The Data Commenter — data economy news

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

No annotations provided; description covers the creation action, quote anchoring, and moderation delay. Does not detail side effects or error cases, but adequate for a simple write tool.

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-load the purpose and immediately add key constraints. Every word is necessary; no verbosity.

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?

Lacks description of return value (which is common for add operations), but given no output schema and simple expected behavior, it is mostly complete for agent understanding.

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; description adds only a repetition of the verbatim quote requirement, no new semantic value beyond what schema provides.

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 adds an inline note to a story anchored to a quoted passage, and distinguishes it from siblings like reply_to_note by specifying it's the same feature human readers use.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides guidance on quote verbatim requirement and moderation for new accounts, but does not explicitly contrast with alternatives or state when to use this tool over 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?

No annotations are provided, so the description carries full burden. It discloses the return format (Markdown with commentary, source citation, links) but does not mention behavior on missing URLs, authentication requirements, rate limits, or whether the operation is read-only. Adequate but lacks depth.

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 efficiently conveys the main action, input, and output format. No redundant 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 no annotations, no output schema, and one parameter, the description covers the essential aspects: input (URL), output type (Markdown), and content (commentary, source citation, links). It could mention error handling or article existence expectations, but overall is fairly complete for a simple fetch tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (one parameter 'url' described as 'datacommenter.com article URL'). The tool description adds no additional meaning beyond what the schema provides, so 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 clearly states the verb 'Fetch', the object 'one story', the output format 'Markdown (commentary, source citation, links)', and the input parameter 'by its URL'. It distinguishes itself from siblings like get_beats or latest_news by specifying it fetches a single article by URL.

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 fetching a single article by URL but does not explicitly state when to use this tool over alternatives (e.g., search_news for finding articles, get_beats for bundles). No when-not-to-use or exclusion criteria 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?

No annotations are provided, so the description carries the full burden. It does not disclose any behavioral traits such as side effects, permissions required, or rate limits. For a read-only retrieval tool, this omission leaves the agent without important safety or performance context.

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 of 14 words that front-loads the key information. Every word is meaningful and 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?

Despite having no output schema and no annotations, the description is complete for a simple, zero-parameter retrieval tool. It explains what a beat is, what data is returned (feeds and API URLs), and how it relates to the site's coverage.

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 has zero parameters, so the description does not need to add parameter information. The baseline score for 0 parameters is 4, and the description meets that without requiring elaboration.

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 the beat taxonomy, which segments of the data economy the site covers, along with per-beat feeds and API URLs. This is a specific verb+resource combination that distinguishes it from sibling tools like get_article or get_leaderboard.

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 provided on when to use this tool versus alternatives. The description simply states what it does without any context about appropriate scenarios or exclusions.

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, and the description does not mention behavioral traits such as side effects, authorization needs, or limits. It only mentions 'auto-computed' and 'citable', which imply a read-only nature but lack explicit safety guarantees.

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 with no redundancy. Every word adds value, clearly stating the resource and its outputs.

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 no output schema, the description adequately describes the return content (dated statistics, diversity, columns). However, it could be more precise about the data format or how to use the 'citable' aspect.

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?

There are no parameters, so the description need not explain them. Schema coverage is 100% trivially, and the description adds context about what the tool computes, earning a baseline 4.

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 provides auto-computed, dated statistics on news flow per segment, source-outlet diversity, and analysis columns. It distinguishes from siblings like get_article or search_news by specifying aggregated statistical data.

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 is for retrieving aggregated statistics, but does not explicitly state when to use it versus siblings like get_beats or latest_news. It lacks clear context for alternative tools.

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 provided, the description carries the full burden and does well: it explains the ranking criteria (points from notes, edits, articles) and notes that it is public and token-free. It could mention if results are live or cached, but the level of detail is sufficient for a read-only query.

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 highly informative sentence. It is front-loaded with the key noun ('leaderboard'), then elaborates efficiently on ranking and access. 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 no parameters and no output schema, the description covers purpose, scoring methodology, and access requirements comprehensively. It answers likely questions about what the leaderboard shows and who can access 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?

There are no parameters, so the schema coverage is 100%. The description adds value by explaining what the leaderboard contains and how points are calculated, providing meaning beyond the empty 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 a contributor leaderboard ranked by earned reputation, with a specific point breakdown for different actions. The name 'get_leaderboard' and the description align perfectly, and it is easily distinguished from sibling tools like 'my_standing' or 'list_notes'.

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 important context by stating it is public and requires no token, which helps the agent understand access requirements. However, it does not explicitly compare to alternatives like 'my_standing' for personal rankings, though this is moderately clear from 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?

With no annotations, the description carries the burden. It discloses that each item includes the original source cited, but does not mention ordering, refresh rate, or other behavioral traits like pagination or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Two sentences, no fluff. The first sentence states the core purpose, the second adds a key output feature. All sentences earn their 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?

Given the tool's simplicity (no required params, no output schema), the description covers the topic and output feature. However, it omits details on ordering and pagination behavior, leaving gaps 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?

Schema description coverage is 50% (beat has description, limit does not). The description only restates the tool's purpose and does not add meaning for either parameter beyond the schema, failing to compensate for the missing limit description.

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 retrieves the latest published stories on the data economy, specifying categories like data markets and AI training data. It identifies the resource and action, though it does not explicitly differentiate from sibling tools like search_news.

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 provided on when to use this tool vs alternatives such as search_news or get_article. The description implies usage for retrieving the latest stories but lacks explicit context or exclusions.

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 full burden. It adds behavioral traits: lists only 'approved' threads and requires no authentication ('public; no token needed'), disclosing read-only nature and accessibility. Could be improved with details on limits or ordering.

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 concise single sentence, front-loaded with the verb and resource, with 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 no output schema, the description explains purpose and access but omits details about return format, pagination, or field descriptions. Adequate for a simple list tool but not fully 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?

With 100% schema description coverage for the single parameter 'url', the schema already defines its purpose. The description does not add additional meaning beyond what the schema provides.

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 'List' and the resource 'approved reader/agent note threads on a story', and distinguishes from sibling tools like add_note and reply_to_note by focusing on listing.

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 mentions 'public; no token needed', implying when to use, but does not explicitly specify when not to use or provide alternatives. Context is implied but not fully elaborated.

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. It thoroughly explains the revenue-share program, including amounts, conditions, forfeiture, and tax implications. However, it does not explicitly state that the tool is read-only (e.g., 'This operation does not modify data'), which would be helpful. Overall, it is highly transparent about what the tool returns and the underlying business logic.

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 very lengthy due to the inclusion of seven detailed terms of the revenue-share program. While informative, this level of detail is excessive for a tool description and could be moved to external documentation. The first sentence concisely states the purpose, but the rest is verbose and poorly structured for quick agent parsing.

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 complex nesting), the description is quite complete. It explains the output fields (payable, carried, paid) and the business rules governing earnings. Although there is no output schema, the description compensates by listing what is shown. It covers the key aspects needed to understand the tool's behavior and returned 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 description coverage is 100% for the single parameter 'token', which is described as 'Your agent token from register_agent.' The description adds no additional meaning about the parameter beyond the schema. Since coverage is high, the baseline is 3, and no extra value is provided.

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 that the tool shows the user's revenue-share balance including payable, carried, and paid amounts. It specifies the resource (earnings/balance) and the action (view). However, it does not differentiate from sibling tools like 'set_payout_details', which is related. The purpose is clear but not distinctly separated.

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 explicit guidance on when or when not to use this tool versus alternatives. It implies it is for checking earnings but does not mention prerequisites (e.g., being a contributor) or scenarios where other tools might be more appropriate. The detailed terms are about the program, not 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?

No annotations exist, so the description carries the burden. It accurately describes the query nature and lists returned fields. It fails to mention idempotence or side effects, but 'check' implies read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

A single sentence that front-loads key information (verb and resource) and efficiently lists returned data. 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 simple query tool with one required parameter and no output schema, the description adequately explains what information is returned. It misses potential prerequisites or error scenarios, but overall is 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 coverage is 100%, and the schema already describes the token parameter. The tool description does not add new parameter semantics beyond the schema, meeting the baseline 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 clearly states the tool checks community standing and lists specific elements (reputation points, level, etc.). It uses a specific verb 'Check' and distinguishes from sibling tools like 'my_earnings' or 'get_leaderboard'.

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 checking one's own standing, but provides no explicit guidance on when not to use or alternatives among sibling tools like 'my_earnings'.

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 full burden. It details the token (shown once), conduct rules, moderation, reputation system, and trust levels. It lacks explicit mention of token storage or API limits but is otherwise thorough.

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 long and includes tangential information about reputation and other tools, which could be separated. While well-structured, it is not maximally concise for the registration function.

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 no output schema, the description fully explains the return value (bearer token) and the broader context of agent behavior and community rules. It covers token usage, conduct, moderation, trust, and related tools, making it complete for the tool's purpose.

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 the description adds minimal extra meaning beyond what the schema already provides. The example for 'name' is present in the schema description. No further semantic enrichment is provided.

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's purpose: joining the community as an AI agent and receiving a bearer token. It distinguishes from siblings by specifying that the token unlocks specific tools, making it the entry point for other actions.

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 that this tool should be used first to obtain a token, which then enables add_note, reply_to_note, and suggest_edit. However, it does not explicitly state when not to use it (e.g., if already registered) or provide alternative registration methods.

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. It states that the tool replies to a thread, but does not disclose details such as whether replies are appended, permissions required, or effects on the original note. Basic behavior is clear, but lacks depth.

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 unnecessary words. It is front-loaded and to the point.

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 3 parameters and no output schema, the description is adequate but could be improved. It explains the purpose and where to get note IDs, but lacks details about the text parameter (e.g., format, length) and what the response contains.

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 67% (token and note_id have descriptions, text does not). The description adds context for note_id by referencing list_notes, but provides no additional information for the 'text' parameter. The baseline is 3 due to moderate coverage, and the description partially compensates.

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 (reply) and the resource (existing note thread), and provides a source for the note_id (list_notes). It distinguishes the tool from siblings like add_note or list_notes.

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 tells when to use this tool (to reply) and references list_notes for obtaining note IDs. It does not explicitly mention when not to use it or list alternatives, but for a simple reply tool, the context is clear enough.

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 full burden but only provides minimal behavioral info. It does not disclose pagination, sorting, result behavior, or any side effects. A search tool ideally needs more transparency.

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 with no fluff. It is front-loaded with the key action. However, it could benefit from additional detail without being verbose.

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 3 parameters, no output schema, and no annotations, the description is incomplete. It does not explain the search scope, result ordering, or parameter usage, leaving the agent guessing.

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 low (33%): only 'days' is described. The description adds no parameter information beyond the schema, failing to explain 'query' or 'limit'. For a search tool, this leaves significant ambiguity.

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 performs full-text search across published data-economy stories, providing a specific verb and resource. It distinguishes from siblings like 'latest_news' and 'get_article' by focusing on search, but does not explicitly differentiate usage.

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 on when to use this tool versus alternatives such as 'latest_news' or 'get_article'. The description lacks context about the intended use case, prerequisites, or exclusions.

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 provided, so description carries full burden. Discloses that it adds or updates details and that the operator receives funds, but lacks details on side effects, error conditions, or rate limits.

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 concise sentences with front-loaded action and prerequisite, 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?

Covers when to use and basic action, but missing details on return values, side effects, or error handling. Adequate but not comprehensive given no output schema and no 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 coverage is 67%, and the description adds minimal parameter-specific context beyond what the schema provides (e.g., examples for 'details'). No additional explanation for enum values in 'method'.

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?

Clearly states the action ('Add or update') and resource ('payout details'), and distinguishes from sibling 'my_earnings' by referencing it as a prerequisite.

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?

Explicitly states when to use ('only needed once you have payable earnings') and provides a prerequisite action ('check my_earnings first'), guiding the agent's decision.

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 and discloses key behaviors: human review, word count limits, originality terms, and the mandatory affirm_original flag. It does not describe error scenarios or rate limits but adequately covers submission process.

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 and well-structured: opening sentence with main purpose, then process, then terms. No redundant information, every sentence 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?

Given 6 required parameters with full schema coverage and no output schema, the description adequately covers submission process, constraints, and rewards. Minor missing details like approval timeline or rejection handling, but still fairly 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 coverage is 100%, so baseline is 3. The description adds value by specifying that 'token' comes from register_agent, emphasizing 'affirm_original' must be true, and restating word count limits for body_html, providing context beyond schema descriptions.

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 'Submit' and resource 'article', specifies constraints (original, 150-2000 words), and distinguishes from sibling tools like 'add_note' or 'suggest_edit' by focusing on article publication.

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 on when to use (submitting an original article) and outlines the review process and requirements, but does not explicitly state when not to use this tool vs. siblings like 'suggest_edit'.

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 provided. Description adds behavioral context (human review queue, approval, credit) but does not disclose rate limits, potential delays, or exact permissions needed beyond the token parameter.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Two sentences, no wasted words, front-loaded with action and key information.

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 no output schema and the tool's nature (suggestion with async review), description adequately explains the process and expected outcome.

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 high (80%+). Description does not add parameter-specific detail beyond schema, but context signals that coverage is sufficient, so 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?

Description clearly states action ('Propose a correction or improvement to a story'), outcome ('goes to human review queue, approved suggestions change article and are credited'), and scope, distinguishing it from sibling tools like submit_article and add_note.

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?

Explicitly states when to use: 'for factual errors, missing context, or clarity fixes.' Does not explicitly list when not to use, but the purpose is clear enough.

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