chess

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

Annotations (readOnlyHint, idempotentHint, etc.) are consistent with the description. The description adds valuable behavioral context: cost implications for Anthropic, default free model, and return format. 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 concise, well-structured, and front-loaded. Every sentence adds necessary detail without redundancy. It efficiently conveys the tool's purpose, usage, and behavior.

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

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

For a tool with no output schema, the description adequately explains return values (per-model objects with score, confidence, etc.) and combined view. It also covers the optional nature of models and API key, making it complete for its complexity.

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%, but the description enhances understanding by explaining the default model ('Workers AI Llama-3.3-70b (free)') and the purpose of the API key. This adds value beyond the schema.

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

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

The description clearly states it probes LLMs for knowledge about an entity and returns a visibility score, with specific use cases like AI-marketing audits. It distinguishes from sibling tools by focusing on LLM knowledge scoring rather than direct querying or other analyses.

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 contexts for usage (AI-marketing audits, brand checks, competitive monitoring) and explains the default vs paid model. However, it does not explicitly state when not to use this tool or compare it to alternatives like scan_competitor_ai_presence.

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 indicate read-only, idempotent, safe; description adds context about routing to 3745 tools and returning URIs, without contradiction.

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?

Multiple sentences but each adds value; front-loaded with key instruction; slightly long but justified by complexity.

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 usage, examples, scope; does not detail return format extensively but implies structured answer with citations, which is adequate given no output 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 has 100% coverage with descriptions; description adds examples and trigger phrases that clarify what constitutes a good question, beyond the schema.

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

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

The description clearly states it queries authoritative structured data from many sources (SEC, FDA, etc.) and routes questions to internal tools, distinguishing it from web 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?

Explicitly prefers over web search, lists trigger phrases and examples, but does not mention when not to use it or compare to sibling tools like ask_pipeworx_grounded.

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 indicate safe reads (readOnly, idempotent). Description goes beyond: details refusal reasons and extra LLM cost, adding valuable 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?

Front-loaded with key purpose and guidelines. Dense but each sentence contributes. Slightly verbose with refusal reasons list, but still 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?

Describes return structure (answer, evidence, etc.) and refusal reasons despite no output schema. Complete for a high-stakes QA tool with complex 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 coverage is 100%; description mentions 'question' but adds minimal new meaning beyond schema aliases. Baseline 3 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?

Clear verb+resource: 'hallucination-resistant answer mode' for high-stakes reads. Specifically distinguishes from sibling ask_pipeworx.

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 (high-stakes, cited answers) and when not (casual lookups, prefer ask_pipeworx).

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, non-destructive. The description adds extensive behavioral context: fan-out mechanism, response shapes, resolver contract, parent event extractor, news fallback handling, safety short-circuits, wide-spread warnings, and resolution-rule risk. 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 very long (632 words) and front-loaded with purpose, but includes extensive detail on classifiers, fan-out examples, response shapes, and safety mechanisms. While well-structured, it could be more concise for an AI agent.

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 no output schema, the description comprehensively covers all return fields (market, analysis, evidence), resolver contract, parent event, news fields, and safety mechanisms. It provides complete context for an AI to use the tool effectively.

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 descriptions for all three parameters. The description adds extra context for 'depth' (default thorough) and 'include_raw' (size considerations), providing meaning 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?

Clearly states verb 'Research' and resource 'Polymarket bet', specifies input types (slug, URL, question), and distinguishes from sibling polymarket tools by emphasizing evidence gathering and fan-out to category-specific data packs.

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 use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'), but does not mention when to avoid this tool or use an alternative sibling tool.

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, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds specific data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), mentions handling of off-calendar fiscal years, and states results are sorted by primary metric. 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?

Description is comprehensive but somewhat verbose (6 sentences, 124 words). Front-loaded with trigger phrases, but could be more concise. Every sentence adds value, but some details (e.g., 'off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan') could be shortened.

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?

No output schema, but description mentions 'Returns paired data + pipeworx:// citation URIs per entity.' This gives a reasonable idea of output. Given the tool's moderate complexity and rich annotations, the description is fairly complete for an AI agent to understand 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 coverage is 100%, so baseline 3. Description adds meaning: explains type enum values ('company' and 'drug'), gives examples of valid values for values array (tickers for company, drug names), and clarifies min/max items. This adds value 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?

Description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs in a single call, with specific trigger phrases like 'compare X and Y, 'X vs Y', etc. It distinguishes from siblings by noting it replaces 8-15 sequential lookups, and explicitly contrasts with 'sequential single-pack lookups'.

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 explicit guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Gives example queries. Does not explicitly state when not to use (e.g., for single entity details), but context from sibling tools (entity_profile) implies that.

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?

Beyond annotations (readOnly, idempotent, etc.), the description reveals decomposition into sub-questions, parallel routing, evidence with confidence/source, and explicit gaps. No contradictions 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?

Description is detailed but not verbose; front-loaded with core value. Each sentence adds useful information, though length could be slightly trimmed without losing content.

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 all key aspects: account requirement, plan limits, expected time (15-60s), output structure (structured findings packet, gaps). No output schema exists, but description sufficiently explains return format.

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 both parameters fully (100%). Description adds meaningful context: depth enum explained as number of facets (quick=3, standard=5, thorough=8) and that question can be broad/multi-part.

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

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

The description clearly states it performs 'grounded multi-source research in ONE call' by decomposing questions and routing to 3,745 tools. It distinguishes from sibling ask_pipeworx by noting 'use ask_pipeworx for single lookups'.

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 provides when to use (broad or multi-part questions) and when not (single lookups), with a named alternative (ask_pipeworx). Also notes account and plan requirements.

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. The description adds beyond annotations: it explains that the tool returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and that results are 'ready to call directly, no second schema lookup needed.' 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 long, front-loaded with the action and resource. Every sentence serves a purpose: first states what it does, second provides usage guidance and return value details. No fluff.

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

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

Given the tool's discovery nature and the rich annotations, the description covers everything needed: purpose, when to use, what it returns, and examples. No output schema exists, but the description explains the return format sufficiently.

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 documented. However, the description adds examples of queries ('analyze housing market trends'), mention of default limit (20) and max (50), and clarifies that multiple aliases map to query. This adds meaning beyond the schema's minimal 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 starts with 'Find tools by describing the data or task,' which is a clear verb+resource statement. It enumerates specific domains (SEC filings, FDA drugs, etc.) and distinguishes from siblings by noting it returns schemas ready to call, making the purpose unmistakable.

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 advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear context for when to use the tool, though it does not mention 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context: it fans out across multiple sources, mentions the USPTO PatentsView API sunset (soft-fails), and specifies the order of preference for news sources (GDELT→GNews fallback). 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 front-loaded with example queries and then provides detailed specifications. While a bit lengthy, every sentence adds value, explaining data sources, return fields, and edge cases (API sunset). It is well-structured and informative.

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 that there are only two required parameters and no output schema, the description fully explains what the tool does, what it returns (cik, company_name, recent_filings, fundamentals, patents, news, LEI), and its limitations (names not supported, patent soft-fail). No gaps left for the 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 coverage is 100%, so parameters are fully documented. The description adds semantics beyond the schema: type is limited to 'company', value must be ticker or zero-padded CIK, and explicitly states that names are not supported. This clarifies the input format and constraints.

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 a full cross-source profile of a US public company, listing specific data sources and outputs (SEC, XBRL, USPTO, news, GLEIF). It distinguishes itself from sibling tools like resolve_entity by specifying input requirements (ticker or CIK, not names).

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 says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clearly instructs to pass ticker or CIK, and notes that names are not supported, directing users to use resolve_entity first. This provides excellent when-to-use and when-not-to-use 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 indicate destructive and idempotent nature. Description adds usage context but no additional behavioral details beyond confirming deletion. No contradiction.

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, no wasted words. Highly 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?

With one simple required parameter, no output schema, and annotations covering behavioral traits, the description is complete enough for an agent to use 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?

Schema description covers the single parameter 'key' at 100% coverage. Description does not add information beyond what the schema already 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 'Delete a previously stored memory by key.' It provides a specific verb (delete) and resource (memory), and distinguishes itself from sibling tools remember and recall.

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: 'when context is stale, the task is done, or you want to clear sensitive data' and recommends pairing with remember and recall.

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 (readOnly, idempotent, non-destructive) align with description of fetching and extracting. Description adds that it outputs a text blob for a specific file location, providing full 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?

Two sentences: first defines the action, second lists use cases. No redundant 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?

No output schema, but description explains output format and location. Parameters are well-covered by schema. Sufficient for a straightforward 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%, so baseline 3. Description adds value by explaining that max_links controls link entries (though not explicitly named, the link extraction context implies 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?

Clearly states it generates a production-ready llms.txt for any URL, with specific actions (fetch, extract, emit). Distinct from any sibling tool.

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 lists three use cases (client indexing, own project, competitor audit). No exclusion criteria but contextually clear.

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, so the safety profile is covered. The description adds value by specifying the month format (YYYY/MM) and listing return values, which is behavioral context 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 two sentences, front-loaded with the purpose, and every word adds value. 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's simplicity (3 parameters), full schema coverage, annotations, and an output schema, the description is complete. It explains the input format and output fields, leaving no ambiguity about what the tool does.

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 coverage, baseline is 3. The description adds meaning by specifying the month format (YYYY/MM) and giving an example, which goes beyond the schema's description of month as a number. It also states what the tool returns, aiding in parameter 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 the verb 'Retrieve' and the resource 'a player's completed games for a specific month', with specific return fields (URLs, time controls, results, ratings). It distinguishes from sibling tools like get_leaderboards or get_stats by specifying the month and game details.

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 when to use (for a specific month) but does not explicitly say when not to use or mention alternatives. However, the context is clear and the sibling tool list implies other tools exist for different purposes.

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, etc. The description adds that it returns rankings with ratings and win percentages, providing some behavioral context. However, it does not mention any limitations or side effects beyond what annotations cover.

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, efficient and front-loaded. No wasted words, but lacks structural elements like examples or usage notes.

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 zero parameters and an output schema, the description provides minimal but sufficient context. However, the format filtering is not explained (how to specify format), leaving a 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?

No parameters exist, so baseline is 4. However, the description mentions filtering 'by format' (daily, rapid, blitz, bullet), which implies parameterization not reflected in the input schema (empty properties). This inconsistency reduces clarity.

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 resource (top-ranked Chess.com players) and the action (check), implying retrieval. It distinguishes from sibling tools like get_player and get_stats by focusing on rankings and formats. However, the verb 'Check' is slightly generic.

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 like get_player or get_stats. It does not specify any 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by specifying the exact fields returned (title, country, followers, join date, last online time), providing behavioral insights beyond 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 that communicates the essential information without any unnecessary words. It is front-loaded with the action and target.

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 this simple tool with one parameter, full schema coverage, and annotations covering safety and idempotency, the description is complete. It mentions the output fields, and an output schema exists to further detail the return structure.

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 a clear description of the username parameter. The description reinforces the parameter with an example but does not add new semantics 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 action (Get), the resource (Chess.com player's profile), and provides an example username. It distinguishes from sibling tools like get_games and get_stats by specifying it returns profile 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 when to use the tool (when needing a player's profile) but does not explicitly compare to alternatives or mention when not to use it. No guidance on choosing over sibling 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?

Annotations already declare read-only, idempotent, non-destructive behavior. Description adds that it returns ratings and game counts, but does not disclose additional behavioral traits beyond confirming the data content.

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 efficiently convey purpose and return data without extraneous information. Information is front-loaded and every phrase 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?

Given the simple tool with one parameter and no nested objects, the description sufficiently covers what the tool does. Output schema presence reduces burden. However, it could mention that ratings are from chess.com, though the username parameter implies that.

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 covers 100% of parameter description with 'Chess.com username'. Description adds no further semantic meaning beyond the schema, such as expected format or examples.

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 'Get' and identifies resource 'player's ratings and game records'. It lists formats (daily, rapid, blitz, bullet) and exact data returned (current/best ratings, win/loss/draw counts), clearly distinguishing from siblings like get_games or get_player.

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?

Description implies usage when needing player stats across time controls, but does not explicitly state when to use this tool over siblings like get_games or get_player, nor provides exclusion reasoning.

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 provide readOnlyHint, idempotentHint, etc. Description adds return field details but no further behavioral traits like pagination. No contradiction.

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 and return fields, followed by usage guidance. No redundant 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?

Simple tool with one optional boolean param and no output schema; description covers purpose, return fields, and usage. Annotations fill safety gaps. 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?

Only parameter 'include_inactive' is fully described in the schema, so baseline applies. Description doesn't add param-specific meaning beyond 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?

Description clearly states 'List the caller's active subscriptions' with a specific verb and resource, and distinguishes from sibling tools like 'subscribe' and 'unsubscribe'.

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: 'review what you're monitoring before adding more or to find an id to cancel.' Lacks explicit alternatives but context is sufficient.

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?

Description discloses rate-limiting (5 per identifier per day), cost (free, not counted against quota), and that feedback is read daily and influences roadmap. Annotations provide no behavioral info (all false), so the description fully carries the burden and adds significant value.

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 (6 sentences), front-loaded with the core action, and each sentence adds essential information. No redundant or unnecessary text.

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

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

All relevant context is covered: purpose, when to use, how to phrase feedback (not pasting prompts), rate limits, cost, and impact. For a 3-param tool with no output schema, this is 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?

Schema coverage is 100% and descriptions are present. The description adds meaning by explaining the enum values (type categories) with concrete examples (e.g., 'bag = something broke or returned wrong data') and provides guidance for the message field ('Be specific (which tool, what error, what data was missing)'). This enriches understanding beyond 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's purpose: sending feedback about Pipeworx being broken, missing, or needing existence. It specifies categories (bug, feature, data_gap, praise) and is distinct from all sibling tools, which are query or action tools.

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

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

Explicit usage guidance: use for bugs, missing features, or praise. Advises against pasting end-user prompts and notes daily rate limits and free usage. No alternative tool exists for feedback, making this the sole channel.

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, destructiveHint. Description adds valuable details: data source ('CF analytics-engine'), no PII, caching delays (5min-1h). 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?

Four sentences, highly efficient. Each sentence adds unique information: purpose, use cases, data derivation, caching. Front-loaded with the main action.

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 aggregation tool with no output schema, description fully covers return types and behavior (caching, derivation). No gaps given tool's simplicity and rich 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 100%. Description adds meaningful context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' Enhances enum choices without redundancy.

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?

Starts with a clear, specific statement: 'What other AI agents are calling on Pipeworx right now.' Specifies returns (top tools, top packs, total call volume) and windows (24h, 7d, 30d). Distinct from siblings like get_games or entity_profile.

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 lists three use cases: discovering hot data sources, confirming canonical tool, checking use-case alignment. Lacks explicit exclusion or alternative tools, but use cases are sufficiently clear to guide selection.

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 declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds rich behavioral context: dual-mode operation, partition check, fill check, semantic anchor (Jaccard similarity), and partition filter. 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 detailed and well-structured with clear sections, but slightly long (250 words). Each part earns its place, covering modes, filters, response, and fill check. Still, minor redundancy could be trimmed without losing clarity.

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 no output schema, the description fully explains the response structure (opportunities array, partition_check fields), edge cases (skipped_low_similarity, placeholder filter), and the fill check mechanism. References to sibling tools complete the context.

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

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

Schema coverage is 100% with descriptions, but the tool description adds significant value: it explains the semantic difference between modes, gives concrete examples of valid inputs (slugs, seed questions, full URLs), and describes how each parameter affects 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 tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes two modes (single-event and cross-event) with specific parameter names. It differentiates from siblings like polymarket_edges or polymarket_fill_risk by explaining its unique approach.

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 requires one of 'event' or 'topic', warns that no args fails, and provides recommended use cases for each. It explains when not to trade (e.g., negative fill check) and directs to a sibling tool (polymarket_fill_risk) for custom sizing, giving complete 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 safety (readOnly, idempotent). Description adds rich behavioral details: caching (KV-level, 1h, keyed on knobs), segment logic, filter reasoning (diagnostics), and model family explanations. 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?

Very verbose with deep technical details (model formulas, funnel counters) that may overwhelm agents. Front-loads purpose but then expands extensively. Could be trimmed for quicker parsing while retaining essential context.

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?

Extremely comprehensive: covers output structure (by_segment, diagnostics, fed_candidates), explains why segments can be empty, and details return fields (edge_pp_net, kelly_fraction, etc.) despite no output 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 coverage is 100%, baseline 3. Description adds value by explaining how parameters like min_liquidity, max_spread_pp act as 'tradeable-edge knobs' and the interplay between min_kelly and min_partition_leg_kelly, which goes 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?

Clear verb+resource: 'scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' Distinguishes from sibling 'polymarket_arbitrage' by focusing on model-driven edge detection rather than pure arbitrage.

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 use case: 'Built for what should I bet on today'. Provides context for usage but does not mention alternatives like 'polymarket_arbitrage' 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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false, and the description confirms read-only behavior. It adds critical context: history bounded by 60-day TTL, decay computed from daily closes (not intraday), snapshots written only on cache-miss. 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 lengthy and includes detailed output structure, which is useful but could be more concise. It is front-loaded with the core question, but the RESPONSE section adds 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?

Despite no output schema, the description thoroughly explains return objects (tracked[], expired[], snapshot_dates[]) and their fields. It covers limitations (TTL, cache-gaps) and decay details, making it complete for a telemetry 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 description coverage is 100%, but the description adds default values (14 days, 1wk), clamp range (2-30), and explains 'snapshot family' for window. This adds meaningful context beyond 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 it provides edge persistence and decay telemetry from daily snapshots, answering 'how long has this edge existed and is it shrinking?' It distinguishes between a fresh edge and an old one, and lists specific output fields (tracked, expired, snapshot_dates). It differentiates from sibling polymarket_edges by focusing on history.

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 understanding edge persistence but does not explicitly state when to use this tool over siblings like polymarket_edges. No when-not or alternatives are mentioned, leaving the agent to infer the 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=true, idempotentHint=true, destructiveHint=false. The description adds detailed behavioral context: walks the order book ladder, returns specific metrics (top_of_book, vwap_fill_price, slippage_pp, shares_filled, verdict), and explains risks like thin books and forced directional risk. 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 lengthy (multiple paragraphs) and contains some redundancy (e.g., repeating mode distinctions). While it is front-loaded with the core purpose and required fields, it could be more concise. However, the detail is justified given the tool's complexity.

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 the tool and the lack of an output schema, the description is remarkably complete. It enumerates all return fields (top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict, theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill detail, thin_legs[], max_clean_notional_usd, forced_directional_risk) and explains the verdict categories. It also warns about partial fills and unhedged positions, covering edge cases.

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%, but the description adds significant meaning beyond the schema. It explains the difference between single-market and basket modes for parameters, clarifies default behavior for 'side' in basket mode (auto-based on partition sum), and interprets 'size_usd' as settlement notional in basket mode. This provides essential context not obvious from the schema alone.

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

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

The description clearly states it performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth'. It distinguishes two distinct modes (single-market and basket) with specific use cases, and its purpose is differentiated from sibling tools like polymarket_arbitrage and polymarket_edges.

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: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also explains why (partial fills convert arb into unhedged directional position), providing clear context for when not to skip this check.

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 read-only, idempotent, non-destructive behavior. The description adds extensive context: safety fields (compatibility_warning, temporal_alignment, skipped counters), explains when spreads are meaningless, and details edge cases. 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 relatively long but well-structured with clear sections (modes, response, safety fields) and minimal redundancy. Every sentence adds value, though some brevity could be achieved without losing clarity.

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 no output schema, the description explains the response structure (leg-by-leg prices, matched spread, safety fields) and covers edge cases (compatibility_warning conditions, temporal alignment). It addresses most agent needs for correct invocation.

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

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

Schema coverage is 100% with detailed parameter descriptions. The description adds value by explaining the mode switching logic and listing macro shortcuts beyond the schema, though the schema itself already provides clear meanings.

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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question, specifies two modes (pre-mapped topics and explicit tickers), and differentiates from sibling tools like polymarket_arbitrage which focus on intra-venue arbitrage.

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 each mode, notes that real spreads are rarer than pre-mapped suggests, and warns that pre-mapped does not guarantee tradeability. It lacks explicit alternatives but provides sufficient context for usage decisions.

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 provide readOnlyHint and idempotentHint. Description adds value by explaining scoping ('scoped to your identifier') and pairing with other tools. 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?

Three sentences, each adding value. Front-loaded with action and resource. No superfluous text.

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

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

Complete for a simple retrieval tool. Schema covers parameter, annotations cover safety, description explains scope and usage with siblings. No output schema needed.

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 description adds clarity: 'omit the key argument to list all keys' explains optional behavior beyond 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?

Clear verb+resource: 'Retrieve a value previously saved via remember, or list all saved keys.' Distinguishes itself from siblings remember and forget by specifying the operations.

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: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' Also notes scoping and pairing with remember/forget. Lacks explicit when-not-to-use but context is clear.

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?

Beyond annotations (readOnlyHint, idempotentHint), the description adds valuable behavioral context: it explains that mark_read:true flags returned events as read, affecting subsequent calls, and that the same feed is accessible via a URL. These details help the agent understand side effects and alternatives.

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 four sentences long, front-loaded with the core purpose, followed by essential details and side effects. Every sentence adds value without redundancy or unnecessary 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 no output schema, the description helpfully lists fields (source, citation_uri, raw payload) and covers filtering and side effects. However, it omits explanation of the limit and unread_only parameters, and does not mention pagination or ordering. It is mostly complete but has minor gaps.

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

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

The input schema covers all 5 parameters (100% coverage), so the baseline is 3. The description adds extra meaning for type and since by providing examples ('sec_8k', ISO timestamp) and explaining mark_read's effect. However, unread_only is not mentioned in the description, so not all parameters get added 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 starts with a specific verb 'Pull' and resource 'fired events from your subscription feed', clearly stating what the tool does. It distinguishes itself from sibling tools by focusing on reading recent alerts, while siblings like list_subscriptions handle subscription management.

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 polling recent alerts and mentions an alternative URL for scripts/dashboards, but it does not explicitly state when to use this tool versus other tools like list_subscriptions or search tools. No exclusion criteria are provided, making the guidance adequate but not strong.

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 include readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant context: fallback from GDELT to GNews on rate limit/5xx, PatentsView API sunset soft-fail, and return structure (changes[] grouped by source, total_changes, pipeworx:// URIs). 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 front-loaded with sample queries and packs essential details without waste. Although a bit long, every sentence adds value given the tool's complexity (multisource fan-out, fallback behavior). It is appropriately concise for the information density.

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 required parameters (all explained), no output schema (but description covers return structure), and complex behavior (fan-out, fallback, soft-fail), the description covers all necessary operational context. It fully equips the agent to understand tool capabilities and limitations.

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. Description adds value beyond schema: for `since`, provides examples of ISO date and relative shorthand with recommended usage ('30d' or '1m' for monitoring). For `value`, explains ticker or CIK formats. For `type`, notes only 'company' is supported. This extra guidance justifies a 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 verb (get change feed) and resource (changes for a company) with multiple examples. It distinguishes from sibling `entity_profile` by noting that `entity_profile` is for static profiles regardless of time window.

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 explicit when-to-use for `entity_profile` instead, and gives example queries like "What's new with X" and "latest on Y". Mentions the fan-out to multiple sources, helping the agent decide when to use this tool versus others.

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 significant behavioral detail beyond annotations: memory scoping by identifier, persistence for authenticated users (permanent) vs anonymous (24 hours), and idempotence implied. 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?

Three concise sentences front-load the core purpose, then add usage context and behavioral details. Every sentence adds value with no 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 simple two-parameter tool with no output schema, the description fully covers purpose, usage, behavior, and integration with sibling tools. Gaps like return values are negligible.

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 minimal extra meaning beyond the schema's parameter descriptions, though it reinforces with examples.

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 saves data for later reuse using a key-value pair. It distinguishes from siblings by referencing recall and forget, which retrieve and delete respectively.

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 tells when to use: 'when you discover something worth carrying forward.' It provides concrete examples (ticker, address, preference) and pairs with recall and forget, offering clear guidance on usage.

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, etc. The description adds behavioral context: 'Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.' This informs the agent about potential latency or resource usage. No contradictions 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 well-structured, starting with user-phrased questions, then the core purpose, and then detailed breakdown by type. While somewhat lengthy, every sentence adds value. It could be slightly more concise, but the structure facilitates quick scanning.

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 explains return values for both types, including citation URIs. It mentions auto-disambiguation for companies. It does not cover error cases (e.g., not found), but overall provides sufficient context for an AI agent to understand what to expect.

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 significantly enriches parameter meaning. It explains that for 'company', value can be ticker, CIK, or name, and returns ticker + 10-digit CIK + company_name + citation URI. For 'drug', it accepts brand/generic name and returns RxCUI + ingredient + brand + citation. This goes well beyond the schema's brief 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 it resolves user-spoken names to canonical/official identifiers, with specific examples like ticker, CIK, RxCUI. It explicitly lists supported entity types (company, drug) and what each returns. The purpose is specific and distinguishes this tool from siblings like compare_entities or entity_profile.

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 includes explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also mentions that each call cascades through several endpoints, replacing multiple manual lookups. However, it does not explicitly state when not to use this tool or list specific alternatives, though 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.

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it orchestrates multiple calls to ai_visibility_check, ranks results, and returns a specific structure (score, confidence, signal density). 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 three sentences with no wasted words. The first sentence states the core purpose, the second explains the mechanics, and the third gives a concrete use case. Front-loaded 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?

Given the tool's moderate complexity (4 parameters, no output schema, composite behavior), the description covers the main function, input constraints (2-8 entities), output structure (ranked list with metrics), and relationship to sibling tools. It does not detail error handling or rate limiting, but the annotations and schema fill some 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%, providing baseline of 3. The description adds unique semantic value by noting that the first entity in the 'entities' array is treated as the 'subject' for narrative, which is not present in the schema. It also clarifies the role of _apiKey ('passed to api.anthropic.com per probe') and the context parameter ('disambiguates common names'). This elevates it above baseline.

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 ('compare AI visibility'), the resource ('multiple entities side-by-side'), and the method ('probes each entity with ai_visibility_check, ranks by score'). It distinguishes from the sibling tools ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the competitive audit context.

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 a clear use case ('competitive AI-marketing audits') and an example question ('does Claude know about us as well as our competitors?'). It does not explicitly exclude when not to use it or name alternatives, but the context is specific enough to guide appropriate usage.

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?

Discloses graceful degradation on bundlephobia timeouts, sources_failed listing, and composite nature. Annotations already declare readOnly and idempotent, description adds timing and failure 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?

Efficiently front-loaded with purpose, then details. Somewhat lengthy but each 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?

Despite no output schema, description thoroughly covers return fields (summary block, advisories, links, versions) and error behavior, compensating fully.

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 adds minimal new meaning beyond schema (e.g., defaults for version). Baseline 3 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?

Clear verb+resource: composite check for npm package evaluation. Distinguishes from siblings by being npm-specific and a multi-source fan-out.

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 (agent asks about safety, popularity, size) and provides alternatives for non-npm ecosystems (deps.dev:version directly).

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 significant behavioral context beyond annotations (which already indicate safety). It discloses truncation at 200K chars with flagging, use of BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, and return of character offsets. No contradictions 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 well-structured with a clear first sentence stating purpose, followed by usage guidance, technical details, and pairing info. It is somewhat lengthy but each sentence adds value. Could be slightly more concise but it's 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?

Despite no output schema, the description fully explains what is returned: top-N passages with character offsets and similarity scores. It also mentions integration with sibling tool ask_pipeworx_grounded, providing complete context for 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 coverage is 100%, so the schema already documents parameters. The description adds value by providing example queries for the 'query' parameter and explaining the character limit for 'text'. This enhances understanding beyond the schema.

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

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

The description clearly states it performs semantic search inside a fetched record. It specifies the verb 'search', the resource 'a fetched record', and provides concrete examples like SEC 10-K body. It also distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded.

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 says when to use: when the record is too large for the prompt. It mentions pairing with ask_pipeworx_grounded. However, it does not explicitly state when not to use or provide alternatives beyond that one sibling.

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 provide idempotentHint true, readOnlyHint false, etc. The description adds significant behavioral context: returns subscription id, requires account type, details delivery behavior (SMS cap, webhook auto-disable after 10 failures, HMAC signing). No contradictions 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 relatively long but well-structured: purpose sentence, requirements, type list with examples, delivery channels. Each sentence adds value, though it could be slightly more 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 no output schema, the description explains the return value (subscription id). It covers creation, types, delivery, and constraints like phone verification and webhook auto-disable. It lacks explicit mention of error handling or idempotency behavior but is 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 detailed examples for each type's params and delivery options (e.g., 'items:["5.02"]' for sec_8k, 'delivery:{sms:"+15551234567"}' with verification requirement), greatly enriching meaning beyond 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 it creates a proactive monitoring subscription and returns a new subscription ID. It distinguishes from sibling tools like list_subscriptions and unsubscribe by using verbs 'create' and 'subscribe' and mentioning 'returns new subscription id'.

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 specifies prerequisite: requires a Pipeworx OAuth account (anonymous/BYO cannot persist). It provides examples of when to use each type and delivery constraints (SMS cap, phone verification). However, it does not explicitly say when not to use this tool versus siblings like ask_pipeworx for one-off queries.

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, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds that it returns 'category-bucketed example questions ... each with the exact tool + argument shape that answers it, drawn from the live catalog.' 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 comprehensive but somewhat verbose, including many example queries and details. While front-loaded with common queries, it could be more concise without losing value.

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

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

For an onboarding tool with no output schema, the description fully explains return structure (category-bucketed examples with tool+argument shapes), invocation patterns, and ties to sibling meta-tools. Complete for its 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%; description adds context about omitting topic for cross-category spread and enumerates possible values, complementing the schema's parameter 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 returns category-bucketed example questions for onboarding, distinguishing it from siblings like ask_pipeworx and discover_tools. It specifies the exact use case: 'the onboarding entry point for an agent that just connected and wants to know what is worth asking.'

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 says when to use: 'Use this FIRST when you do not yet know what Pipeworx can do for you.' Also provides guidance on arguments: no arguments for full spread, or pass topic to focus. Implies when not needed after onboarding.

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?

Discloses ownership enforcement and the deactivation (not deletion) behavior, adding significant value beyond annotations. No contradictions 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?

Two sentences, front-loaded with action, no redundant information. 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?

For a simple tool with one parameter and annotations present, the description covers purpose, constraints, and side effects completely. No output schema needed.

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% for the single parameter. The description adds context that the id is from 'subscribe', which helps the agent understand the expected input beyond the schema.

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

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

The description clearly states the action ('Cancel a subscription by id'), specifies the resource (subscription), and distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' through ownership enforcement and deactivation semantics.

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 context for when to use (cancel own subscriptions) and references 'recent_alerts' for historical events. Does not explicitly exclude other scenarios or compare to alternatives, but the context is clear.

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 indicate read-only, open-world, idempotent, and non-destructive. The description adds that it uses SEC EDGAR + XBRL, returns a verdict with citation and percent delta, and replaces 4-6 sequential calls. This significantly supplements 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 paragraph but packs essential information: use cases, domain, output format, and efficiency gains. While not elegantly structured, it is reasonably concise and informative.

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 thoroughly explains the return values (verdict types, citation, delta) and the source. It covers input, output, domain, and replaces multiple calls, making it complete for the 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 for 'claim' is detailed with examples. Description adds context on the domain and expected format, which is valuable since schema_coverage is 100%. Overall well-covered.

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 verifies natural-language claims against authoritative sources, specifically company-financial claims, with examples like 'fact check' and 'verify'. It distinguishes itself from siblings like deep_research by focusing on factual verification with a verdict output.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and notes the domain limitation to company-financial claims. It does not explicitly mention alternatives, but the sibling list includes many research tools, and the domain specificity provides implicit guidance.

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