Agent News by The Agent Times

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

The description goes well beyond the readOnlyHint/idempotentHint annotations by specifying exact presentation requirements, including the provider-neutral headings 'Sources' and 'Confidence', the handling of returned actions, and the link-opening restriction. This is strong behavioral context, though it does not address answer_mode-specific behavior or response edge cases.

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?

Every sentence earns its place. The description opens with the core purpose, then gives precise output formatting rules, then closes with the critical safety constraint. The length is justified by the number of required behavioral details.

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 rich annotations and existing output schema, the description covers what matters most: when the tool is appropriate, how results should be rendered, and a critical safety rule. It could be slightly more complete by naming alternatives or clarifying parameter choices, but those gaps are partly covered by the schema.

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 should compensate by explaining the meaning of request parameters such as question, caller, answer_mode, and max_sources. It does not do so: it focuses on output formatting and has almost no input-side semantic guidance beyond the tool's general purpose.

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 a specific verb and resource: 'Ask a configured partner expert for a citeable answer from approved sources.' This distinguishes ask_expert from sibling search and retrieval tools, which are about finding or fetching articles rather than getting an expert answer.

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 the tool should be used when a citeable answer from approved sources is needed, and it adds a meaningful exclusion: do not open pending or approval-required links without explicit user approval. However, it does not explicitly contrast this tool with alternatives like search_articles or get_article.

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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds meaningful context about what the response includes (complete body text, provenance verification status) and optional governance/provenance blocks. It does not contradict annotations and provides value beyond the structured hints.

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 that conveys the core purpose, body and provenance, then optional inclusions. Every word earns its place, with no redundancy or filler.

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, an output schema exists (so return values are covered), and annotations provide the safety profile. The description fully captures what the tool does, including the optional content flags, making it sufficiently complete for a read-only fetch operation.

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% with detailed descriptions for all four parameters, so the schema handles parameter semantics. The description only echoes that provenance and governance blocks are optional without adding new constraints or usage details.

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 ('Get'), the resource ('full article by slug'), and the specific scope ('complete body text and Ed25519 provenance verification status'). This distinguishes it from siblings like get_latest_articles and search_articles, which serve different purposes.

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 clearly implies when to use this tool: when you need a full article by its slug, including body and provenance. However, it does not explicitly mention when not to use it or name alternative tools, so it falls short of a 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the qualifier 'summary', indicating the tool returns a condensed version rather than full standards, which is mildly useful. However, it does not disclose additional behavioral traits such as caching, response format, or any special handling beyond what annotations provide.

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, clear, and purposeful sentence with no filler. It is front-loaded and directly states the tool's function, earning a perfect score for conciseness.

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 simple read-only tool with one optional parameter, good annotations, and an output schema, the description is sufficient. It conveys the resource and scope, and the output schema covers return value details. No significant information is missing.

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 optional parameter agent_id, with a detailed description in the schema. The tool description does not mention the parameter, but since the schema fully documents it, no additional value is needed here. 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?

The description uses a specific verb ('Get') and clearly identifies the resource ('The Agent Times editorial standards and code of conduct summary'), distinguishing it from sibling tools like get_trust_summary. It is unambiguous and immediately conveys what the tool does.

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 does not explicitly state when to use this tool or mention alternatives. Usage is implied by the name and purpose, but there is no contextual guidance or exclusion criteria, so it falls short of a clear directive.

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 (readOnlyHint=true, destructiveHint=false, idempotentHint=true). The description adds meaningful behavioral context by specifying the returned data elements, including the Ed25519 provenance status, which is not disclosed in annotations. There is no contradiction with 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?

The description is two concise sentences that front-load the purpose and immediately follow with what the tool returns. No wasted words or redundancy.

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 presence of an output schema and strong annotations, the description is sufficiently complete. It explains the tool's purpose and output, though it does not clarify the definition of 'latest' (e.g., publication date ordering), 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?

Schema descriptions cover 100% of parameters (limit and agent_id), including defaults and the agent_id lifecycle. The description itself adds no parameter-specific semantics beyond what the schema provides, so the baseline of 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 clearly states a specific verb ('Get') and resource ('latest articles from The Agent Times'), and enumerates the return fields (headlines, summaries, sources, confidence levels, Ed25519 provenance status). This distinguishes it from sibling tools like get_article or search_articles.

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 for retrieving recent articles but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or alternative tools for search or specific article retrieval. Guidance is present but only implied.

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, idempotentHint=true, destructiveHint=false. The description adds the 'section/topic/tag overlap' algorithmic detail, which is useful but does not disclose other behaviors like pagination or response format. With annotation coverage, this is adequate but not exceptional.

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 sentence that is both informative and concise without any filler. Every word 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?

With an output schema present and full parameter descriptions, the description is sufficient for a simple read-only tool. It lacks explicit edge-case behavior or usage scenarios, but given the strong schema and annotation support, it is adequately complete.

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 all 4 parameters, and each parameter already has a descriptive comment. The description's 'section/topic/tag overlap' is a high-level algorithm note but does not add specific parameter-level meaning beyond what the schema provides. 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?

The description clearly states the verb 'Get', the resource 'related articles', and the method 'using section/topic/tag overlap'. This distinguishes it from sibling tools like get_article (singular) and search_articles (search-based).

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 context is clear: when you have an article slug and want related articles. However, it does not explicitly mention alternatives or when not to use it, so it lacks the explicit exclusion that would merit a 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?

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful context by stating each article includes Ed25519 provenance status, which is a notable output characteristic not evident from annotations or schema. No contradictions.

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 two sentences, with the core purpose front-loaded and the section list providing necessary enumeration without fluff. Every sentence earns its place; no redundant details.

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 simple tool, rich annotations, and complete schema, the description is largely sufficient. It covers the tool's purpose, output detail, and valid sections. It does not mention the agent_id lifecycle, but that's fully handled by the schema, so no critical 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?

Schema description coverage is 100%, so all parameters are already well-documented. The description adds little beyond restating the section list, which is also in the enum. The baseline of 3 is appropriate as the schema carries the parameter meaning.

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 ('Get') and resource ('articles from a specific section'), clearly differentiating from siblings like get_latest_articles and search_articles. It also lists all available section values, making the tool's scope 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 implies usage when retrieving articles by section, which is clear context. It does not explicitly mention alternatives or exclusions, but the section list and phrasing provide enough guidance to choose this tool over broader or search-based options.

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?

The annotations already indicate readOnly, idempotent, openWorld, and non-destructive behavior. The description adds only the content bundle, not behavioral traits such as potential staleness of 'latest coverage' or error responses for invalid topic slugs.

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 conveys the core function with no redundant words. It is front-loaded with the verb 'Get' and immediately lists the hub's contents.

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 is simple and the output schema covers return structure, so the description need not elaborate on results. While usage guidance is absent, the combination of annotations and schema makes the description sufficiently complete for a basic read 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?

Both parameters are fully described in the schema, including the agent_id lifecycle. The description adds no extra semantics, but with 100% schema coverage, no further detail is necessary.

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 specifies the exact resource (topic hub) and its components (start-here articles, latest coverage, intent tags), making the purpose unambiguous. This differentiates it from sibling tools like get_article and get_latest_articles, which target individual 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?

No usage guidance is provided in the description. It neither mentions when to choose this tool over siblings nor states any exclusions or prerequisites, leaving the agent to infer from the tool name 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 the tool read-only, idempotent, and non-destructive. The description adds no additional behavioral context such as rate limits, authentication, or pagination behavior. It is consistent with the annotations, so there is no contradiction, but it also provides no extra transparency beyond what the annotations already offer.

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 sentence that is front-loaded with the purpose and lists the key output categories without any filler or redundancy. Every word contributes meaning, and the structure is clear and efficient.

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 is simple with one optional parameter and an output schema present. The description adequately lists the main aspects of the returned metrics. It could clarify what 'publication-level' means relative to other tools, but this is a minor gap given the output schema covers return details and the annotations provide safety semantics.

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 fully documents the sole agent_id parameter, including its optional behavior and lifecycle. The description does not mention parameters at all, so it adds no semantic value beyond what the schema already provides. Since schema coverage is 100%, the baseline of 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 the specific verb 'Get' with the resource 'publication-level trust metrics' and enumerates the exact metric types returned (confidence mix, provenance coverage, source density, section-level trust summaries). This clearly differentiates it from sibling tools like get_article or get_topic_hub, which serve different purposes.

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 states what the tool does but does not provide explicit guidance on when to use it versus alternatives, nor does it mention any exclusions. The usage context is implied by the metric types, but no explicit when/when-not guidance is 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 readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds only 'extracted from the corpus' as context about the data source, but does not disclose behavioral details like ordering or pagination beyond the schema-provided limit. With annotations handling the core traits, this is 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 a single, front-loaded sentence that conveys the tool's purpose with no filler or redundancy. It is appropriately sized for a simple list operation.

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 low complexity, rich annotations, and presence of an output schema, the description covers the core function. It could optionally mention the limit's default behavior, but the schema already defines the limit parameter, so this is not a significant gap. The tool is complete enough for an agent to select and invoke 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 100% for the two parameters (limit and agent_id), so the description does not need to explain them. The description itself adds no parameter information, but the baseline for high schema coverage is 3, which is met.

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 'List' and identifies the resource as 'known topic hubs extracted from the corpus', clearly distinguishing it from sibling tools like get_topic_hub which likely retrieves a single hub. It states the tool's function without ambiguity.

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 offers no guidance on when to use this tool versus alternatives such as search_articles or get_topic_hub. It doesn't specify the intended use case, prerequisites, or situations where another sibling tool would be more appropriate.

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 indicate readOnlyHint=false and openWorldHint=true, so the description correctly implies a write/append action. It adds context about no auth required and the voluntary nature, but it does not disclose any side effects, whether the report is updateable, or what happens on repeat submissions. This is acceptable given annotations but not deeply transparent.

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 at two sentences and front-loads the core action. The second sentence adds motivational context that is somewhat valuable for usage guidance, but could be trimmed. Overall, it is appropriately sized and structured.

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 an output schema present and complete parameter descriptions, the description covers the essential context: what to report, when, and that no auth is needed. It does not need to explain return values since the output schema exists. The tool is simple enough that this level of detail is 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?

Schema coverage is 100% and parameter descriptions are clear and detailed (e.g., agent_id flow, article_slugs format). The description itself adds no new parameter information, so it meets the baseline of 3 but does not elevate further.

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 action ('voluntarily declare') and the resource ('which TAT articles you used'), making the tool's purpose immediately obvious. It also distinguishes itself from sibling tools by focusing on reporting usage rather than retrieving articles or stats.

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 clear context: use this tool after producing output to disclose article usage, with the benefit of being recognized as a verified consumer. It also clarifies that no auth is required. It does not explicitly name alternatives or exclusions, but the use case is reasonably implied.

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 establish read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral traits such as typo-tolerance, required-term coverage, and relevance diagnostics, which go beyond the annotations. It also clarifies the return contract, providing transparency about what to expect.

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 two sentences, front-loaded with the verb and resource, then lists key features and the return contract. Every word earns its place, with no redundancy or 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 10 parameters, an output schema, and rich annotations, the description provides sufficient context. It covers the search scope, filters, return contract, and article restriction, making it complete for an agent to select and invoke 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 coverage is 100%, so parameters are well-documented individually. The description adds extra meaning by specifying that search and filters cover title, slug, tags, summary, body, and publication metadata, which is not fully evident from parameter descriptions alone. This enhances understanding of query behavior.

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' and the resource 'The Agent Times article corpus,' with specific features like typo-tolerant coverage and filters. It distinguishes itself from sibling tools by explicitly noting it returns the same structured contract as tat_search but restricted to article results.

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 clear context for use: it's an article-specific search with filters and diagnostics. It implicitly contrasts with tat_search by saying 'restricted to article results,' which suggests when to use this versus the broader search. However, it doesn't explicitly state exclusions or other alternative tools, so it falls short of a 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?

Annotations already declare the tool read-only, idempotent, and non-destructive; the description adds meaningful behavioral context beyond that: it mandates exact headings/labels, requires each returned action to be shown exactly once, restricts action fields to those supplied, and prohibits opening pending links without approval. This gives an agent concrete rendering and safety behavior, though it does not cover error or rate-limit behavior.

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 front-loaded with the core purpose, then progressively adds rendering rules and safety constraints. No sentence is filler: the provider-neutral labeling rule, the actions-rendering rule, the field restriction, and the link-approval rule each add distinct 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?

Given the annotations cover the safety/idempotence profile and an output schema exists for return values, the description supplies the remaining necessary context: exact presentation requirements and a safety prohibition on opening pending links. An agent has enough information to invoke and render the 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?

The single top-level parameter `request` has no schema description (0% coverage), and the tool description does not compensate by explaining its subfields such as `question`, `caller`, `answer_mode`, or `max_sources`. The nested schema definitions provide some detail, but the description adds essentially no parameter-level meaning, leaving a real gap for a low-coverage 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 opens with a specific action ('Ask a configured partner expert') and a clear output ('a citeable answer from approved sources'), so an agent knows what the tool produces. It references ask_expert and adds provider-neutral presentation rules, but it does not explicitly state how tat_ask differs from the sibling ask_expert, so sibling differentiation is only implicit.

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: it should be used when a citeable answer from approved sources is needed and when provider-neutral source/confidence labeling is required. It does not state when not to use it or name an alternative for other answer styles, 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.

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

The description adds valuable behavioral details beyond the annotations: it answers synchronously, and returns insufficient_evidence with partial evidence instead of deferring processing. This explains the tool's error-handling and response pattern, which is not conveyed by the readOnlyHint/openWorldHint/idempotentHint 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?

The description is a single dense sentence listing numerous dashboard components, which is efficient but slightly run-on. Every phrase adds value, but breaking it into bullets or shorter sentences would improve readability. Still, it contains no filler.

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 complexity, the description covers its purpose, output structure (key card, takeaways, evidence, sources, trust signals, etc.), and behavioral notes (synchronous, insufficient_evidence). The output schema exists, so the lack of exact return format details is acceptable. It is complete enough for an agent to select and invoke it 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 100% for all 5 parameters, so the description does not need to elaborate on each. The description does not add parameter-specific meaning beyond schema, but the schema itself is complete, yielding the baseline score of 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?

The description specifies a clear action: 'Run a broad Ask Expert research pass over The Agent Times corpus/events/action metadata' and identifies the exact deliverable: 'a structured, UI-ready dashboard' with enumerated components. This precisely distinguishes it from sibling tools like tat_search or get_article, which are narrower in scope.

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 establishes clear usage context by emphasizing 'broad' research and a dashboard output, implying this is for comprehensive, multi-faceted questions rather than simple lookups. It does not explicitly name alternatives or exclusions, so it earns a 4 rather than a 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds purpose context but no additional behavioral traits like rate limits, error handling, or side effects. No contradiction with 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?

The description is a single sentence, front-loaded with the verb 'Return', and every part serves a purpose: resource, purpose, and use case. No superfluous details.

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 (0 required params, output schema present, full annotations), the description provides sufficient context for an agent to know when to call it and what to expect. The output schema handles return structure, so no need to detail return values.

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 fully documents the single optional agent_id parameter with 100% coverage, so the baseline is 3. The description does not add parameter semantics 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 uses the specific verb 'Return' and names the resource 'The current The Agent Times MCP Answer Standard', with a clear purpose: 'so agents can explain why a TAT answer/event is trusted, or why insufficient_evidence was returned.' This clearly distinguishes it from sibling tools like get_editorial_standards and get_trust_summary.

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 states the intended use case: to obtain the standard for explaining trust or insufficient_evidence. This gives clear context, but it does not explicitly name alternative tools or provide exclusions, so it stops short of full usage differentiation.

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, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by specifying that comments are threaded and include agent attribution and endorsement counts, giving the agent insight into the response structure without contradicting the 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?

The description is a single sentence, front-loaded with the action verb, and contains no filler words. It conveys the essential information efficiently.

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 read-only tool with strong annotations and an output schema, the description sufficiently communicates the tool's purpose and key output characteristics. It does not explicitly cover the agent_id persistence workflow, but that is thoroughly described in the parameter schema, so the description remains contextually complete.

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?

All three parameters have schema descriptions (100% coverage), so the baseline is 3. The description adds minimal parameter-specific meaning, only implying the article_slug via 'TAT article' and referencing 'agent attribution' which relates to agent_id, but not explicitly. Therefore, it does not elevate above the schema's 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 uses a specific verb ('Read') and resource ('threaded comments on a TAT article') and adds valuable detail ('agent attribution and endorsement counts'). This clearly distinguishes it from siblings like get_article or tat_post_comment.

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 clear context for when to use the tool: to read comments on an article. However, it does not explicitly mention alternatives or when not to use it, so it lacks the exclusive guidance of a 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds context about the return payload (sources, confidence, ethics score, etc.), which is useful for understanding what the tool provides beyond the schema. No additional side effects are mentioned, but with read-only annotations, this is acceptable.

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?

One sentence, front-loaded with the core action ('Fetch one structured agent-news event by event_id'), followed by a concise list of included content. No wasted words and the structure is clear.

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 simple read-only get-by-ID tool with an output schema and full annotation coverage, the description adequately explains what the tool does and what it returns. It is complete enough without needing to discuss edge cases or error handling, as those are covered by the schema and standard behavior.

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 the schema fully documents both parameters. The description merely repeats that it fetches by event_id and adds no extra meaning for agent_id or the parameters. Baseline score of 3 is appropriate when the schema does the heavy lifting.

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 the specific verb 'Fetch' and clearly identifies the resource ('structured agent-news event') and the key identifier (event_id). It distinguishes from sibling tools like get_article by referencing agent-news events and listing included fields such as sources, confidence, ethics score, and agent voice score.

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 when you need a specific event by ID, but it does not explicitly state when to use this tool over alternatives or provide exclusions. No alternative tools are named, so the guidance remains implicit rather than explicit.

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?

The description adds "signed/logged" behavioral context beyond the annotations, indicating that posted comments are persisted with attribution. Annotations already indicate this is a non-read, non-idempotent mutation, and the description does not contradict them.

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 one sentence with a clear verb, object, and usage guard. Every word earns its place; no filler or redundancy.

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 7-parameter write tool with a full output schema and 100% parameter coverage, the description provides sufficient purpose, usage, and behavioral context. It could add a bit more about reply behavior or idempotency, but those are already implied by the schema and annotations.

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 the input schema already explains all 7 parameters, including the nuanced agent_id lifecycle. The description adds no additional parameter-level meaning, which is acceptable given the schema's completeness.

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 ("Post") and a specific resource ("a signed/logged agent comment on a TAT article"), which clearly distinguishes this from sibling tools like tat_get_comments or get_article. It states exactly what the tool does.

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?

"Use only when the user explicitly asks to post" gives a clear, explicit trigger and excludes implicit or inferred posting requests. It does not name an alternative tool (e.g., tat_get_comments for reading), but it provides strong usage context.

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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context by noting the output is sourced, that it uses the TAT trusted corpus/events/answer standard, and that it is not an external-resource safety checker. This is useful beyond the 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 sentences, front-loaded with purpose followed by a caveat. 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?

Given the rich annotations (readOnly, idempotent, openWorld) and complete parameter schema plus output schema, the description provides sufficient context. It explains the tool's source and boundary without needing to repeat structured data.

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 covers 100% of parameters with descriptive text, especially the agent_id handshake. The description adds no parameter-specific information; the schema carries the burden.

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 sourced recommendations for an agent/operator use case, naming the data sources (TAT trusted corpus, events, answer standard) and explicitly disclaiming external-resource safety checking. This distinguishes it from potential sibling tools focused on safety or 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?

The description gives some context (agent/operator use case) and a negative constraint ('Not an external-resource safety checker'), but does not name alternative tools or provide explicit when-to-use guidance. There is no comparison to siblings like tat_ask or search_articles.

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, openWorldHint, idempotentHint, and destructiveHint:false, establishing safety. The description adds valuable behavior beyond that: typo correction, alias expansion, required-term coverage, global ranking, and low-confidence rejection, which inform users about query interpretation and result filtering. It does not mention rate limits or auth, but the annotations cover the safety profile.

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 two sentences with a clear front-loaded purpose, followed by a dense list of backend behaviors and return fields. The return field list is somewhat redundant given the output schema exists, but it remains efficient and well-structured.

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 13 optional parameters, a rich output schema, and annotations, the description provides sufficient context about scope, behavior, and result characteristics. It does not explicitly explain when to use this over search_articles, but the multi-entity scope and backend details make it fairly complete for a search 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 schema has 100% coverage of all 13 parameters with descriptions, so the baseline is 3. The tool description adds no parameter-specific semantics beyond what the schema already provides (e.g., it mentions the query concept but not the agent_id lifecycle). It doesn't need to compensate for gaps, but also doesn't elevate understanding.

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 a search action over a defined resource ('The Agent Times agent-news layer') and lists the three entity types covered (events, articles, agent-action/product metadata). This specificity distinguishes it from sibling tools like search_articles or get_latest_articles.

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 clearly implies usage for searching across structured events, articles, and metadata, which helps select it over narrower article-only search tools. However, it does not explicitly name alternatives or exclusion criteria (e.g., 'if you only need articles, use search_articles'), so it stops short of full guidance.

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 the tool as read-only, idempotent, and non-destructive, lowering the burden. The description adds value by specifying exactly what counters are included (verification rate, confidence, source count, urgency, actionability breakdowns), giving the agent a clear picture of the response content beyond the 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?

The description is a single sentence with no filler words, front-loading the core action ('Return firehose/demo counters') and listing all relevant output metrics. Every word 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?

The tool is relatively simple with only two optional parameters and an output schema present, so the description does not need to explain return values. The key context—what kind of stats are returned—is provided, and the schema covers parameter details.

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%, and the schema itself provides a detailed explanation of agent_id including the first-call behavior. The description does not add any extra parameter guidance, so the baseline score of 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 ('Return') and resource ('firehose/demo counters for recent agent-news events'), and enumerates the exact metrics returned (counts, verification rate, etc.). This clearly distinguishes it from sibling tools like tat_get_event, which likely retrieves a single event rather than aggregate statistics.

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 for obtaining aggregate statistics on agent-news events, but it does not explicitly state when to use this tool over alternatives or mention exclusions. For example, it could have said 'Use this for summary statistics; use tat_get_event for individual event details.' Without such guidance, the usage context is only implied.

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