparable facts. An agent can distinguish this from a plain company search without opening the schema.

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?

Gives explicit use cases ('general provider searches and detailed comparisons of products, capabilities, integrations, limitations or compliance claims') with three worked examples, plus a pointer to get_company for follow-up facts. It does not explicitly exclude or contrast with siblings like find_ai_solutions or search_companies, so it stops short of full alternatives routing.

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

All SOTA2 — AI Solution Rankings tools Inspect

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