DeepInquiry Verified Facts

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered by structured data. The description adds one piece of behavioral content beyond that — the fact is returned 'with its source groups' — but says nothing about what happens if the id does not exist or how missing ids are handled.

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 front-loaded sentence that names the action, the lookup key, and the payload, with no filler. Every clause earns its place.

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 one-parameter read tool with no output schema, the description is adequate but thin: it hints at the return ('source groups') without describing the response shape, and gives no error or empty-result behavior. It stops just short of what an agent would want when an id is invalid.

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 description coverage is 100% for the single id parameter, so the schema already documents the type, minimum, and meaning. The description's 'numeric id' phrasing is consistent but adds no constraint or format detail beyond the schema, so the baseline of 3 applies.

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 ('Fetch one fact') and the lookup key ('by its numeric id'), which pins down the operation. It does not explicitly contrast itself with the siblings random_fact and search_facts, so the distinction is left to inference.

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 when-to-use guidance: it never says to use this when the id is already known versus using search_facts or random_fact. The 'by its numeric id' phrasing weakly implies the precondition but no alternative is named.

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=true and openWorldHint=false, so the safety profile is covered. The description adds that only 'approved' facts are returned, a useful filtering trait, but says nothing about what a result contains or how selection works.

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 eight-word sentence with no filler, and the key qualifier 'random approved' is front-loaded. It is efficient, though so terse that it omits information an agent might want.

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 trivial zero-parameter read tool this is close to sufficient, but with no output schema the description should indicate what is returned (fact text, category, identifier, etc.). That return-shape gap is the main incompleteness.

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 no-parameter tool applies. The description correctly signals an argument-free call.

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 gives a specific verb and resource ('Fetch one random ... fact') and the qualifier 'random' and 'approved' narrows the scope meaningfully. It does not name its siblings get_fact or search_facts, so an agent must infer the distinction (random vs. specific vs. filtered) rather than being told.

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 word 'random' — pick this when any fact will do, as opposed to retrieving a known one or searching. However, no alternative is named and there is no explicit when-to-use/when-not guidance, so an agent must reason it out from the sibling names.

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=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful return context (sources, tier, score, signature) and implies ranked results, but says nothing about auth, rate limits, pagination, or how ties/scores behave. Moderate added value on top of annotations.

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 tight sentences with zero waste: the first establishes what the tool searches, the second enumerates the return fields. The core purpose is front-loaded.

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 helpfully enumerates the returned fields (sources, tier, score, signature), and the annotations cover safety while the schema covers all four parameters. Only gap is the minor 'verified facts' framing versus the unverified tier option, and no note on result ordering.

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 description coverage is 100%, so limit, query, category, and min_tier are all documented with defaults and constraints in the schema itself. The description adds no syntax, format, or filtering semantics beyond what the schema already provides, so the baseline 3 applies.

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+resource ('Semantic search over DeepInquiry verified facts') and implies ranking behavior. It broadly distinguishes itself from get_fact and random_fact, but never names them or states how search differs from fetching a single or random fact. One minor tension: it says 'verified facts' while the schema permits min_tier='unverified'.

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 explicit when-to-use guidance, no mention of the sibling tools get_fact or random_fact, and no conditions under which this tool is the wrong choice. An agent must infer from the name alone that this is the query-driven retrieval path.

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