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by io.github.builtwith
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vector-api

Search BuiltWith technologies and categories by text query using vector similarity. Returns ranked results with similarity scores (0–1), descriptions, and category info. Useful for discovering what technologies match a concept or description (e.g. 'react framework', 'payment gateway', 'live chat'). Costs 1 API credit per query.

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": "vector-api"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax results to return (default 10, max 100)
queryYesText to search for (e.g. 'javascript framework', 'email marketing')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

The description adds real context beyond the annotations: 'Costs 1 API credit per query' is a billing side effect an agent cannot learn from the schema, and it explains why readOnlyHint is false on what is semantically a search. It also discloses the shape of results (ranked, similarity scores 0–1, descriptions, category info), which compensates for the absence of an output schema.

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?

Four sentences, front-loaded with purpose, then return shape, then use cases, then cost. Each sentence carries information the agent needs, though the example query list overlaps with the schema's own examples and could be trimmed without loss.

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?

For a two-parameter read-style tool with no output schema, the definition covers purpose, return format, qualifying use cases, and cost. The one material gap is disambiguation from the near-identically named x402 sibling, which matters for correct tool selection.

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%, so both 'query' and 'limit' (with default 10 and max 100) are already documented in structured form. The description's example queries duplicate and slightly restate what the schema already gives, adding no new syntax, tokenization, or matching-behavior detail. Baseline 3 applies when the schema carries the parameter burden.

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 a specific verb and resource: 'Search BuiltWith technologies and categories by text query using vector similarity.' That is unambiguous about what the tool does. It does not, however, distinguish itself from the sibling 'x402-vector-api', which by name appears to be the same capability under a different payment path, leaving the agent to guess between them.

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

Usage Guidelines3/5

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

Usage is implied through concrete examples ('react framework', 'payment gateway', 'live chat') that show the intended conceptual-discovery use case. There is no explicit when-not guidance and no routing to alternatives such as the keyword or x402 variants, so an agent must infer selection from tool names alone.

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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