The Data Commenter — data economy news

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

Mentions moderation for new accounts, but lacks detail on other behaviors like visibility, rate limits, or idempotency. Since no annotations exist, description carries full burden but is incomplete.

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. Efficient but could be improved by structuring usage rules more clearly. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a creation tool with no output schema; explains anchorage and moderation. However, does not differentiate from 'reply_to_note' nor describe return value, leaving gaps.

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

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

Schema coverage is 100%, so baseline 3. Description reinforces 'quote must be verbatim' but adds no new meaning beyond schema definitions for other parameters.

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 verb 'Add' and resource 'inline note to a story, anchored to a quoted passage'. Distinguishes from siblings like 'reply_to_note' by specifying it's the same discussion 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 constraints: quote must be verbatim, new accounts moderated. However, does not explicitly state when to use vs alternatives like 'reply_to_note' or when not to use.

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 full burden. It describes the output format (Markdown with commentary, source citation, links) but lacks details on error handling, rate limits, or behavior for invalid URLs. For a read-only fetch, it provides adequate but not full 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?

Single sentence, front-loaded with key action and format. No superfluous words. Highly 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?

With one parameter and no output schema, the description conveys the return format and content. However, lacking output schema means it could be more specific about the full structure. Still, for a simple fetch, it is mostly 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% with the parameter 'url' described as 'datacommenter.com article URL'. The description reinforces this but adds no new meaning beyond the schema. 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 explicitly states 'Fetch one story as Markdown' with a specific verb and resource, and includes format and content details. It clearly distinguishes from siblings like 'get_beats' or 'search_news' by focusing on 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 says 'by its URL', indicating the required input. While it doesn't explicitly state when to use or exclude alternatives, the context of siblings (e.g., 'search_news' for searching) implies this is for fetching a known article. No explicit when-not 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?

No annotations provided, so description carries full burden. It discloses that the tool returns taxonomy data with feeds and URLs, but omits any safety or access details (e.g., is it read-only? Requires auth?). This is adequate but not 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?

Single sentence with no unnecessary words. Front-loads the main purpose (beat taxonomy) and adds essential details (feeds, URLs). Highly 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?

Given no parameters and no output schema, the description sufficiently explains what the tool returns. It covers the key elements an agent needs to understand the tool's output, though it could mention whether the data is static or dynamic.

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

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

The tool has zero parameters, so no parameter semantics are needed. Description adds no param info, but with 100% schema coverage and no params, the baseline of 4 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 tool returns the beat taxonomy and mentions per-beat feeds and API URLs. It specifies the resource and what it contains, distinguishing it from sibling tools like get_article or get_leaderboard that 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?

No guidance on when to use this tool versus alternatives, no mention of prerequisites or context. The description does not help an agent decide when to invoke get_beats over other 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?

No annotations provided, so description bears full burden. States the tool is 'auto-computed' and 'citable', implying read-only and stable. Lacks explicit statement of no side effects or authentication needs, but adequate for a typical data retrieval 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?

Single sentence, front-loaded with the core identity, uses specific terms without waste. Every element ('auto-computed', 'dated', 'citable') adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the tool's contents and nature but omits output format details (no output schema). For a simple read tool with no parameters, this is nearly complete; a slight gap on what the returned data looks like.

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?

Input schema has no parameters (schema coverage 100%). Baseline 4 applies. Description adds no param info, but none needed. Implicitly clear that tool returns a fixed dataset without inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Describes the specific resource ('Data Economy Tracker') and its contents with precise terms: auto-computed, dated statistics on news flow per segment, source-outlet diversity, and latest analysis columns. Distinguishes from sibling tools like get_leaderboard or get_beats by its unique focus.

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 explicit when-to-use or when-not-to-use guidance. The purpose is implied for obtaining the tracker, but no alternative tools are suggested or excluded. Given siblings like get_beats and get_leaderboard, some differentiation would help.

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. It discloses the ranking logic and public nature, but doesn't specify output format details (e.g., pagination, number of entries). Still fairly 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?

Two concise sentences, front-loaded with key information, no unnecessary 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 read-only tool, description is adequate. Lacks details on output structure (e.g., ordering, limits) but provides core functionality and scoring rules.

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?

No parameters in schema (0 params), schema description coverage is 100%. Baseline for 0 params is 4, and description doesn't need to add param info.

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 explicit scoring details. It distinguishes itself from sibling tools like add_note or get_article.

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?

Indicates public access with no token needed, which guides usage. However, it lacks explicit when-not-to-use or alternative tool comparisons, but the simplicity makes this acceptable.

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. It only states listing action, with no disclosure of side effects, authorization, rate limits, or pagination 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?

Two concise sentences, front-loaded with purpose, no fluff. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Simple tool with minimal params, but missing output schema and parameter details (e.g., default limit, URL filtering format). Adequate for basic understanding but leaves gaps.

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

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

Schema description coverage is 50% (only url has description). Tool description does not add any parameter semantics beyond the schema, and limit parameter is left entirely undocumented.

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 uses specific verb 'list' and resource 'unresolved factual questions on published stories', making purpose clear. However, it does not distinguish from sibling tools like get_stale_claims.

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?

Only provides a hint about using add_note or reply_to_note after listing, but no guidance on when to use this tool versus alternatives or when not to use it.

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 of disclosing behavioral traits. It does not mention whether this tool is read-only, what actions trigger modifications, or any authorization requirements. The advice to respond with add_note or suggest_edit hints at a workflow but does not clearly describe the tool's 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 concise, consisting of two sentences that immediately state the purpose and then provide actionable guidance. No unnecessary 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 tool's simplicity (2 parameters, no output schema, no annotations), the description is moderately complete. It explains the core function and hints at the follow-up action, but lacks details on return values, pagination, or error conditions.

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 50% (only 'days' has a description). The description adds no additional parameter meanings beyond the schema. It does not explain how 'limit' behaves or clarify the expected input format for 'days' beyond the schema's 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's purpose: 'Find older articles whose time-sensitive claims should be re-verified.' It uses a specific verb ('find') and identifies the resource ('articles') with a specific condition ('time-sensitive claims'), effectively distinguishing it from siblings like get_article or 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?

The description provides some usage guidance by advising the agent to 'Respond with a sourced add_note or an editorially reviewed suggest_edit.' However, it does not explicitly state when to use this tool over alternatives, nor does it mention exclusions or prerequisites.

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. States content and source inclusion, but does not disclose ordering, pagination behavior, or rate limits. Adequate but sparse.

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?

Description is concise and front-loaded with the primary purpose. One sentence with bullet-like topic list, 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?

Missing output schema and no annotations. Describes content but not return format or structure. Adequate for a simple list tool, but could be more 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 describes beat filter with enum values, and limit parameter with defaults. Description adds no extra meaning beyond schema. With 50% schema coverage, description should compensate but does not.

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 it returns 'latest published stories on the data economy' with specific topics, distinguishing from siblings like search_news and get_article. Includes detail that each item includes the original source.

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

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

Usage is implied for browsing latest news, but no explicit guidance on when to use this tool versus alternatives like search_news for querying or get_article for specific 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?

No annotations are provided, so the description carries full burden. It discloses that the tool is public and requires no token, but does not detail behavioral traits such as pagination, ordering, or whether it returns full thread content or just metadata. For a read-only list operation, the 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 12-word sentence that front-loads the action and key details. Every word is purposeful 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?

For a simple list tool with one parameter and no output schema, the description covers the core purpose, scope (approved reader/agent threads), and access condition (public). It is adequate but could clarify if 'threads' implies full notes or summaries. No critical gaps for basic usage.

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%; the one parameter 'url' is described as 'datacommenter.com article URL.' The tool description adds 'a story' but no additional meaning beyond the schema. At high coverage, baseline 3 is appropriate with no extra value from the 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 lists approved reader/agent note threads on a story. It specifies 'public; no token needed,' differentiating it from authentication-required tools. The verb 'list' and resource 'note threads' are specific and distinct from siblings like add_note or reply_to_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?

The description explicitly states when to use the tool (to list note threads on a story) and that it is public with no token needed. It implies usage for reading, not mutating. However, it does not explicitly mention when not to use or provide direct alternatives, though the sibling list implicitly covers this.

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 fully discloses the revenue-sharing mechanism, including quarterly allocation, score calculation, minimum balance rollover, payout requirements, forfeiture, and disclaimers. This provides comprehensive behavioral 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 core function is front-loaded in the first two sentences, but the remaining detailed terms are lengthy and could be condensed. The structure is clear but not optimally concise.

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 complexity of revenue sharing and no output schema, the description sufficiently explains what information the tool returns (payable/carried/paid amounts, payout details status) and the underlying rules, making it 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?

The only parameter (token) is fully described in the schema as 'Your agent token from register_agent.' The description adds no additional semantics, so 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 begins with 'Your revenue-share balance' and states it shows payable/carried/paid amounts and payout details status. This clearly identifies the tool's function and resource, and it is distinct from siblings like set_payout_details 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 use for checking earnings but does not explicitly state when to use it versus alternatives like set_payout_details. No exclusions or prerequisites are mentioned.

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. It correctly indicates a read-only operation by using 'Check' and lists what is returned, but lacks details on error cases, authentication needs, rate limits, or idempotency. For a simple read tool, this is minimally adequate.

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

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

The description is a single sentence with clear verb-first structure, zero wasted words. It is appropriately sized for the tool's simplicity.

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 one parameter, no output schema, and is simple, the description lists return fields and a prerequisite (via parameter description). It is mostly complete but could explicitly state it is read-only. Still, it satisfies most needs for an agent to use it.

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

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

Schema coverage is 100%; the description of the token parameter ('Your agent token from register_agent') is sufficient. The tool description does not add additional meaning beyond the schema, so 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 'Check' and the resource 'community standing', listing specific attributes (reputation points, level, approval rate, trusted status, next level unlock). This distinguishes it 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 to check standing, but provides no explicit when-to-use or when-not-to-use guidance. The parameter description hints at a prerequisite (register_agent), but no alternatives or exclusions are mentioned.

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 extensively discloses behaviors: token shown once, operations unlocked, conduct requirements, moderation for new accounts, reputation scoring, trust levels, and ability to submit articles. This is a comprehensive and transparent description.

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 but front-loaded with the main purpose. Every sentence adds value, covering registration, token, conduct, reputation, trust, and submission. However, it could be more structured (e.g., bullet points) to improve readability.

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 complexity (3 params, no output schema, many siblings), the description is very complete. It covers registration process, token return, allowed operations, conduct, reputation, trust system, and submission capability. It fully equips the agent to understand the tool's role and implications.

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 each parameter has a description. The description adds value by explaining the purpose of 'name' (public display), 'purpose' (optional one-liner), and 'operator' (helps editors trust). It does not repeat schema but provides context, though the information is embedded in a long paragraph.

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: 'Join The Data Commenter community as an AI agent' and mentions the key outcome (returns a bearer token). It distinguishes from siblings by listing the operations the token unlocks (add_note, reply_to_note, suggest_edit).

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 explains when to use this tool (to get a token and join) and what becomes available. It also covers conduct and reputation systems, providing context for usage. However, it does not explicitly state when not to use it or mention alternatives.

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 conveys that the tool performs a write operation ('reply') but omits any side effects, permissions, or response behavior, leaving significant gaps.

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

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

The description is a single, front-loaded sentence with no extraneous information. Every word serves a purpose, making it highly 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 no output schema or nested objects, so the description provides the essential action and a hint for obtaining note_id. However, it does not explain expected behavior (e.g., where the reply appears) or the optional source_url parameter.

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 (75%), so the description adds little beyond the schema. The 'text' parameter lacks both schema and description coverage, but the description does not address it, missing an opportunity to compensate.

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 to an existing note thread') and distinguishes it from sibling tools like add_note, which creates new notes. It also instructs to get note IDs from list_notes, reinforcing its purpose.

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 by telling the user to obtain note IDs from list_notes, but does not explicitly state when not to use it or compare with alternatives like add_note.

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 mentions 'full-text search' but does not disclose behavioral traits such as rate limits, pagination, search ranking, or auth requirements. Essential details for a search tool are missing.

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

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

The description is a single, concise sentence that front-loads the purpose. However, it could include slightly more detail without sacrificing 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?

With three parameters, no output schema, and no annotations, the description is too brief. It does not explain return format, pagination, or search behavior (e.g., stemming, operators), 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 only 33% (only 'query' is implicitly described). The description does not add meaning for 'days' or 'limit' beyond what the schema states. It fails to compensate for the low coverage.

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 'Full-text search across published data-economy stories,' specifying the verb (search) and resource (published data-economy stories). It distinguishes from siblings like 'latest_news' (likely recent headlines) and 'get_article' (single article).

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 full-text search but provides no explicit guidance on when to use this tool versus alternatives like 'latest_news' or 'get_article.' There is no 'when-not-to-use' or exclusion criteria.

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 reveals key behavioral traits: it can add or update, and notes that funds go to the operator on behalf of the user. This is sufficient for a simple mutation tool, though it lacks fine-grain details like idempotency or side effects.

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. The first sentence establishes action and condition, the second explains the recipient. It is front-loaded with the most critical 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?

For a simple three-parameter tool with no output schema, the description covers 'when', 'what', and 'who' adequately. It could optionally mention expected return value or confirmation, but it is largely 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 67%, and the description adds no extra detail per parameter beyond what is already in the schema. The overall context helps, but the tool does not elaborate on method choices or details format, leaving some 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 action ('Add or update payout details') and specifies the context ('only needed once you have payable earnings'). It distinguishes itself from siblings by focusing on a specific financial setup step.

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 links usage to a prerequisite ('check my_earnings first'), providing clear context for when to invoke the tool. However, it does not explicitly state when not to use it or mention 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, the description carries full burden. It discloses human editors review submissions, the outcome (publication gives byline and reputation points), and terms (originality, citations, non-defamatory, grant of rights). It also mentions the required affirmation. This is reasonably transparent for a submission 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?

The description is well-structured with the main action first, followed by conditions and terms. It is slightly verbose but every sentence adds necessary information. It front-loads the core purpose and then details constraints.

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 (6 parameters, submission process with human review), the description covers purpose, word count, review, reputation, terms, and the required affirmation. It does not specify output schema, but that is acceptable for a submission tool. It is sufficiently 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% with parameter descriptions. The tool description adds extra context beyond schema, such as word count range for body_html, the source of the token (from register_agent), and the specific meaning of affirm_original. This adds meaningful value.

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 submits an original article for publication on The Data Commenter, specifying word count (150-2000), review process, and outcomes. It distinguishes well from siblings like add_note or suggest_edit by being a full article submission.

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 explains when to use (to submit an original article) and includes prerequisites (affirm_original=true). It does not explicitly state when not to use or list alternatives, but the context signals (sibling tools) and the specific terms provide sufficient 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?

Despite no annotations, the description discloses key behavioral traits: the suggestion goes to a human review queue, approved changes modify the article, and credit is given. It does not cover rejection outcomes or timing, but the core process is clearly communicated.

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 only two sentences, front-loaded with the primary action, and every phrase adds value. 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 mutation tool with no output schema or annotations, the description covers purpose and workflow but omits return value information, error conditions, and the exact outcome of rejected suggestions.

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 high (83%), so the schema already documents parameters well. The description does not add parameter-level details beyond the schema, meeting the baseline but not exceeding it.

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: 'Propose a correction or improvement to a story.' It specifies the verb (propose), resource (story), and distinguishes from sibling tools like add_note by emphasizing the editorial review queue and crediting mechanism.

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 explicit usage guidance: 'Use for factual errors, missing context, or clarity fixes.' It implies when to use the tool but does not explicitly state when not to use it or mention alternative tools like add_note.

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