Ons Uk

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

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) are consistent. The description adds behavior beyond annotations: routes questions to the correct subtool, returns structured answers with citation URIs. It fully discloses the tool's operational model.

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 well-structured with a bold directive, comma-separated list of example categories, and specific example queries. It is slightly verbose but every part adds value; the front-loading is 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?

For a tool with one free-text input and no output schema, the description is comprehensive: it explains how the tool works (routing, citations), what sources it covers, and what types of questions are appropriate. No gaps remain for effective agent use.

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 one parameter 'question' described as 'Your question or request in natural language'. The description adds context about what kinds of questions work but does not enhance the parameter's meaning beyond the schema. Baseline 3 applies.

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

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

The description clearly defines the tool as a query interface for authoritative structured data across 1423+ tools, with specific examples like 'current US unemployment rate' and 'Apple's latest 10-K'. It distinguishes itself from web search and covers a wide range of factual queries.

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 'PREFER OVER WEB SEARCH' and provides detailed guidance on when to use: for factual questions about real-world entities, events, or numbers. Lists 20+ problem types and example queries, making the usage context unmistakable.

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=true, openWorldHint=true, destructiveHint=false. The description adds behavioral context: it 'pulls' data, 'fans out to the right packs', and 'returns an evidence packet plus comparison'. This enriches the agent's understanding 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 a single paragraph of 5 sentences. It front-loads the core purpose, then explains the process, then provides example queries, and ends with a value proposition. Every sentence is necessary and informative, with 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 lacking an output schema, the description fully explains what the tool returns: 'an evidence packet plus a simple market-vs-model comparison'. It also details the internal logic (resolving market, classifying bet, fanning out to relevant data packs), making the tool's behavior clear and complete for an agent.

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

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

Schema coverage is 100% and both parameters have descriptions. The description adds value by clarifying the effect of the depth parameter ('quick = 2-3 evidence sources, thorough = full fan-out') and the flexibility of market input (slug, URL, or question text). This goes beyond the schema's 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 it researches a Polymarket bet, resolves the market, classifies bet type, fans out to data packs, and returns an evidence packet with comparison. It distinguishes itself from siblings by positioning as the core demo product that agents should use for bet-relevant context.

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

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

The description explicitly provides use cases: 'should I bet on X?', 'what does the data say...', 'is there edge in this bet?'. It also implies priority by stating this is the core product and that agents using it convert better. However, it does not explicitly mention when not to use 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 indicate readOnlyHint=true and destructiveHint=false, and the description aligns by describing a data pull operation. It adds valuable behavioral context: for companies it pulls financial data from SEC EDGAR/XBRL, for drugs it pulls FAERS counts, FDA approvals, and trials. No negative behaviors are disclosed, but the critical safety profile is covered.

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 at five sentences, front-loads the core purpose, and uses bullet-like structure for the two types. Every sentence provides essential information without redundancy or fluff.

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

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

Given the tool's complexity (two entity types, multiple data sources) and no output schema, the description adequately covers the return format (paired data + citation URIs) and data sources. It is sufficient for an agent to decide when to invoke but lacks detail on exact field names or pagination, which is acceptable for a selection-use description.

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema: it explains the interpretation of the 'values' array for each type (tickers/CIKs for companies, drug names for drugs) and what data fields are returned (revenue, net income, etc.). This helps the agent understand parameter 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 specifies a clear verb 'Compare' and resource '2–5 companies or drugs', with explicit examples of user queries that trigger the tool. It distinguishes itself from sibling tools by stating it replaces '8–15 sequential agent calls', making its purpose distinct.

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 guidance with specific user phrase examples like 'compare X and Y', 'X vs Y'. It explains the two entity types and their data sources. However, it lacks explicit when-not-to-use or alternative tool recommendations, which prevents a higher score.

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 destructiveHint=false, so the description adds no new behavioral context. It does not contradict annotations, but fails to provide additional details beyond structured 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?

The description is extremely short (3 words), which is concise but lacks necessary detail. Could be improved while maintaining brevity.

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 and presence of annotations, the description is too sparse. Missing clarification of what 'metadata' includes or the expected output.

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 the 'id' parameter (e.g., format, required dataset type). No value added 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 dataset metadata.' clearly indicates the tool returns metadata for a single dataset, differentiating it from the sibling 'datasets' tool which likely lists all datasets.

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 'datasets', 'editions', or 'series'. Context must be inferred from the name alone.

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 and non-destructive behavior. The description adds the qualifier 'publicly available', but does not disclose pagination behavior or how results are returned, which is an important behavioral trait for a list tool.

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

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

The description is a single, clear sentence but is overly minimal. It could include parameter details or usage hints 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 no output schema and simple parameters, the description should clarify return format, pagination defaults, or how to retrieve all datasets. Currently, it leaves significant gaps for a list 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 0% description coverage, and the description does not mention or explain the 'limit' and 'offset' parameters. The agent receives no guidance on their meaning or typical 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 verb (list), resource (datasets), and scope (publicly available), effectively distinguishing it from sibling tool 'dataset' which likely targets a specific dataset.

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 versus alternatives, nor does it mention any prerequisites or exclusions. Sibling tools like 'dataset' could be confused without further 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 (readOnlyHint=true, destructiveHint=false) are consistent. The description adds that it returns 'top-N most relevant tools with names + descriptions,' which is useful 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?

The description is three sentences, front-loaded with purpose, then usage context and examples. Every sentence adds value with no fluff.

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

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

Given the tool's simplicity, rich annotations, and full schema coverage, the description is complete. It explains what the tool does, when to use it, and what it returns.

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 both parameters described. The description adds examples for the query parameter, 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: 'Find tools by describing the data or task.' It specifies the verb 'find' and resource 'tools,' and distinguishes from siblings by advising to call this first to see the option set.

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

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

The description explicitly says when to use: 'Use when you need to browse, search, look up, or discover what tools exist for...' and advises calling this first when many tools are available. It also lists specific domains, providing clear context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds no behavioral context beyond that, such as output format, pagination, or side effects.

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

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

Single sentence is very concise, but it sacrifices completeness. It earns its place but could be restructured to include more critical information.

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

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

With no output schema and minimal annotations, the description fails to explain what the tool returns (e.g., list of edition objects) or any limitations. It is insufficient for an agent to understand the tool's outcome.

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

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

Schema description coverage is 0%, so the description must compensate. It only hints that 'id' is a dataset identifier via the phrase 'of a dataset,' but does not explicitly describe the parameter's role or expected format.

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 is 'Editions of a dataset,' lacking a verb to indicate action (e.g., list, get). It implies the tool returns editions but does not clearly state what operation it performs or how it differs from sibling tools like 'dataset' or 'datasets.'

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. There is no mention of prerequisites, context, 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 already indicate readOnlyHint=true and destructiveHint=false, so the description's burden is lower. It adds valuable behavioral context: what data is returned (SEC filings, fundamentals, patents, news, LEI) and that results include pipeworx:// citation URIs. It does not contradict annotations. A score of 4 reflects that it enriches the annotation-provided safety profile without needing to repeat it.

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 long, front-loaded with the core purpose, and every sentence adds information. There is no redundant or filler content. It achieves maximum information density with minimal 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 the tool's moderate complexity (multiple domains aggregated) and the absence of an output schema, the description adequately lists return types (SEC filings, revenue/net income/cash, patents, news, LEI) and mentions citation URIs. It does not cover potential pagination or result limits, but with openWorldHint annotation and the aggregate nature, this is a minor gap. The description is largely complete for an agent's invocation needs.

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 adds meaningful extra semantics: it clarifies that only 'company' type is supported (the enum in schema already shows this, but description reinforces it), explicitly states that ticker or zero-padded CIK are acceptable values for 'value', and notes that names are not supported (pointing to resolve_entity). This goes 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 the tool's purpose: 'Get everything about a company in one call.' It provides specific use cases and examples (e.g., 'tell me about X', 'research Microsoft'), and distinguishes itself from siblings by noting it replaces calling 10+ pack tools. This fully meets the 'specific verb+resource' and sibling differentiation criteria.

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 (when a user asks for a profile, brief me, etc.) and when not to use it (for names, use resolve_entity first). It provides clear context and one specific alternative (resolve_entity), but does not fully enumerate all sibling alternatives. Nonetheless, it offers strong guidance for correct invocation.

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?

Consistent with annotations (destructiveHint=true), and adds context about clearing sensitive data. However, does not detail behavior for non-existent keys or return values, though tool is simple enough.

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 with no extraneous information. Action is immediately stated, followed by usage guidance and sibling mentions.

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 single-parameter deletion tool with no output schema, the description covers purpose, usage context, and relationships, making it complete.

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

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

Schema has 100% coverage for 'key' parameter with description 'Memory key to delete.' Description adds little beyond 'by key,' but provides baseline value as schema already sufficient.

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 'Delete a previously stored memory by key,' specifying verb and resource. Differentiates from sibling tools like 'remember' and 'recall' by describing the inverse operation.

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 (stale context, task done, clear sensitive data) and recommends pairing with 'remember' and 'recall,' providing clear guidance 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 already declare readOnlyHint=true and destructiveHint=false, so the tool's safety profile is clear. However, the description adds no additional behavioral context, such as response structure or limitations.

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 short sentence, which is concise but at the expense of clarity. It is not informative enough to warrant the brevity.

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 3 parameters and no output schema, the description should explain what the tool returns and how to use it, but it only mentions 'metadata'. This is insufficient for effective tool selection.

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 the meaning of parameters 'id', 'edition', or 'version'. The agent gets no help understanding required inputs.

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

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

Description 'Latest observation data file metadata' is vague about the tool's action (e.g., retrieve, list). It doesn't specify what the tool does relative to its name, leaving purpose ambiguous.

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 siblings like 'dataset', 'datasets', or 'editions'. No context or alternative suggestions are provided.

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

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

Beyond annotations, it discloses rate limits (5 per identifier per day), that it's free and doesn't count against tool-call quota, and that team reads digests daily. 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?

Well-structured, front-loaded with purpose, then usage, then instructions, then constraints. Every sentence serves a purpose 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 no output schema, the description covers all needed context: how to provide feedback, what to include, constraints, and outcomes. Complete for its purpose.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by advising specificity in message and explaining the context object fields within the description.

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

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

Description clearly states the tool's purpose: telling the Pipeworx team about bugs, missing features, data gaps, or praise. It distinguishes itself from sibling tools which are focused on data retrieval and analysis.

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

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

Explicitly provides when-to-use scenarios for each feedback type, warns not to paste end-user prompts, and notes rate limits and quota-free 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 and destructiveHint=false, which the description aligns with. It adds behavioral context: the tool walks child markets, searches for related markets, groups them, checks monotonicity, and returns ranked opportunities with suggested trade direction and reasoning. 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 well-structured single paragraph, roughly 6 sentences, each earning its place. It front-loads purpose, then clearly explains the two modes and their differences, with no wasted words.

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

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

Given no output schema, the description explains return format: 'ranked opportunities with suggested trade direction + reasoning.' It covers both modes thoroughly, addresses edge cases (cross-event detection), and provides enough context 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 input schema has 2 parameters with 100% description coverage. The description adds meaning beyond schema: explains event slug vs topic, how each mode works internally (walks child markets, searches across events), and provides examples ('Strait of Hormuz'), significantly enriching usability.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It specifies two distinct modes (event and topic) and what each does, making it fully distinguishable from sibling 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 explicit when-to-use guidance: event mode for a single event slug checking its child markets, topic mode for cross-event arbitrage. It explains why cross-event mode is needed (Polymarket listing cutoffs as separate events), helping the agent choose correctly.

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

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

Despite annotations already declaring readOnlyHint=true, the description adds rich behavioral details: the V1 process (groups by asset, fetches price history once, computes model probability, ranks by edge). It explains what data sources are used (FRED, coinpaprika) and that it is a read-only computation, fully disclosing 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 front-loaded with the main purpose and process, but it is a single paragraph with multiple sentences. Some redundancy exists ('by hand' phrase), but overall it is efficient and earns its sentences. Slightly verbose for 5.

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

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

Given the tool has 3 parameters (all documented with enums), no output schema, and moderate complexity, the description covers the entire workflow: scanning top markets, grouping, data fetching, model computation, ranking, and return value (top N edges with trade direction). It fully informs the agent about what the tool does and returns.

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 each parameter described. The description adds context for limit and min_edge_pp within the ranking process, but this does not significantly add beyond the schema's own descriptions. Baseline value according to guidelines.

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, finds where Pipeworx data disagrees most, and returns top edges with trade direction. It specifies the scope (crypto-price bets, V1) and verb 'scan' with resource, distinguishing it from siblings like polymarket_arbitrage.

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

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

The description explicitly frames the tool for the 'what should I bet on today' question, indicating when to use. It mentions discovering opportunities without manual browsing, but does not provide explicit when-not or alternative tools, limiting guidance.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds scoping detail ('Scoped to your identifier') and pairing advice, beyond what annotations provide. No contradictions. Could mention rate limits or response formatting, but not essential given the simple operation.

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, front-loaded with the core action, and each sentence adds value (purpose, when-to-use, scoping, pairing). No wasted words or redundancy.

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

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

Given the tool's simplicity (1 optional param, no output schema, no nested objects), the description is fully adequate. It covers purpose, usage context, scoping, and relationships with sibling tools. No missing essential information.

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 1 optional key parameter with description 'Memory key to retrieve (omit to list all keys)'. The description reiterates this but adds no new semantic detail. With 100% schema coverage, the description does not significantly enhance parameter 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's purpose: 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' It specifies the verb (retrieve/list), the resource (memory values), and distinguishes it from siblings like remember and forget.

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

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

The description explains when to use ('to look up context the agent stored earlier') and pairs with remember and forget. It implies when-not by omission, but doesn't explicitly state alternatives for other use cases. Provides sensible context for agent decision-making.

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 key behaviors: parallel fan-out to three sources, accepted date formats (ISO or relative shorthand), and return structure (structured changes, total_changes count, citation URIs). This adds substantial value beyond the annotations, which only mark it as read-only.

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

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

The description is a single paragraph of five sentences, front-loaded with the core question and examples. Every sentence adds information without repetition or fluff. Highly efficient.

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

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

The description covers input semantics, data sources, and output components adequately for a monitoring tool with three parameters and no output schema. It could mention pagination or rate limits if applicable, but these are not critical for typical use.

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 covers all three parameters with descriptions, but the description enriches them: clarifies 'type' only supports 'company', provides examples for 'since' (ISO and relative), and explains 'value' can be ticker or CIK. This adds practical meaning that an AI agent can use.

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 'What's new with a company in the last N days/months?' and gives concrete example queries, making the purpose unmistakable. It specifies it fans out to multiple sources (SEC, GDELT, USPTO), distinguishing it from sibling tools like entity_profile which are broader.

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 example queries and a recommendation for the 'since' parameter ('Use "30d" or "1m" for typical monitoring') provide clear usage context. However, it does not mention when to avoid this tool or outline alternative sibling tools, leaving room for improvement.

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 scoping by identifier, persistence differences between authenticated and anonymous users, and retention duration (24 hours for anonymous). This adds value beyond the annotations, which only indicate it is not read-only, not open-world, and not destructive.

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 long, front-loaded with the core purpose, and every sentence adds essential information. No redundant or extraneous content.

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

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

For a simple two-parameter tool with no output schema, the description fully covers purpose, usage, behavior, and pairing with sibling tools. It is complete for an agent to select and 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 input schema already provides 100% coverage with descriptions for 'key' and 'value'. The description adds contextual meaning by explaining key-value pair scoping and giving example keys, surpassing the baseline of 3.

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

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

The description uses specific verbs ('save', 'reuse') and resources ('data', 'conversation', 'sessions') to clearly state the tool's purpose. It distinguishes from the sibling tools 'recall' and 'forget' by mentioning them explicitly.

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 the tool ('discover something worth carrying forward') and pairs it with siblings for retrieval and deletion. No exclusions are needed.

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) are consistent with a lookup operation. The description adds that return values include 'pipeworx:// citation URIs' and lists the types of IDs returned. It does not discuss error handling or rate limits, but the strong annotation coverage reduces the burden. 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 front-loaded with the main action and is concise overall. It includes necessary examples and lists of ID systems without excessive verbosity. Minor redundancy exists (e.g., both mentions of 'CIK'), but it remains 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 simple parameter set (2 required, no output schema), the description is largely complete. It explains the purpose, usage context, return values (IDs + citation URIs), and gives examples. It does not address ambiguous inputs or errors, but this is acceptable for a straightforward lookup tool with openWorldHint=true.

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 baseline is 3. The description provides examples that contextualize the parameters (e.g., 'For company: ticker (AAPL), CIK (0000320193), or name'), but does not add substantial meaning beyond what the schema already defines. The description is helpful but not exceptional.

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: 'Look up the canonical/official identifier for a company or drug.' It specifies the types of identifiers (CIK, ticker, RxCUI, LEI) and provides concrete examples (e.g., 'Apple' → AAPL, CIK 0000320193). This distinguishes it from sibling tools that likely require these identifiers as inputs.

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 when to use the tool: 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' It also notes it 'Replaces 2–3 lookup calls,' but does not mention scenarios where the tool should not be used or provide alternative tools for cases where no ID is needed.

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 the tool as read-only and non-destructive. The description does not add any additional behavioral context such as rate limits or data freshness, but neither does it contradict 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 extremely concise at only six words. While it could benefit from more detail, it contains no wasted information and is clear in its brevity.

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 absence of an output schema, the description should provide more context about what data is returned (e.g., time points, metadata). The current description is too minimal for an agent to fully understand the tool's output.

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

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

The input schema has 100% description coverage for the only parameter 'uri', including an example. The description adds no extra meaning beyond what the schema provides, so it meets the 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 conveys the tool's purpose: to retrieve a time series using an ONS URI. It uses a specific verb ('by' implies retrieval) and resource ('Time series'), but does not explicitly differentiate from sibling tools like 'dataset' which might also return 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?

No guidance is provided on when to use this tool versus alternatives, nor are there any prerequisites or exclusions. The description simply states the action without contextual advice.

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, destructiveHint), description reveals return verdict types (confirmed, refuted, etc.), extracted structure, actual value with citation, and percent delta. Full transparency with no contradictions.

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

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

Three sentences: first states purpose and domain, second gives usage guidance with examples, third summarizes output. No wasted words, front-loaded with 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?

Given the tool's simplicity (1 param, no output schema) and complete annotation coverage, the description fully covers purpose, usage, domain, and return format, leaving no gaps for agent understanding.

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

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

Schema coverage is 100% for the single 'claim' parameter, and description adds context about supported claim types (e.g., company-financial) and examples, going beyond the schema description 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?

Description clearly states the tool validates natural-language factual claims against authoritative sources, specifies domain (company-financial claims via SEC EDGAR), and distinguishes from siblings like ask_pipeworx 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?

Explicitly says when to use ('check whether something a user said is true') with examples, and notes it replaces multiple sequential calls. Does not provide explicit when-not-to-use exclusions, but domain restriction is implied.

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