DartzDabz AI agent tool directory

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is fully covered without description help. The description usefully adds that the counts carry a date and that a category list is included, hinting at a snapshot rather than live data, but it says nothing about freshness, caching, or staleness limits. Adequate, not rich, given annotations carry the safety burden.

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?

A single sentence that front-loads the core question ('How many tools DartzDabz tracks') and then lists the breakdowns in a compact parenthetical, with the date and category list appended. No filler or redundant restatement of the title.

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?

With no output schema, the description carries the burden of describing return values, and it does enumerate the counts, their date, and the category list. It omits the shape/keys of the response and whether the date is per-count or global, but for a simple parameterless stats endpoint this is largely sufficient.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. The description correctly implies the call takes no arguments by describing only returned content.

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 names a specific resource (DartzDabz directory counts) and enumerates exactly what is counted (total, by source, scored, robots opted out, pending), which distinguishes it from get_tool, get_x402_scores and search_tools. It is a clear retrieval purpose, though it never says the word 'retrieve' or 'get' explicitly.

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 statement of when to call this tool versus its siblings — no 'use this to get an overview before searching' style guidance. The agent must infer that this is a directory-statistics endpoint from the name and returned-field list alone.

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

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds genuine behavioral context by enumerating exactly what comes back (endpoint URL, type, trust score, trust/readiness sub-scores, UTC last-check time, page URL), which matters because there is no 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.

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

Two sentences, no filler. The lookup key is front-loaded and the return-field list follows, which is the right order for a fetch tool.

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 single-parameter read tool with full annotation coverage, the description supplies the one missing piece an agent needs: the shape of the response, since no output schema exists. It stops short of noting behavior for missing or ambiguous identifiers, which is a minor gap.

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?

With 100% schema description coverage, the single id parameter is already fully documented in the schema, including the same 'mcp/github-official' example. The description largely restates the accepted identifier forms rather than adding new constraints such as ambiguity handling when a name matches multiple entries.

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?

States a specific verb (get) and resource (one tool from the DartzDabz directory) and specifies that lookup is by a unique identifier, which clearly separates it from the plural, query-based search_tools sibling. An agent can tell immediately this is the point-fetch counterpart to search.

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?

Usage is implied by the identifier-based framing (use when you already know the slug, URL, or exact name), but the description never states when to prefer this over search_tools or what to do if the identifier is unknown. No exclusions or prerequisites are given.

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

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

Annotations already declare readOnly/idempotent/non-destructive, but the description adds real context beyond them: the data comes from an 'unpaid, never-signing' probe (so no auth or cost is incurred), it is refreshed daily, and attribution is required under CC BY 4.0. That licensing obligation is genuinely useful behavioral information not available anywhere else.

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?

Two compact sentences, front-loaded with what the tool returns before the filtering and licensing detail. The attribution string is slightly awkward inline but earns its place as a usage requirement.

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?

No output schema exists, but the description compensates by enumerating the returned fields (grade, score, latency, HTTP status, price, network). What is missing is pagination/limit behavior and the volume of results returned, which matters for a list tool.

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?

Schema coverage is only 33%, so the description carries extra burden; it mentions host and min_score filtering, which maps to two of the three parameters, but adds no syntax or range detail beyond the schema, and the limit parameter is untouched. Marginal value over the schema, consistent with a 3.

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?

States a specific verb (get/return) and resource (x402 endpoint scores) plus the exact fields returned: grade, score, latency, HTTP status, price, network. This is clearly distinct from siblings like get_directory_counts, get_tool and search_tools, which cover unrelated resources.

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?

It says the tool supports filtering by host or minimum score and implies a freshness model via 'latest ... daily probe', so usage is inferable. However it names no alternatives, no when-not-to-use case, and no guidance on choosing between host vs min_score filtering beyond the schema.

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

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.

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.

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.

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.

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.

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.