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DartzDabz AI agent tool directory

Search the DartzDabz AI agent tool directory

search_tools
Read-onlyIdempotent

Search the DartzDabz directory of AI agent tools (MCP servers, x402 pay-per-call APIs and agent platforms) by keyword, with optional type and category filters. Returns name, type, category, the latest automated 0-100 trust score (null when not scored) and the DartzDabz page URL. Official vendor MCP servers rank first on ties.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional: limit to MCP servers, x402 APIs or agent platforms (app).
limitNo
queryNoKeywords, e.g. "github", "web search", "weather". Empty lists top tools.
categoryNoOptional category id: payments, search, dev, data, productivity, ai, commerce, security, other (see get_directory_counts).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds genuinely new behavior: result fields returned, that trust scores can be null when unscored, and that official vendor MCP servers rank first on ties. It omits pagination/resumption behavior, which is a minor gap for a capped-limit search.

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

Conciseness5/5

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

Two dense sentences, front-loaded with the verb and scope, then the return payload and tie-breaking rule. No filler or repetition of the title.

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

Completeness5/5

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

With no output schema, the description carries the return-value burden itself (name, type, category, trust score with null caveat, page URL) and also covers search semantics and ranking. An agent has everything needed to call it and interpret the response.

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

Parameters4/5

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

Schema coverage is 75%, so the schema already documents type, query and category. The description restates the filter semantics in prose and adds ordering behavior relevant to interpreting results; only limit lacks any explanation in either place (its 1-25 bounds are self-evident from the 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?

States a specific verb and resource (search the DartzDabz directory of AI agent tools) plus the searchable facets (keyword, type, category). An agent can distinguish it from get_tool (single lookup) by inference, but the description never names a sibling or explicitly contrasts the two.

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?

Provides one concrete usage rule ("Empty lists top tools") and points to get_directory_counts for category ids, which is useful routing. However, it never states when to use this instead of get_tool or get_x402_scores, or any exclusions, so the guidance is implied rather than explicit.

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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