niubiagent Signal Intelligence

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

With no annotations, the description carries the full burden of behavioral disclosure. It says the tool 'builds' a briefing, implying aggregation/synthesis, but does not disclose whether it mutates state, how it selects or ranks signals, whether it caches, or what the output structure looks like. This is a meaningful gap for a tool that appears to synthesize data.

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, compact sentence that front-loads the core purpose ('Build a compact briefing and action queue') and includes the key qualifier ('from the latest source-backed signals'). No wasted words.

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 tool with one optional parameter and no output schema, the description is mostly adequate, but it lacks any detail about the output format or how the briefing is constructed. Given the sibling tools include raw signal access, an agent would benefit from knowing whether this returns a summary object, a list, or something else.

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 0%, so the description must compensate. The description does not mention the 'limit' parameter at all, but the schema itself provides a clear name, type, default, and bounds, which is reasonably self-documenting. The description adds no semantic meaning beyond the schema, so a baseline 3 is appropriate.

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 uses a specific verb ('Build') and names the resource ('a compact briefing and action queue') derived from 'latest source-backed signals.' It clearly distinguishes the tool's purpose from siblings like get_latest_signals or search_signals, though it doesn't explicitly name a sibling.

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 this is the tool to use when a synthesized briefing/action queue is needed rather than raw signals, but it does not explicitly state when to use it versus get_latest_signals or search_signals. No exclusions or alternative routing are provided.

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 must carry behavioral disclosure. It states that results are 'verified' and include 'source URLs, change types, and action items,' which tells the agent what to expect. However, it does not describe ordering beyond 'newest,' how the limit affects results, or any caveats about data freshness or access requirements.

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?

Single sentence, front-loaded with the action and object, and no filler. Every word contributes to understanding the tool's purpose and output.

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 tool, the description covers the core return value, but there is no output schema and no guidance about when to prefer this over similarly named siblings. The agent can call it correctly from name, schema, and description, but the description does not fully compensate for the missing return schema and usage context.

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 only parameter, limit, has no description in the schema (0% schema description coverage), and the tool description never mentions it. While the name and min/max/default make its purpose inferable, the description adds no explicit connection between limit and the number of returned signals.

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 verb ('Return') and resource ('newest verified AI-agent ecosystem signals') and lists return fields ('source URLs, change types, and action items'), so an agent can tell this is a latest-signals list tool. It does not explicitly contrast with siblings like search_signals or get_signal, but 'newest' provides an implicit differentiator.

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?

No sentence says when to use this tool versus sibling tools such as search_signals, get_signal, or get_daily_briefing. The description only states what the tool returns, leaving the agent to infer that 'latest' is the selection criterion. There are no exclusions or alternative routing clues.

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 must fully disclose behavior. It does state the return content (summaries, provenance, confidence, actions), which is useful, but it omits any mention of error behavior (e.g., slug not found), read-only assurance, or response shape. For a simple read operation this is acceptable but not exhaustive.

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, efficient sentence that front-loads the action and resource, then lists the return contents. No redundant words, and every element adds 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?

For a low-complexity tool with one parameter and no output schema, the description covers the primary purpose and return contents. It lacks explicit error handling or usage context, but given the simplicity and the absence of annotations, it is reasonably complete. A small gap is the lack of guidance on when not to use it.

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 0%, so the description must clarify the parameter's role. It does so by saying 'by slug', which tells the agent that slug is the unique identifier. This adds semantic meaning beyond the schema's type/length constraints. It could provide format examples, but the core meaning is clear.

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 verb 'Retrieve' and the resource 'signal', specifies the lookup key ('by slug'), and enumerates the included content (human/agent summaries, provenance, confidence, actions). This distinguishes it from sibling tools like get_latest_signals or search_signals, which operate on collections or filters.

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?

No guidance is given on when to use this tool versus alternatives. The description implies the agent needs a slug but does not mention that search_signals should be used to find slugs, nor does it contrast with get_latest_signals or get_daily_briefing. This is a significant gap for a tool with four siblings.

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 full burden. It conveys a read-only inspection behavior and names the output themes, which is useful. However, it does not disclose details such as freshness thresholds, failure interpretation, or any operational caveats.

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 concise sentence that front-loads the action and immediately names the key content areas. There is no wasted text.

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 zero-parameter read-only tool with no output schema, the description is largely sufficient and names the key output categories. It could add explicit mention of what 'source' refers to or how results are scoped, but nothing critical is missing for basic invocation.

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 has zero parameters, so there is nothing for the description to explain beyond what the schema already communicates. The baseline for parameter semantics is appropriately high because no parameter documentation is needed.

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 uses a specific verb ('Inspect' with 'source health' as the resource) and enumerates the covered aspects: coverage, last runs, failures, and content freshness. It is clear about what the tool observes, though it does not explicitly differentiate from sibling tools by name.

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 guidance on when to use this tool versus siblings like get_latest_signals or search_signals. The usage context is implied by the word 'Inspect,' but no alternatives, exclusions, or prerequisites are stated.

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 full burden of behavioral disclosure. It only mentions that results are 'source-backed' and 'machine-readable', which touches on output format but not on rate limits, authentication, side effects (though it's likely read-only), error conditions, or pagination behavior. For a search tool with no annotations, this is a significant gap – an agent doesn't know if the search is expensive, if it mutates anything, or what happens on invalid filters.

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, compact sentence that covers the core functionality without extraneous words. It front-loads the search action and lists the key filters, then adds a brief note on output format. This is appropriately concise for the information it conveys, though it could be slightly longer to address other dimensions.

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 tool with 5 parameters, no required fields, no output schema, and no annotations, the description is insufficiently complete. It doesn't explain how multiple filters combine (AND/OR), whether there's pagination or if the 'limit' parameter controls a hard cap, what the output structure looks like beyond 'machine-readable', or any constraints on valid query syntax. An agent would have to experiment or guess to use this tool correctly.

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 only 20% (only 'query' has a description). The description lists keyword, category, tag, and sort order but does not explain the meaning of category enum values (e.g., 'agent-infrastructure' vs 'model-behavior') or sort enum semantics (e.g., what 'usefulness' or 'confidence' mean). It also omits 'limit' entirely. With low schema coverage, the description needed to compensate by clarifying parameter usage, but it stays at a surface level.

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 verb 'search', the resource 'niubiagent signals', and the filtering dimensions (keyword, category, tag, sort order). It also adds that results are 'source-backed, machine-readable', which distinguishes this from siblings like get_latest_signals or get_signal that likely return predefined sets or single records. The tool's role as a search endpoint is unambiguous.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention that get_latest_signals is for recent signals or that get_signal is for a specific signal. Usage is only implied by the name 'search', but there is no explicit context about when searching is appropriate or when another sibling is preferred. With zero exclusions or comparisons, the agent must infer routing on its own.

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