Librivox

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds value by explaining that the tool routes to many sources, fills arguments, and returns structured answers with citation URIs. 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 key purpose and usage guidance. It is slightly verbose but every sentence contributes value. Could be split for readability, but overall 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?

For a complex tool routing to many sources with a single input, the description covers purpose, usage, types of queries, and output format (citations). It lacks details on failure modes or latency, but is sufficient given the schema and annotations.

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

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

Schema coverage is 100% with a single 'question' parameter. The description adds no direct parameter details beyond the schema, but provides extensive examples of the types of questions to ask, which compensates and adds practical context for the free-form input.

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: it routes questions to 1,423+ tools across verified sources and returns structured answers with citations. It explicitly contrasts with web search and gives specific examples of data types, making the purpose unmistakable.

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

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

The description explicitly says 'PREFER OVER WEB SEARCH' and lists many use cases and example queries. While it doesn't explicitly state when not to use, the strong guidance and examples provide clear context for 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?

Annotations already indicate readOnlyHint=true (safe read) and destructiveHint=false. The description adds no behavioral details beyond that, but does not contradict annotations. It leaves out potential behaviors like error handling or return format.

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 at 5 words, front-loading the purpose. However, it may be too brief, missing an opportunity to add minimal context without verbosity.

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

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

Given one required param, no output schema, and existing annotations, the description is insufficient. It does not mention return type, error behavior, or examples, leaving the agent underinformed for a simple but data-critical tool.

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

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

The schema has one parameter 'id' (number) with 0% description coverage. The description does not explain what 'id' represents (e.g., Audible ID, format), leaving the agent without semantics beyond the schema.

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

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

The description 'Single audiobook by id.' clearly states the tool retrieves one audiobook based on an ID, distinguishing it from the sibling 'audiobooks' tool which likely lists multiple. It is specific but lacks verb scope (e.g., 'retrieve' or 'get').

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

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

No guidance on when to use this tool versus alternatives like 'audiobooks' for listing or other search tools. The description does not mention context or exclusions.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds no additional behavioral context (e.g., pagination, result format), but also does not contradict 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 extremely concise (one sentence), which is front-loaded, but it provides little information beyond the name. It is not verbose but also not particularly helpful.

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 a search tool with six parameters and no output schema, the description is incomplete. It does not explain what the search returns, how filters combine, or pagination behavior.

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

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

Schema description coverage is low (33%); only 'query' and 'since' have descriptions. The tool description adds no explanation for the other four parameters (limit, title, author, offset), failing to compensate for the coverage gap.

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 'Search audiobooks.' states the verb and resource, but is minimal. It distinguishes from sibling 'audiobook' (singular) by implying a search bar operation, but it is essentially a tautology of the name.

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

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

No guidance is provided on when to use this tool vs alternatives like 'audiobook' or other sibling tools. There is no mention of prerequisites, exclusions, or 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 indicate readOnlyHint=true and destructiveHint=false, so the agent knows the tool is safe. However, the description adds no behavioral details beyond the annotations, such as pagination behavior, rate limits, or any specific constraints.

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 extremely terse (two words), which is under-specification rather than conciseness. It fails to provide necessary context in a structured manner.

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

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

With four parameters and no output schema, the description is completely inadequate. It offers no information about return values, filtering behavior, or any other operational context that a tool of this complexity should have.

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

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

Schema description coverage is 0% and the description does not explain any of the four parameters (limit, query, offset, last_name). The agent receives no guidance on how to use these parameters effectively.

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 'Search authors' specifies a verb and resource, but lacks specificity about which aspects of authors are searched (e.g., by name, by associated works). It is vaguely defined and does not help distinguish the tool from potential sibling search 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 provides no guidance on when to use this tool vs. alternatives, nor any exclusions. No context is given about typical use cases or when not to use it.

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

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

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) are complemented by the description which details the tool's process: resolving the market, classifying the bet, fanning out to packs, and returning a comparison. 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 a single paragraph that efficiently lists inputs, process, and use cases. While it could be trimmed slightly, it is well-structured and front-loaded with the key action.

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

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

With no output schema, the description adequately describes the return as 'evidence packet plus a simple market-vs-model comparison'. It covers inputs, process, and usage context. The lack of output format details is acceptable 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 descriptions for both parameters. The description adds value by clarifying that the 'market' parameter accepts slug, URL, or question text, and explains the 'depth' parameter's behavior ('quick = 2-3 evidence sources, thorough = full fan-out'). This goes beyond what the schema provides.

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

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

The description clearly states the tool's purpose: to research a Polymarket bet by pulling relevant Pipeworx data. It specifies inputs (slug, URL, question text) and outputs (evidence packet with model comparison). It distinguishes itself from siblings by being the core demo product that automatically fans out to appropriate data packs.

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

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

The description explicitly provides use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It implies use instead of manual discovery but doesn't explicitly state when not to use or mention alternatives.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds rich behavioral context beyond annotations: it details data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), specific financial fields (revenue, net income, cash, debt), and output format (paired data with citation URIs). No contradiction with annotations.

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

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

The description is a single dense paragraph but well-structured: starts with purpose and constraints (2-5 entities), then usage triggers, then type-specific details. It is concise (4 sentences) and front-loads the key function. Minor improvement could be bullet points for readability, but overall 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 complexity (two entity types, multiple data sources), the description covers purpose, when to use, data sources, and output format (paired data + citations). No output schema exists, so the description adequately explains return values. It lacks explicit format details (e.g., exact table structure) but is sufficient for an AI agent.

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

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

Schema description coverage is 100%, so the schema already documents both parameters. However, the description adds significant meaning beyond the schema: for 'type' it links to specific data sources and fields, and for 'values' it provides examples (tickers/CIKs for companies, drug names). This compensates for the schema's brief descriptions.

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

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

The description clearly states the tool compares 2-5 companies or drugs side by side, with specific verb 'compare', resource 'entities', and scope. It distinguishes from sibling tools by noting it replaces 8-15 sequential agent calls, implying bulk comparison efficiency.

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 when-to-use triggers like 'compare X and Y', 'X vs Y', and covers both company and drug use cases. While it doesn't explicitly state when not to use, the examples cover common scenarios, 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.

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

Annotations (readOnlyHint=true, destructiveHint=false) already declare safe read behavior. Description adds return format (top-N relevant tools with names/descriptions) but no extra behavioral details like result ordering or error handling.

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?

Efficient use of three sentences: first states purpose, second gives usage context with examples, third explains return format and provides call-to-action. No filler.

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

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

Despite missing output schema, description clarifies return format adequately. Covers purpose, usage, and examples. Minor gap: no mention of edge cases like empty results or query interpretation.

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 provides 100% description coverage for both parameters. Description uses 'top-N' hinting at limit but adds no new semantic beyond schema; baseline score 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 specifies the tool finds tools by describing data or task, listing many example domains. It clearly distinguishes itself as a meta-tool for browsing the available tool set, unlike siblings that perform specific actions.

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

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

Explicitly states when to use ('browse, search, look up, discover') and advises calling it first when many tools are available. Lacks explicit 'when not to use' but context implies alternatives are other tools directly.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds behavioral context beyond annotations by detailing the returned data (SEC filings, fundamentals, patents, news, LEI) and the limitation to company type only. 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 a single paragraph but conveys all necessary information without redundancy. It is front-loaded with the core purpose and then details use cases and return types. Slight lack of structure (e.g., bullet points) but still effective and concise.

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

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

Given no output schema, the description fully explains the tool's output (recent SEC filings, fundamentals, patents, news, LEI). It covers the complexity of aggregating multiple data sources and the tool's limitations. The two parameters are well-documented, and the context for when to use this tool is complete.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds meaning by explaining that 'type' is limited to 'company' and that 'value' accepts ticker or CIK but not names, with examples. This compensates for any potential ambiguity 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 explicitly states 'Get everything about a company in one call' and lists specific use cases ('tell me about X', 'research Microsoft'). It clearly distinguishes itself from siblings like resolve_entity by specifying that names are not supported and that it aggregates data from multiple sources.

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 when-to-use scenarios (user asks for a company profile) and when-not-to-use ('Names not supported — use resolve_entity first'). It also implies alternatives by mentioning the need to call 10+ pack tools otherwise, differentiating from sibling tools.

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

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

Annotations already include destructiveHint: true, and the description reinforces this with 'delete' and adds context on why (stale data, sensitive data). Adds value beyond annotations 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?

Two sentences: first states purpose, second gives usage guidance. No wasted words, front-loaded, and highly efficient.

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

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

For a simple one-parameter destructive tool, the description fully covers when and why to use it, paired with annotations. No output schema needed; complete for effective invocation.

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

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

Schema coverage is 100% with a clear description for the key parameter. The description does not add additional meaning beyond the schema's definition of 'Memory key to delete', so baseline score applies.

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

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

The description clearly states it deletes a previously stored memory by key, using specific verb and resource. It distinguishes from siblings by referencing remember and recall, establishing it as part of a memory management trio.

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 specifies when to use: when context is stale, task is done, or to clear sensitive data. Provides pairing with remember and recall, offering clear guidance on alternative tools within the same family.

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

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

Annotations indicate readOnlyHint=false, openWorldHint=false, destructiveHint=false, which align with a feedback submission tool. The description adds behavioral details not in annotations, such as rate limiting (5 per identifier per day), that feedback directly affects the roadmap, and that it does not count against tool-call quota. 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 a single paragraph that is well-organized, starting with purpose, then usage, then specifics. It is reasonably concise, though slightly more verbose than necessary; however, every sentence adds value.

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

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

Given the tool has three well-documented parameters, no output schema, and the description covers all relevant aspects (usage, rate limits, formatting), the description provides complete contextual information for an AI agent to use the tool correctly.

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

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

Schema coverage is 100%, but the description adds valuable semantic guidance beyond the schema: it instructs to describe the issue in terms of Pipeworx tools/packs and not to paste the end-user's prompt. This helps ensure useful feedback and correct usage of the 'message' parameter.

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 is for providing feedback to the Pipeworx team, enumerating specific categories (bug, feature, data_gap, praise). It distinguishes this tool from sibling tools like ask_pipeworx or discover_tools by its specific purpose of reporting issues or praise to the team.

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 outlines when to use the tool (e.g., when a tool returns wrong data, when a desired tool is missing, or for praise), provides instructions on formatting feedback, and mentions constraints such as rate limits and that it is free. This gives clear guidance on 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?

The description outlines the tool's two operational modes and the return format (ranked opportunities with reasoning). Annotations already declare readOnly and non-destructive behavior; the description adds valuable context about how results are derived 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 concise yet complete, front-loading the core purpose and cleanly separating the two modes. Every sentence adds value, with no redundancy or filler.

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

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

Despite no output schema, the description adequately specifies the return values (ranked opportunities with trade direction and reasoning). The tool's complexity is well covered given the annotations and parameter descriptions.

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 beyond schema by explaining each parameter's role within the corresponding mode and providing concrete examples (e.g., event slug 'when-will-bitcoin-hit-150k'). This justifies a score above baseline.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities on Polymarket via monotonicity violations. It specifies two distinct modes (event and topic), making the function unique and distinguishable from sibling tools like 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?

The description provides explicit guidance on when to use each mode, including an example where single-event mode fails (May≤June rule) and cross-event mode succeeds. It effectively tells the agent when to choose topic mode over event mode.

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, open-world, non-destructive. The description adds extensive behavioral details: covers crypto-price bets, explains the model (lognormal from FRED + live coinpaprika), algorithm steps (scan, group, fetch price history once, compute probability, rank by edge), and output (top N with suggested direction). 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 purpose, second gives technical methodology, third describes output, fourth provides use case. 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?

The description covers model, data sources, algorithm, parameters, and return type (top N ranked with direction). Lacks explicit output field details, but given no output schema, it provides sufficient context for a discovery 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 context by explaining limit as 'Top N edges to return after ranking', window as volume filter, and min_edge_pp as threshold. This goes beyond schema but is not essential given 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 the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with the market price. It specifies the verb (scan, return), resource (Polymarket markets), and outcome (ranked by edge magnitude with trade direction). Distinguishes from sibling polymarket_arbitrage by focusing on disagreement/edge discovery.

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

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

The description explicitly states the intended use case: 'what should I bet on today' and notes it saves paging through hundreds of markets. However, it does not explicitly mention when not to use it or compare to alternatives like polymarket_arbitrage, though 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 declare readOnlyHint=true. The description adds context: lists keys if key omitted, scoped to identifier. 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?

Three efficient sentences. Front-loaded with 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?

No output schema, but description explains return values (value or key list). Covers usage, scope, and pairing. Complete for a simple 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 covers 100% of parameter documentation. Description confirms the omission behavior, adding no new semantic detail 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 specifies the verb (retrieve/list), resource (saved values/keys), and distinguishes from sibling tools like 'remember' and 'forget'. It explains the context of earlier storage.

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

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

Provides explicit examples of when to use (look up stored context like ticker, address, notes) and mentions pairing with siblings. Lacks explicit when-not or alternative 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?

Discloses the parallel fan-out to SEC EDGAR, GDELT, and USPTO, the return structure (structured changes + count + URIs), and the accepted 'since' formats. Annotations already indicate readOnly, but the description adds valuable behavioral 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?

Concise with front-loaded purpose; each sentence adds necessary detail without redundancy. Efficient use of language.

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 covers all key aspects: input parameters, supported values, data sources, and output shape. Provides sufficient information for correct invocation.

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

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

Schema coverage is 100% and description adds meaning: explains that 'type' only supports 'company', gives examples for 'since' (ISO date or relative shorthand), and clarifies 'value' accepts ticker or CIK. Examples like '30d' and 'AAPL' are helpful.

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's purpose: 'What's new with a company in the last N days/months?' with specific example queries ('what's happening with X?'), distinguishing it from sibling tools like entity_profile or compare_entities.

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

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

Provides explicit when-to-use guidance with natural language examples and mentions fanned-out sources, but does not explicitly state when not to use or suggest alternatives. Still clear enough for typical 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?

Adds important behavioral context beyond annotations: scoped by identifier, persistent for authenticated users, 24-hour memory for anonymous. No contradiction with annotations (readOnlyHint false, destructiveHint false). Could mention overwrite behavior but still strong.

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 that efficiently front-loads the purpose, then guides usage, then provides technical details. No wasteful sentences.

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

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

The tool is simple (2 string params, no output schema). The description covers purpose, usage, persistence, and sibling tools completely. No 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?

Both key and value are well-described in the schema (100% coverage). The description adds context (e.g., example key formats) but doesn't significantly enhance semantics beyond the schema. 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 the tool saves data for later reuse across conversations or sessions, using a key-value store. It distinguishes from siblings like recall and forget by specifying the action and context.

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

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

Explicitly describes when to use ('when you discover something worth carrying forward') and pairs with recall and forget. Mentions persistence differences for authenticated vs anonymous users, providing clear guidance.

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

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

Annotations already declare readOnlyHint and destructiveHint false; description adds that it returns IDs plus citation URIs, specifies the ID systems (CIK, ticker, RxCUI, LEI), and gives output examples. 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?

Description is concise (4 sentences), front-loaded with main purpose, and every sentence contributes value—purpose, when to use, examples, and output format.

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, usage context, output format with examples. No output schema, so description compensates adequately. Could mention that for companies it returns both CIK and ticker, but overall 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 already has 100% coverage with descriptions for both parameters. Description adds meaning through examples (e.g., 'Apple' → AAPL, 'Ozempic' → RxCUI) clarifying how to use the 'value' parameter.

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

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

Clearly states "Look up the canonical/official identifier" with specific verb+resource, distinguishes itself from siblings by mentioning it provides identifiers needed by other tools, and includes concrete examples (Apple → AAPL, Ozempic → RxCUI).

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 to use this tool before calling other tools that need official identifiers, and mentions it replaces multiple lookup calls. Does not explicitly state when not to use it, but context is clear.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to repeat that. However, it adds no additional behavioral details 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 very short (4 words), which is efficient but lacks important details. It is not overly verbose, but could include more information without sacrificing conciseness.

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

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

Given the low schema coverage and no output schema, the description is incomplete. It does not explain what a 'project_id' is, what the output contains, or any constraints.

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

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

The only parameter, 'project_id', is not described in the description. With 0% schema description coverage, the description should compensate but does not.

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

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

The description uses a specific verb 'List' and identifies the resource as 'tracks for an audiobook', making the action clear. However, it does not differentiate from sibling tools like 'audiobook' or 'audiobooks', which could cause confusion about scope.

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

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

No guidance is provided on when to use this tool versus alternatives. There are no usage contexts, prerequisites, or exclusions mentioned.

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

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

The description adds significant behavioral context beyond the annotations (readOnlyHint, openWorldHint, destructiveHint). It details the return verdict types, structured output, citation format, and explains the tool replaces multiple sequential calls, enriching the agent's understanding of its behavior.

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

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

The description is concise (5-6 sentences) and front-loaded with the primary action. Every sentence adds value, covering purpose, usage, domain limitations, and output details 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 the simple input (one string parameter) and no output schema, the description fully covers expected behavior: domain, input examples, verdict types, and output structure (with citation). It is sufficiently complete for an agent to invoke correctly.

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

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

The single parameter 'claim' has 100% schema description coverage. The tool description reinforces this with concrete examples ('Apple's FY2024 revenue was $400 billion'), adding semantic value beyond the schema, though the schema itself is already clear.

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: fact-checking natural-language factual claims against authoritative sources, specifically for company-financial claims. It distinguishes from sibling tools like 'ask_pipeworx' or 'compare_entities' by its unique function.

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

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

The description provides explicit usage context ('Use when an agent needs to check whether something a user said is true') and examples. It implicitly excludes non-financial claims by stating the domain limitation (v1 supports company-financial claims). However, it does not explicitly list alternatives or when not to use.

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