Transport Feeds

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds return structure (per-model fields + combined view) and transparently states Anthropic calls pass through to api.anthropic.com with user's key, adding value 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?

Description is concise (3-4 sentences), front-loaded with purpose, and every sentence adds value. No fluff or repetition.

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 adequately explains return format (per-model fields + combined). All parameters are covered, usage context is clear, and it includes important details like data source and billing. Complete for the tool's moderate 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%, so baseline is 3. The description adds meaning: explains `_apiKey` as BYO key with user billing, `context` for disambiguation, and default model. This extra context 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 tool probes LLMs for knowledge about entities and scores visibility 0-100 per model. It specifies the verb 'probe', resource 'LLMs', and the outcome 'score visibility', distinguishing it from sibling tools that focus on Pipeworx or other services.

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

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

The description gives explicit guidance: default free model, optional Anthropic probe with BYO key, and uses cases like AI-marketing audits and brand checks. It doesn't explicitly mention when not to use, but provides clear context for appropriate use.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, and idempotentHint=true, so the safety profile is clear. The description adds value by detailing the tool's ability to route to a vast set of sources and return structured results with citation URIs, which goes 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 detailed but front-loaded with the most critical guidance ('PREFER OVER WEB SEARCH'). It includes examples and usage boundaries. While it could be slightly more concise, every sentence serves a purpose.

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

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

Given the tool's complexity (6 parameters, no output schema), the description thoroughly covers when and how to use it, including source scope, citation format, and alternative tools. It is complete enough for an agent to select and invoke 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 coverage is 100%, with the schema already providing clear descriptions for all parameters and aliases. The description does not add significant additional semantic meaning beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description explicitly states the tool routes questions to 4,774 tools across 1,242 verified sources, returning structured answers with citations. It clearly distinguishes from web search and sibling tools like ask_pipeworx_grounded and deep_research.

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 begins with 'PREFER OVER WEB SEARCH' and provides clear criteria for when to use this tool (factual questions about real-world entities, events, or numbers). It also specifies when to step up to alternatives (ask_pipeworx_grounded for hallucination-resistant answers, deep_research for broad multi-part questions).

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 and idempotent hints; the description adds behavioral details like 'extracts the answer using ONLY what the tool result contains', lists return fields including evidence and refusal reasons, and mentions the extra LLM call cost. 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?

The description is a single paragraph that front-loads the purpose, then provides detailed usage and behavior. Every sentence adds value, though it could be broken into structured points for even better readability.

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

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

The description covers purpose, when to use, behavioral traits, return values (including refusal reasons), and cost trade-off. For a complex tool with no output schema, it fully compensates by describing outputs and error modes.

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

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

Schema description coverage is 100% with aliases for the single 'question' parameter. The description does not add semantics beyond explaining that the question is routed to tools and data is fetched, but no extra parameter details are needed given the schema 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 it is a 'hallucination-resistant answer mode for high-stakes reads' that extracts answers only from tool results. It distinguishes from the sibling 'ask_pipeworx' by noting the extra LLM call cost and the appropriate use cases.

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: 'whenever an answer will be quoted, cited, or acted on' in high-stakes domains, and advises using the sibling 'ask_pipeworx' for casual lookups, providing clear alternatives.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds substantial behavioral context: resolution process, fan-out logic, safety short-circuits, wide-spread handling, 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?

Very verbose with multiple paragraphs and detailed examples. While logically structured and front-loaded with purpose, it could be more concise without losing essential 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?

Covers input formats, classifiers, fan-out examples, response shapes, edge cases (low confidence, closed markets, wide spreads, resolution rules), and safety behaviors. Even without an output schema, the description thoroughly documents expected outputs.

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 all parameters (100% coverage). Description adds examples for market input, explains depth and include_raw defaults and their effects, providing meaningful extra context.

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 clear verb+resource ('Research a Polymarket bet') and specifies input types. Distinguishes from siblings like ask_pipeworx and polymarket_edges by explicitly stating its purpose for bet research.

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 for "should I bet on X", "what does the data say about Y", or "is there edge in Z"'. Lacks explicit when-not-to-use instructions, but context with siblings implies appropriate scenarios.

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

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

Annotations indicate read-only, idempotent, non-destructive. Description adds that it pulls from SEC EDGAR/XBRL and FAERS, handles fiscal-year quirks, and returns citation URIs—context 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?

Description is front-loaded with natural language examples and uses line breaks. Slightly long but every sentence adds value; could be slightly more compact.

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; description explains return value (paired data + citation URIs) and sorting. Covers all needed context for the tool's 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% with descriptions. Description enriches by providing examples (tickers, drug names), constraints (2-5 items), and behavior per type, adding 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 starts with natural language triggers like 'Compare X and Y' and clearly states it performs side-by-side comparison of 2-5 entities. It distinguishes from sequential single-pack lookups and sibling tools like 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 says 'ALWAYS PREFER over sequential single-pack lookups' and provides contextual examples for when to use. It also differentiates by entity type with specific data sources.

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 key behavioral traits beyond annotations: account requirement, paid plan for thorough depth, not open-web search, returns evidence with citations and gaps, expected 15-60s delay. No contradiction with annotations that indicate read-only, idempotent, non-destructive 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?

Description is fairly long but front-loaded with critical info (account requirement, alternatives) and well-structured. Every sentence adds value, but could be slightly more concise 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, description fully explains return format: 'findings packet with verbatim evidence, confidence, source, fetched_at, stable citation, and gaps array'. Also covers response time and the tool's limitation to structured data catalog, ensuring comprehensive understanding.

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

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

Schema coverage is 100% but description adds meaning: depth enum values mapped to facet counts (quick=3, standard=5, thorough=8), and clarifies that question can be broad/multi-part. This enriches the schema for correct parameter usage.

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: 'Grounded multi-source research across Pipeworx's 1242 STRUCTURED data sources' with decomposition and parallel tool routing. It distinguishes from siblings like ask_pipeworx for single lookups and breaking news, making its specific role unambiguous.

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

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

Explicit guidance on when to use: 'Best for broad/multi-part questions over structured data' and when not: single lookup use ask_pipeworx, breaking news prefer ask_pipeworx. Also mentions account requirement and free vs paid plan for depth levels.

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 behavioral context: 'Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' This enriches the agent's understanding of the tool's behavior without contradicting 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: purpose first, then domain list, then return format, then usage cue. It is front-loaded and each sentence adds value. Despite length, it is efficient and clear.

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

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

Given the complexity of 33 sibling tools and the discovery role, the description is complete: it explains what it does, when to use, what it returns (including examples and schema coverage), and how to interpret results. No output schema is needed as return format is described.

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 provides examples and notes aliases, but this largely duplicates the schema property descriptions. It adds minimal extra meaning beyond what the schema already conveys.

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

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

The description explicitly states 'Find tools by describing the data or task.' and lists many domains (SEC filings, FDA drugs, etc.). It clearly differentiates from sibling tools by instructing 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides a specific verb+resource and distinguishes from other 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?

The description gives explicit usage guidance: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST'. It implies when not to use by saying 'not just one answer', pointing to alternatives. This provides clear context and 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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds rich behavioral context: fans out across multiple sources, specifies returned fields, notes patent API sunset (soft-fail), and fallbacks. 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 packed with information and front-loaded with example queries. Every sentence serves a purpose, but may be slightly longer than necessary. Still, it is well-structured 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?

Despite lacking an output schema, the description details return fields and covers edge cases (patent sunset, name resolution). It is comprehensive for the tool's complexity, leaving no critical 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% with clear descriptions. The description adds value by reinforcing constraints ('Pass ticker "AAPL" or zero-padded CIK') and clarifying that names are not supported. Slightly more context than 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 the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It provides multiple example queries and distinguishes from sibling tools like resolve_entity, which handles name resolution.

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: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' and when not to use: names not supported, use resolve_entity first. Provides clear input constraints.

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, so the safety profile is clear. The description adds valuable behavior: 'CF-robust: fetches directly and falls back to a proxy if the source blocks the gateway,' which provides insight into reliability and fallback behavior 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 consists of two short sentences, with the first stating the core purpose and the second adding robustness and usage guidance. Every word contributes 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?

While the tool has no output schema, the description does not explain the output format. For a fetching tool, stating that it normalizes the feed implies a standard output, but completeness is moderate. It is adequate but could mention 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%, so parameters are already fully described in the schema. The description does not add any additional parameter-specific meaning beyond what the schema provides, so a baseline score of 3 is appropriate.

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

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

The description uses a specific verb (fetch and normalize) and resource (any RSS/Atom/RDF feed by URL), and distinguishes itself from the sibling list_feeds by advising to use list_feeds first for curated sources. This clearly states what the tool does and differentiates it.

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 usage hint: 'Use list_feeds first for curated sources.' This tells the agent when to use an alternative. However, it does not explicitly mention when not to use fetch_feed or other alternatives like read_feed, leaving some ambiguity.

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 destructiveHint=true and idempotentHint=true. The description adds that the tool deletes a previously stored memory, but doesn't elaborate on behavior when the key doesn't exist or on irreversibility. Adequate but not rich.

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

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

Two sentences: first states purpose, second provides usage guidelines. Every sentence is necessary and front-loaded. No wasted words.

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

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

For a simple one-parameter tool with comprehensive annotations and no output schema, the description covers purpose, usage, and basic behavior completely. The tool is straightforward, and the description is sufficient.

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

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

With 100% schema description coverage, the schema already documents the 'key' parameter. The description adds no additional meaning beyond what is in the schema, so baseline 3 is appropriate.

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

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

The description explicitly states 'Delete a previously stored memory by key,' which is a specific verb-resource pair. It distinguishes itself from sibling tools like 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?

The description provides clear when-to-use scenarios ('when context is stale, the task is done, or you want to clear sensitive data') and suggests pairing with siblings ('Pair 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?

The description adds significant behavioral details beyond annotations: it fetches the page, extracts title/description/links, and outputs a standard llms.txt blob. Annotations already ensure safety (readOnly, idempotent, non-destructive), and the description complements them well 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?

The description is reasonably concise at about four sentences, front-loads the purpose, and avoids fluff. It could be slightly tighter, but it remains clear and efficient.

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

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

Given the tool's simplicity (2 params, no output schema), the description covers what it does, how it works, output format, and use cases. It lacks details on error handling or edge cases, but for a straightforward tool with annotations, it 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 does not add extra meaning to the parameters beyond what is in the schema, such as how max_links affects output or URL format specifics. No additional semantic value.

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

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

The description clearly states the tool generates a production-ready llms.txt file for a given URL, specifying the verb and resource. It distinguishes effectively from sibling tools by focusing on AI crawler indexing, a unique niche.

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 lists three use cases (client indexing, personal project drafting, competitor auditing), giving clear context for when to use. However, it does not explicitly state when not to use it or point to alternatives, which would have made it a 5.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, which cover safety. The description adds that it lists feeds and optionally filters, but does not describe any additional behavioral traits such as pagination, rate limits, or output format beyond the mentioned fields.

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?

Extremely concise: two sentences that front-load the purpose and then provide optional filtering and referral to read_feed. No unnecessary words or 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?

The description is complete for a simple list tool: it states the resource, returned fields, filtering options, and a pointer to the sibling for detail retrieval. Although lacking output schema, the returned fields are explicitly listed, making it sufficient.

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

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

The description restates what the schema already provides: filtering by category or keyword. With 100% schema coverage, the description adds minimal extra meaning (e.g., enumerates two categories). Baseline 3 is appropriate as it does not significantly enhance 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 the verb 'List' and the resource 'curated transport & mobility feeds', listing the fields returned (id, title, category, source). It distinguishes from sibling 'read_feed' by noting that to get a single feed, one should pass an id to read_feed.

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 clear context on when to use this tool: listing feeds with optional filtering by category or keyword. It explicitly directs to 'read_feed' for a single feed, differentiating usage. However, it does not explicitly exclude other siblings like 'fetch_feed' or 'subscribe'.

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 and idempotentHint. The description adds value by detailing the return fields and the default of active-only, which is beyond the annotations.

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

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

Two sentences, front-loaded with purpose and return fields, then usage guidance. No wasted words.

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

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

Although no output schema, the description lists all return fields. With one optional parameter and full annotation coverage, the description is complete for the 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 covers 100% of parameter descriptions. The description does not add new meaning beyond the schema's explanation of include_inactive, so baseline 3 is appropriate.

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

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

The description clearly states it lists active subscriptions and specifies the return fields (id, type, params, etc.), which distinguishes it 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.' Does not explicitly exclude other uses, 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 indicate no special hints (readOnly, destructive, etc.). The description adds context: rate-limited to 5 per identifier per day, free, and that the team reads digests daily. This provides useful behavioral insight 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 concise (4 sentences) and front-loaded with the purpose. Every sentence adds value: usage guidance, constraints, and behavioral notes. No wasted words.

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

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

Given no output schema, the description covers what happens after submission (team reads, impacts roadmap) and constraints (rate-limited, free). It is sufficient for an agent to understand the tool's effect.

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 adds significant meaning beyond the schema. It explains each enum value (bug, feature, data_gap, praise) and provides guidance on how to fill in context fields and the message. This helps the agent use parameters correctly.

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

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

The description clearly states that the tool sends feedback to the Pipeworx team about issues or praise. It lists specific use cases (bug, feature/data_gap, praise) and distinguishes itself from sibling tools by being the only feedback mechanism.

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

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

The description explicitly tells when to use the tool (e.g., for bugs, features, praise) and what not to do (avoid pasting end-user prompts). It also mentions rate limits and that it is free. However, it does not explicitly state when not to use it, leaving some ambiguity.

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 adds significant behavioral context beyond annotations: notes the tool is self-aggregating from CF analytics-engine, includes no PII, and caches 5min-1h depending on window. This complements the readOnlyHint and idempotentHint annotations well.

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 (four sentences), front-loaded with the core output, then use cases, then technical details. 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?

Despite no output schema, the description clearly states what is returned (top tools, top packs, total call volume) and provides caching info. This is sufficient for a simple query tool with one parameter.

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

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

Schema description coverage is 100% with a clear description for the single 'window' parameter. The tool description merely restates the options without adding new meaning, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool returns top tools, top packs, and total call volume over a window, with three explicit use cases. It distinguishes itself from sibling tools like ask_pipeworx or discover_tools by focusing on trending/usage 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 provides three specific use cases (discovering hot data sources, confirming canonical tools, checking alignment with agent needs) giving clear context on when to use. However, it does not explicitly mention when not to use or name alternatives, so it stops short of a 5.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds significant behavioral context: internal algorithms (semantic anchor, partition filter, fill check), response structure, and 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 lengthy but well-structured. Front-loaded with purpose and usage, then detailed parameter explanation and internal logic. Every sentence adds value, but could be slightly condensed 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?

No output schema exists, but the description thoroughly explains the response structure (opportunities[], partition_check fields, fill_check logic). Covers all major aspects for correct invocation and understanding of results.

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 baseline 3. The description adds deep meaning: explains what event slugs or URLs are acceptable, how topic seeds are used for cross-event scanning, and the difference in behavior. Greatly exceeds 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 the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) and the default trending_scan behavior, making its purpose specific and distinct from siblings.

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 each mode: call with no args for trending_scan, pass 'event' for a specific market slug, or 'topic' for cross-event scanning. Also explains when not to trade via fill_check. Provides clear alternatives and 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 declare readOnlyHint and idempotentHint. The description adds extensive behavioral detail: caching at KV level keyed on knobs, three model families with detailed methodology, Kelly calculations, slippage adjustments, 24h-move warning, and diagnostics for empty segments. 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 dense and well-structured with clear sections, but it is quite long. Every sentence adds value, but for an AI agent it could be more concise without losing essential detail.

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 documents the response structure (by_segment, fed_candidates, diagnostics) and edge cases (empty segments, Fed bets). It also explains caching behavior and tradeable-edge filters, making it complete for a complex 9-parameter 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% with good descriptions. The description adds value by explaining how parameters interact (e.g., min_kelly vs min_partition_leg_kelly for partitions) and providing practical guidance (e.g., 'Bump for very thin partitions').

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 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price' – a specific verb, resource, and purpose. It distinguishes from siblings by targeting arbitrage opportunities vs other Polymarket-related 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?

The description explicitly says 'Built for "what should I bet on today"' and explains the knobs for tradeable-edge filters. However, it does not explicitly mention when NOT to use this tool or alternative 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 indicate read-only, idempotent, and safe behavior. The description adds context on snapshot TTL (60-day limit), data gaps due to cache misses, and computed fields like trend and decay_pp_per_day. 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 purpose and answer, then follows a clear structure: args, response format, limits. It is somewhat lengthy but each sentence adds value; minor trimming could improve conciseness.

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

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

No output schema exists, but the description thoroughly explains the response structure (tracked, expired, snapshot_dates) and limitations. It covers all needed context for an agent to understand inputs and outputs, making it complete for a read-only 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 coverage is 100% with parameter descriptions. The description adds context: default and clamp values for 'days', and 'snapshot family' for 'window', providing meaning beyond the schema. Baseline 3 is exceeded due to 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 clearly states the tool provides 'edge persistence and decay telemetry' from daily snapshots, answering a specific question about edge duration and trend. It distinguishes itself from sibling tool 'polymarket_edges' by focusing on longitudinal analysis rather than raw edge 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 explains when to use the tool: to assess edge age and decay trend. It provides clear context on arguments and return structure, though it does not explicitly state when not to use it or compare with alternatives beyond the implied distinction.

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

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

Annotations indicate a safe read operation, and the description adds context about requiring one of market or event, default sizes, return values like top_of_book, slippage, verdict, and warnings about partial fills causing directional risk, all 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 well-structured with clear sections and all-caps keywords for important notes, but is somewhat long. It efficiently conveys complex information, though minor trimming could improve conciseness.

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

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

Despite no output schema, the description lists detailed return values for both modes, covers all aspects of tool usage, and provides complete guidance, making it contextually complete for a tool of this 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 adds meaning by explaining the difference between side defaults for single and basket modes, clarifying event vs market, and describing size_usd as settlement notional in basket mode, providing more context than 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 the tool's purpose as a realizable-vs-theoretical edge check against live order-book depth, and distinguishes between single-market and basket modes, differentiating it 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 the tool: before acting on polymarket_arbitrage or polymarket_edges signals above ~$500, with reasoning about theoretical overround not being capturable and partial fills leading to unhedged positions.

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, idempotentHint, and destructiveHint, but the description goes far beyond by detailing compatibility warnings (shape mismatch, semantic mismatch), temporal alignment checks, and the structure of the response (matched_pairs, top_spreads_pp, skipped counters). This provides rich behavioral context that the agent needs to interpret results correctly.

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 dense and informative, but it is longer than necessary. Key information (modes, safety fields) is front-loaded, but the explanation of compatibility warning counters could be more concise. Overall, it is well-structured for the complexity, with only minor 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 tool with no output schema and complex cross-venue logic, the description fully covers what the agent needs: input modes, return structure, interpretation of fields (temporal_alignment, compatibility_warning, skipped counters), and limitations. No gaps remain for the agent to misinterpret the results.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add new semantics beyond the schema—it reiterates the two modes and that explicit parameters override the topic, but the schema already explains each parameter with examples. Thus, no significant added 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 begins with 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' which is a specific verb+resource. It clearly distinguishes this tool from sibling tools like polymarket_arbitrage by focusing on cross-venue comparison. The two modes (topic shortcuts vs. explicit IDs) are explicitly stated, leaving no ambiguity about what the tool does.

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

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

The description outlines two modes and warns that most pre-mapped topics return a compatibility warning, setting clear expectations. It explains when spreads are meaningful (aligned temporal and similar bet shapes) and when they are not, but does not directly contrast with alternatives like within-venue arbitrage tools. However, the context is sufficient for an agent to decide when to invoke this 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, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral context: output structure (title, link, published, summary) and filtering option. 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?

Single informative sentence, no wasted words. Front-loaded with key action and resource.

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 purpose, source, output fields, and optional filter. Missing details on pagination or error handling, but these are acceptable given the simplicity and strong 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%, so parameter descriptions already exist. Description reinforces 'by its id (from list_feeds)' for feed parameter and mentions keyword filtering for query parameter, but adds minimal new 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?

Description clearly states the tool reads a curated transport & mobility feed by ID, returns normalized items (title, link, published, summary), and supports optional keyword filtering. It distinguishes from siblings like list_feeds (lists feeds) and fetch_feed (raw fetch).

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

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

Indicates the feed ID comes from list_feeds, providing explicit context. Mentions optional keyword filtering. Does not explicitly state when not to use or suggest alternatives, but context from sibling tools provides differentiation.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds value by explaining scoping (anonymous IP, key hash, account ID) and pairing with remember/forget. 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, each carrying important information: retrieval behavior, usage context, and scope. Front-loaded with the core action. No wasted words.

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

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

For a simple tool with one optional parameter, comprehensive annotations (readOnly, idempotent, non-destructive), and no output schema needed, the description fully covers what an agent needs to use it correctly.

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

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

Schema covers the single parameter 'key' at 100%. Description adds the essential detail that omitting key lists all saved keys, which aids correct invocation beyond 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 retrieves values saved via 'remember' or lists all keys when omitted. It distinguishes from siblings 'remember' and 'forget' by specifying the pairing, making its purpose unambiguous.

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

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

The description explains when to use (look up stored context like ticker, address, notes) and mentions scoping. While it doesn't explicitly state when not to use, the context and sibling mentions imply 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?

The description states that setting mark_read:true flags events as read, which mutates state. This contradicts the annotations which declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description discloses a behavioral trait (side effect) that contradicts the safety hints, causing confusion.

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: three sentences that front-load the main purpose, then detail key fields and usage. Every sentence adds necessary context without redundancy.

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

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

Given 5 optional parameters and no output schema, the description covers purpose, parameters, side effects, and alternative access. It lacks explicit mention of default limit or pagination, but is mostly complete for a poll-style tool.

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

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

The input schema covers 100% of parameters with descriptions. The description adds value by giving examples (e.g., 'sec_8k' for type) and clarifies the purpose of mark_read and since, going beyond schema descriptions.

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

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

The description clearly states the tool 'Pull fired events from your subscription feed' and specifies what it returns (source, citation_uri, payload). It distinguishes from sibling tools like fetch_feed or read_feed by focusing on recent alerts from the subscription feed.

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 guidance on filtering by type and since, the mark_read option, and notes that polling works fine. It also mentions an alternative direct HTTP endpoint for scripts/dashboards, helping the agent choose between tool and alternative access.

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, openWorldHint, etc.), the description details the fan-out behavior, fallback from GDELT to GNews on rate limits/5xx, USPTO soft-fail until reactivated, and the return structure (changes[] grouped by source + total_changes + citation URIs). This adds substantial behavioral transparency.

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

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

The description is a single paragraph but well-structured: example queries first, then expansion, fallback details, and the alternative tool at the end. It is efficient without excess, though slightly dense. A minor improvement could be breaking into bullet points for readability.

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

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

Given no output schema, the description explains the return format (structured changes grouped by source with count and citation URIs). It covers all parameters, behavior, fallbacks, and alternative tool. It is complete for an agent to understand and invoke 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 coverage is 100%, but the description adds value by explaining that `since` accepts ISO date or relative shorthand with examples ('7d', '30d', '3m', '1y') and recommends '30d' or '1m' for typical monitoring. It also clarifies that `type` only supports 'company' currently. This enriches 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 it provides a change feed for a company in a specified time window, fanning out to multiple sources (SEC EDGAR, GDELT/GNews, USPTO). It distinguishes itself from the sibling tool `entity_profile`, which handles static profiles, making the purpose and scope unambiguous.

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

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

The description provides explicit example queries like 'What's new with X' and 'latest on Y', and directly advises using `entity_profile` for static profiles regardless of time window. This gives clear 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?

Description adds behavioral details beyond annotations: scoping by identifier, persistence for authenticated users vs. 24-hour for anonymous sessions. 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?

Four sentences, front-loaded with purpose, then usage, then technical details. Every sentence adds value without 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 store tool with no output schema, the description covers all needed: purpose, when to use, scoping, persistence, and relationship to siblings. Complete and self-contained.

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 decent descriptions. The description enriches parameter meaning with examples like 'subject_property', 'target_ticker' for key and various value types.

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 reuse later, with concrete examples (resolved ticker, target address). It distinguishes from sibling tools recall and forget, which are explicitly mentioned.

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 when discovering something worth carrying forward. Pairs with recall/forget. No explicit 'when not to use', 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, idempotent, and non-destructive behavior. The description adds valuable behavioral context: internal cascading lookups ('replaces 2-3 manual lookups'), auto-disambiguation for companies, and specific return structures including citation URIs.

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 efficient and front-loaded with purpose and usage. It includes necessary details for each type but avoids redundancy. Slightly long due to examples and return explanations, but no wasted text.

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

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

Given no output schema, the description fully covers what the tool returns for each type, including citation URIs. Combined with annotations, all behavioral aspects are addressed. The tool is simple (2 params), so completeness is high.

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 already well-defined. The description enhances meaning by explaining accepted input formats (e.g., 'ticker, CIK, or company name' for type='company') and auto-disambiguation, which is not in 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 begins with concrete examples like 'What's the ticker for…' and clearly states the core purpose: 'resolve a user-spoken NAME to the canonical/official identifier'. It distinguishes itself from siblings by specifying 'Use FIRST whenever you have a name but need an ID' and notes that it replaces multiple manual 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?

The description explicitly advises 'Use FIRST whenever you have a name but need an ID', providing clear when-to-use guidance. It details supported entity types and what each returns, leaving no ambiguity about when this tool should be invoked over 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?

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint, openWorldHint), the description discloses that the tool probes each entity with ai_visibility_check, ranks by score, and surfaces the most/least recognized. It also notes that the 'anthropic' model requires an API key. 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 concise and well-structured: first sentence states the core function, second explains the process, third gives a concrete example, fourth describes the return format. 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?

Given 100% schema coverage, good annotations, and no output schema, the description fully explains the output (ranked list with score, confidence, signal density) and the internal probe mechanism. It is complete for an agent to understand usage, though it omits potential error handling or rate limits (acceptable for a read-only survey 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?

All 4 parameters have schema descriptions (100% coverage). The description adds extra meaning: explains that the first entity is treated as the subject, that models default to workers-ai, that _apiKey is only needed for anthropic, and that context disambiguates common names. This significantly enriches 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 the tool compares AI visibility across multiple entities side-by-side, using a specific verb ('compare') and resource ('AI presence'). It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison).

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 it is useful for competitive AI-marketing audits and gives an example use case. It implies when to use but does not explicitly state when not to use or list alternatives, though the sibling context makes it 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 mark readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds critical behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. 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?

Single paragraph is front-loaded with purpose ('should I add this npm package'), then covers behavior, return fields, and edge cases. Every sentence adds value without 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?

No output schema, but the description details the return summary block (fields listed), per-advisory detail, links, and recent alternative versions. Also covers partial failures. Complete for a composite tool with two external services.

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% description coverage (both package and version described). The description adds ecosystem constraint (NPM only) and default behavior for omitted version, but schema already covers the basics. Scores 4 because description adds value 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 verb ('check') and resource ('npm package'), and specifies it composites multiple data sources (deps.dev and bundlephobia). It distinguishes itself from siblings by noting it covers NPM only, while other ecosystems fall under deps.dev:version directly.

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 an agent asks 'is X safe/popular/small' or 'what does adding lodash cost me'. It also provides an alternative for non-NPM packages (deps.dev:version directly), guiding correct 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?

Discloses technical details beyond annotations: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, returns passages with character offsets and scores, and caps at 200K characters with truncation flagging.

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?

Approximately 120 words, front-loaded with purpose, each sentence adds value: usage scenario, technical details, limitations, and pairing with another tool. 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?

Given no output schema and moderate complexity, the description covers input, processing (embedding, chunking), output, constraints, and usage pattern comprehensively. It explains character offsets and similarity scores, which are not in 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?

Input schema covers all parameters with descriptions (100% coverage). The description adds value by explaining the return format (top-N passages with offsets and scores) and providing example queries, which helps interpret the query parameter's semantics.

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

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

The description clearly states the tool performs semantic search inside a fetched record, specifying the action ('search') and resource ('a fetched record'). It distinguishes from siblings by mentioning the pairing with ask_pipeworx_grounded and contrasting with cases where the record is too large.

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: 'Use when the record is too big to cram into the prompt.' Also provides workflow guidance by pairing with ask_pipeworx_grounded and mentions the truncation behavior for very long inputs.

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 discloses OAuth requirement, delivery channel behaviors (feed always on, email, SMS with limits, webhook with HMAC and auto-disable), and supported types beyond what annotations provide. No contradiction with annotations (readOnlyHint=false, idempotentHint=true, etc.).

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 clear sections (purpose, requirement, types, delivery) and front-loaded main action. It is slightly verbose with details like the entire HMAC explanation, but each sentence adds value. Could be more concise, but still effective.

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

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

Given the complexity (3 params, nested objects, enum), the description covers purpose, requirements, supported types, delivery, and return value. No output schema, but it states returns id. Missing edge cases (idempotency behavior, error handling) but adequate for an agent to use correctly.

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

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

Schema coverage is 100%, but the description adds significant context: explains 'type' values with examples, details 'params' structure for each type, and clarifies 'delivery' options (e.g., SMS verification, webhook signing). This goes well beyond the schema descriptions.

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

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

The description clearly states 'Create a proactive monitoring subscription' (verb+resource) and 'Returns the new subscription id'. It distinguishes from siblings like 'unsubscribe', 'list_subscriptions', and 'recent_alerts' by focusing on creation.

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 specifies prerequisites ('Requires a Pipeworx OAuth account') and provides examples for each subscription type and delivery channel. While it doesn't explicitly state when not to use it, the context implies alternatives like 'recent_alerts' for pulling. Could add explicit exclusions for listing or reading.

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 nature. The description adds valuable context about returning live catalog examples and the exact tool+argument shapes, going beyond what annotations provide.

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

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

The description is front-loaded with common trigger phrases and each sentence adds value. However, it is slightly verbose; could be condensed without losing information, but still well-structured.

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

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

For a tool with no output schema, the description fully explains the input (optional topic), output (categorized example questions with tool shapes), and use case (onboarding). It is complete given the tool's 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% with a clear description of the optional `topic` parameter including example values. The description explains the effect of omitting vs providing the parameter, adding semantic 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 the verb 'return' and resource 'category-bucketed example questions' with specific detail about including tool+argument shapes. It distinguishes from sibling tools by positioning itself as the onboarding entry point.

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 this FIRST when you do not yet know what Pipeworx can do for you or to learn how to call the meta-tools', providing clear context for when to use it and the types of user queries it addresses.

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 key behavior beyond annotations: row is deactivated not deleted, and historical events remain available via recent_alerts. This adds significant value over annotations alone.

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 efficient sentences: first defines purpose and constraint, second explains durability. 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?

Fully self-contained for a single-parameter tool with no output schema. Covers what, constraints, and side effects.

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 context that the id must come from subscribe, clarifying expected input beyond the schema 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?

Description clearly states 'Cancel a subscription by id', specifying verb and resource. Distinguishes from sibling tools like 'subscribe' which creates rather than cancels.

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 ownership enforcement ('you can only cancel your own subscriptions'), setting clear boundaries for use. Does not mention alternative tools 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds value by detailing the output format (verdict, extracted structure, actual value with citation, delta) and the data source (SEC EDGAR + XBRL), enhancing transparency 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 front-loaded with examples and use cases, but it is somewhat lengthy and contains some redundancy. Still, every sentence contributes to understanding.

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

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

For a tool with one parameter and no output schema, the description adequately explains the return values and the process. It could mention limitations or error handling, but is largely complete given the tool's scope.

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

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

Schema description coverage is 100% for the single parameter, so baseline is 3. The description adds example values and clarifies that the input is a natural-language claim, but does not significantly extend 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 identifies the tool as a natural-language claim verification system for company-financial claims, provides example queries, and distinguishes it from sibling tools by noting it replaces multiple sequential calls.

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 ('whenever the agent needs to check whether something a user said is factually correct') and specifies the domain (company-financial claims for public US companies), providing clear context for selection.

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