factanker

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

With no annotations provided, the description carries the full burden. It discloses that facts are currently valid and substantiated, and enumerates the response fields (source, location, time reference, retrieval time, license, fact_url), which is substantial behavioral context. It does not mention edge cases or rate limits, but for a read tool, this is adequate.

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?

The description is compact and well-structured: two sentences. The first sentence states the core output and enumerates the metadata fields, the second adds the optional filter. Every word contributes value without fluff.

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?

Given the tool has only two parameters and no output schema, the description is quite complete. It explains the response contents, the optional filter, and the scope ('currently valid'). It still lacks details on pagination, error behavior, or limits, but these are not critical for a fact-fetching 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?

The input schema provides no descriptions for the two parameters, and the description compensates by explaining that 'entity' is the entity whose facts are returned and that 'predicate' is an optional filter with concrete examples ('revenue', 'total_assets'). It does not specify the exact format for entity, but adds meaningful clarity beyond 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?

The description clearly states the tool returns all currently valid, documented facts for an entity, with optional predicate filtering. It is specific about the resource (facts of an entity) and includes response details, but lacks an explicit verb like 'retrieve' or 'list' and does not directly distinguish from sibling tools.

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?

The description implies usage: call this tool to get facts for a specific entity, optionally filtered by predicate. However, it does not explicitly discuss when to use this tool versus lookup_entity or search_facts, leaving the differentiation to inference.

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?

No annotations are provided, so the description carries the burden. It mentions that all known register anchors are returned, which is useful, but it doesn't disclose potential side effects, rate limits, or error behavior. It's adequate but not rich.

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?

The description is concise, two sentences, and front-loaded with the core purpose. Every sentence adds value, no fluff.

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?

Given the tool's simplicity (one parameter, no output schema), the description is fairly complete. It explains the input format and what is returned. However, it lacks details on error cases or how to interpret the output, which could be important for an agent.

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 schema has only one parameter with 0% description coverage, so the description must compensate. It explains that name_or_id can be a name or an ID with specific formats, which adds meaning beyond the bare schema. However, it doesn't specify the expected format for names or how to handle ambiguous inputs.

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 clearly states the tool finds entities in the FACTANKER register by name or ID, and specifies the ID formats. It distinguishes from siblings by focusing on entity lookup rather than facts.

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 provides guidance on preferring IDs over names and lists specific ID formats, which helps the agent choose the right input. However, it doesn't explicitly contrast with sibling tools like get_facts or search_facts, so it's not a full 5.

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?

With no annotations, the description carries the burden. It discloses that results are the most recent matching facts and include provenance, adding helpful behavioral context. However, it does not mention read-only semantics, pagination, or sorting details beyond 'jüngsten'.

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?

The description is a single, front-loaded sentence with no unnecessary words. It efficiently conveys the core purpose and return value.

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?

The tool has only two parameters and no output schema. The description covers the search scope and return content but lacks usage guidelines and parameter details for 'limit'. Given the lack of annotations, it is minimally sufficient but has gaps.

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 description gives semantic meaning to the 'query' parameter by stating it searches over entity names and predicates, but it does not describe the 'limit' parameter. Since schema coverage is 0%, the description only partially compensates for parameter documentation.

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 clearly states it performs full-text search over entity names and predicates, returning recent matched facts with provenance. This distinguishes it from sibling tools like lookup_entity (entity lookup) and get_facts (fact retrieval).

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?

The description implies its use for searching by free-text across entity names and predicates, but it does not explicitly state when to prefer it over siblings or mention exclusion criteria. No alternative tools are named.

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