Newsapi

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

Beyond the readOnly, openWorld, and non-destructive annotations, the description adds that the tool routes to 2,520 tools, fills arguments, and returns structured answers with stable URIs. This gives valuable insight into tool behavior not captured by 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 a key preference statement, then explains functionality, and ends with concrete examples. Every sentence serves a purpose 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 the single required parameter and no output schema, the description fully informs the agent: what it does, when to use it, what output to expect (structured answer with citations), and how many tools are behind it. No important gaps remain.

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

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

With 100% schema coverage and a clear parameter description ('Your question or request in natural language'), the description goes further by specifying the types of questions that work well, adding meaning beyond the schema.

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

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

The description clearly states the tool's function: routes factual questions to a large set of specialized tools and returns structured answers with citations. It explicitly prefers over web search and lists many data domains, making the purpose distinct from siblings.

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

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

The description provides strong positive guidance with examples of when to use the tool ('what is', 'look up', etc.). It also suggests preferring over web search. However, it lacks explicit when-not-to-use scenarios, such as subjective questions, but overall offers clear instructions.

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 valuable behavioral context: it fans out to different data packs based on bet type and returns an evidence packet with a market-vs-model comparison. 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 four sentences, well-structured with the core action first, then inputs, process, and use cases. It is front-loaded and each sentence contributes. Slightly long but not excessive.

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

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

Given no output schema, the description explains the return value: 'evidence packet plus a simple market-vs-model comparison'. It covers the tool's complexity (resolve, classify, fan-out) adequately. It could mention limitations or format, but it is sufficient for most use cases.

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

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

Schema coverage is 100% with clear descriptions for both parameters (market accepts slug/URL/text; depth has enum and default). The tool description does not significantly enhance parameter understanding beyond the schema—it adds context about how depth affects fan-out, but that's already in the schema description.

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

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

The description clearly states the tool's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the input types (slug, URL, question text) and the process (resolves market, classifies bet, fans out to packs). This distinguishes it from siblings like ask_pipeworx or validate_claim, which have different scopes.

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 to use it for 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It also implies it is preferable to discovering packs manually. However, it does not explicitly name alternatives or state when not to use it.

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

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

Annotations declare readOnlyHint=true and destructiveHint=false. The description adds context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) and that it returns citation URIs. It lacks details on latency or error behavior, but annotations cover the safety profile adequately.

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

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

The description is concise (three sentences), front-loaded with the core use case, and structured logically: general purpose, specific use cases per type, and efficiency claim. No redundant 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 the simple input schema (2 params) and no output schema, the description covers key aspects: entity types, data sources, and that it returns paired data with citations. It lacks return structure details, but the tool's simplicity and annotations make this acceptable.

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% (both parameters have descriptions). The description enriches this by specifying the exact financial metrics for company type (revenue, net income, cash, long-term debt) and drug data (adverse events, approvals, trials), going beyond the schema's basic 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: comparing 2–5 companies or drugs side by side. It uses specific verbs like 'compare' and lists example queries ('X vs Y', 'how do X, Y, Z stack up'), making it highly 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 use cases and example phrases (e.g., 'compare X and Y', 'which is bigger'), guiding when to invoke the tool. It mentions replacing 8–15 sequential calls, implying efficiency, but does not explicitly state when not to use it or 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 mark it as read-only and non-destructive. The description adds that it returns a ranked list of tools with names and descriptions, which informs behavior. It does not detail edge cases like empty results, but the core behavior is clear.

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, front-loads purpose and usage, and includes examples and return format. 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?

Given no output schema, the description explains the return format (top-N tools with names+descriptions) and usage context. It is complete for a simple discovery tool with 2 parameters and clear sibling tools.

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 both parameters fully with descriptions. The tool description does not add new parameter meaning beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool finds tools by describing a data or task, lists example domains, and specifies it returns top-N tools with names and descriptions. It distinguishes itself from sibling tools by being a discovery tool rather than a domain-specific one.

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 it ('browse, search, look up, or discover what tools exist') and advises calling it first when many tools are available. It also implies when not to use (not just one answer), providing clear usage 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 indicate readOnly and openWorld. The description adds substantial behavioral context: it returns recent filings, fundamentals, patents, news, LEI, and 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 concise (two sentences plus one on parameters) and front-loaded with the main purpose. Every sentence adds essential information without redundancy.

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

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

For a tool with no output schema, the description compensates by listing all returned data types and note about citation URIs. It also provides examples for parameter values, making it complete 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 coverage is 100% with descriptions for both parameters. The description adds extra context: for 'value', it clarifies acceptable inputs (ticker or CIK) and instructs to use resolve_entity for names. This adds value beyond the schema.

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

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

The description clearly states the tool's purpose: 'Get everything about a company in one call.' It lists specific data sources (SEC filings, revenue, patents, news, LEI) and distinguishes itself from siblings like resolve_entity and compare_entities by providing a consolidated profile.

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

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

Explicitly gives when-to-use scenarios ('when a user asks tell me about X...'), mentions it replaces 10+ pack tools, and includes clear caveats: 'Names not supported — use resolve_entity first if you only have a name.'

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 adds no behavioral context. It does not mention pagination, rate limits, or result 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 (one short phrase) but at the expense of being under-specified. Not front-loaded with actionable 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 11 parameters, no output schema, and a minimal description, the definition is completely inadequate for an AI agent to understand the tool's functionality and proper usage.

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

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

Schema description coverage is 0% and the description does not explain any of the 11 parameters. Parameter names like 'q', 'to', 'from' are ambiguous without further context.

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

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

The description 'Archive search' is a tautology of the name 'everything' and provides no specific verb or resource. It fails to distinguish from siblings like 'top_headlines' or '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?

No guidance is given on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description is a single vague phrase.

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 destructiveHint=true. The description adds context about deleting sensitive data and reasons for deletion. 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?

Two concise sentences. First sentence states purpose, second provides usage guidance. No wasted words, and purpose is front-loaded.

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

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

The tool is simple with one required parameter and no output schema. The description covers purpose, usage, and pairing, making it complete for an AI 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?

Schema coverage is 100% and includes a description for 'key.' The description mentions 'by key' but does not add meaningful semantics beyond what the schema already provides. Baseline score of 3 is appropriate.

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

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

The description clearly states 'Delete a previously stored memory by key,' specifying the verb (delete) and resource (stored memory by key). It distinguishes itself from sibling tools like 'remember' and 'recall' by focusing on deletion.

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 scenarios: 'when context is stale, the task is done, or you want to clear sensitive data.' Also advises pairing with 'remember' and 'recall,' offering clear usage 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?

Discloses rate limit (5/day), free cost, and that feedback affects roadmap. Annotations are complemented effectively; 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?

Concise three sentences plus rate-limit note; front-loaded with purpose; 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?

Covers feedback submission comprehensively; no output schema but not critical for this tool. Rate-limit and free info included. Minor gap: no mention of expected response.

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 reinforces enum meanings but doesn't add significant new semantics 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?

Clearly states the tool is for giving feedback to Pipeworx team, identifying specific categories (bug, feature/data_gap, praise), and distinguishes from siblings which are all data query 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?

Provides explicit when-to-use conditions for each type and cautions against pasting user prompts. Lacks explicit when-not-to-use but covers main use cases well.

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, openWorldHint, destructiveHint) are non-contradictory. Description adds behavioral details: walks child markets, groups related markets, returns ranked opportunities with reasoning. No missing critical traits.

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 yet comprehensive, structured with mode explanations and examples. Every sentence adds value, no redundancy.

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

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

Despite no output schema, description adequately covers return value (ranked opportunities with direction + reasoning). Handles complexity of two modes and cross-event grouping thoroughly.

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, and description enriches each parameter with purpose, mode context, and examples (e.g., event slug vs topic string). Adds meaning beyond schema types.

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

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

Description clearly states it finds arbitrage opportunities via monotonicity violations, specifies two modes ('event' and 'topic'), and distinguishes itself from siblings like 'polymarket_edges' by focusing on arbitrage detection.

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 each mode: event for single Polymarket event slug, topic for cross-event searches. Explains why cross-event mode catches cases missed by single-event mode. No explicit when-not-to-use, but context is clear.

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

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

Annotations declare readOnlyHint, openWorldHint, and destructiveHint=false. The description adds substantial behavioral details: uses a lognormal model from FRED and live coinpaprika price, groups by asset, fetches price history once, computes model probability, ranks by |edge|, and returns top N with direction. This goes beyond annotations, providing a clear picture of the tool's internal logic. Minor omission: no discussion of error handling or rate limits.

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

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

The description is relatively long and includes multiple sentences with details about the model and process. While it is well-structured and front-loaded with the main action, it could be more concise. Some sentences, like 'Built for the...' are informative but not essential. Overall, it is adequate but wordy.

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

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

Given the complexity of the tool (model, data sources, ranking) and the lack of an output schema, the description provides a good overview. It explains the process and return value (top N with direction). However, it lacks details on error cases, limitations (only crypto), and handling of edge cases. For a complex tool, more completeness would be expected.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add new information about parameters beyond what the schema already provides. It mentions 'top N' (mapping to limit) and 'volume window' (mapping to window), but these are already documented. No additional semantics or examples are given.

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 function: scanning high-volume Polymarket markets and returning those where Pipeworx data disagrees most with market price. It specifies the verb (scan/return), resource (Polymarket markets), and the core logic (disagreement measure). However, it does not explicitly differentiate from sibling tools like 'bet_research', so it lacks explicit sibling differentiation, falling short of a 5.

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 usage context: 'Built for the "what should I bet on today" question'. This implies when to use it (discovery), but does not explicitly state when not to use it or mention alternative tools. No exclusion criteria or comparisons are given, so it scores at the adequate level.

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, so safety is covered. Description adds scoping detail and pairing complements, but doesn't specify behavior on missing key or listing limits. Still, adds value beyond annotations with context on persistence and scope.

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 with clear front-loading: purpose first, then usage context, then pairing info. No wasted words, each sentence adds value. Excellent structure.

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 (one optional param, no output schema), description covers retrieval and listing adequately. Could mention error handling or response format, but not required for basic use. Complete enough to avoid confusion.

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

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

Schema describes key as optional string with description. Description adds semantics: key to retrieve, omit to list all, reinforcing purpose. Schema coverage is 100%, so description's addition is clear but not extensive.

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 'Retrieve a value previously saved via remember, or list all saved keys', specifying verb (retrieve) and resource (saved memory). Distinguishes from sibling tools 'remember' and 'forget' by pairing and contrasting.

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 use case: 'look up context the agent stored earlier'. Mentions scoping and pairing with remember/forget, giving clear guidance on when to use. Could be improved by adding explicit when-not-to-use or alternatives, but still clear for a simple retrieval tool.

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

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

Annotations already indicate read-only, open-world, non-destructive behavior. The description adds value by detailing the parallel fan-out to SEC EDGAR, GDELT, and USPTO, and describing the return structure (structured changes + count + URIs). No contradictions.

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

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

The description is front-loaded with the core purpose and example queries. It is concise, with every sentence providing essential information about usage, sources, parameter formats, and return values.

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 sufficiently describes the return format (structured changes, total_changes count, citation URIs). All three required parameters are clearly explained, and the use case is fully covered.

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 descriptions cover all parameters (100% coverage). The description enriches them by explaining the 'since' parameter formats (ISO date or relative shorthand like '7d') and that 'value' accepts ticker or CIK, going 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: retrieving recent changes about a company across multiple sources (SEC, news, patents). It provides example queries and distinguishes from siblings like entity_profile (full profile) and top_headlines (just headlines).

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 with example user queries. It explains the fan-out to multiple sources, giving context for its broad scope. While it doesn't explicitly state when not to use, the sibling tools provide clear alternatives.

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

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

Discloses key-value scoping by identifier, persistent vs 24-hour retention. Adds behavioral context beyond annotations (readOnlyHint=false, etc.).

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

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

Concise, front-loaded with purpose. Every sentence adds value; no extra 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?

Covers purpose, usage, and pairing. Lacks return value description, but not critical for a storage 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?

Input schema covers both parameters with descriptions and examples. Description adds no additional parameter meaning beyond schema.

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

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

Description clearly states verb 'Save' and resource 'data the agent will need to reuse later'. Distinguishes from siblings like recall 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?

Explicitly states when to use ('when you discover something worth carrying forward') and pairs with recall/forget for retrieval/deletion.

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 it's read-only, non-destructive, and open-world. The description adds that it returns pipeworx:// citation URIs and clarifies it performs a lookup that replaces multiple calls, enhancing transparency without contradiction.

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

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

The description is concise yet comprehensive, front-loading the core purpose followed by usage, examples, and return info. Each sentence serves a clear 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 adequately describes return values (identifiers and citation URIs). It covers input, purpose, usage context, and output format, making it reasonably complete for a lookup 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%, and the description goes beyond by explaining acceptable values for both parameters with examples (e.g., 'company' type accepts ticker, CIK, or name). This provides significant added context.

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

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

The description clearly states the tool resolves entity names to canonical identifiers (CIK, ticker, RxCUI, LEI) with specific examples for 'Apple' and 'Ozempic'. It distinguishes itself from siblings by noting it replaces 2–3 separate lookup calls.

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

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

Explicitly advises using this tool before others that need official identifiers. Provides examples and lists supported ID systems, though does not explicitly state 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 agent knows it's safe. However, the description 'News sources' adds no additional behavioral information, such as whether results are paginated, what the output format looks like, or any data freshness constraints. Given annotations, the description fails to enrich transparency.

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

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

At only two words, the description is extremely concise but at the expense of informativeness. It does not earn its brevity because it fails to convey purpose or usage. This is under-specification rather than effective 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 three optional parameters, no output schema, and 0% schema coverage, the description must provide substantial context. 'News sources' is completely inadequate. The agent cannot determine what the tool returns, how filters work, or how this differs from sibling tools.

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 description provides no parameter information. The three optional parameters (country, category, language) are not explained. The description does not hint at filtering or any semantics beyond the schema names, leaving the agent without clarity on valid values or behavior.

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

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

Description 'News sources' is a noun phrase lacking a verb. It vaguely indicates the tool returns news sources but does not clearly state an action like 'list' or 'search'. Sibling tool 'top_headlines' suggests a similar domain, but description does not differentiate how this tool is 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?

No guidance on when to use this tool versus alternatives like 'top_headlines' or 'bet_research'. No explicit context about filters or prerequisites. The agent must infer usage solely from the tool name and siblings.

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 open-world behavior, but the description adds no additional behavioral details such as rate limits, pagination, or response structure.

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 but at the cost of usefulness; the single phrase lacks structure and substance.

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 6 parameters and no output schema, the description is grossly incomplete, offering no guidance on input usage or 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 entirely fails to explain any of the 6 parameters (q, page, country, sources, category, pageSize).

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 'Current top headlines' clearly states the tool returns top headlines, but it is vague about what 'top' means and lacks differentiation from sibling tools like '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?

No guidance on when to use this tool versus alternatives; no context provided for appropriate usage scenarios.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds behavioral details: returns a verdict (confirmed, refuted, etc.), actual value with citation, and percent delta. It also mentions that it replaces 4-6 sequential calls, providing useful 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 concise at 5 sentences, each serving a clear purpose: purpose, usage, domain/version, returns, efficiency benefit. It is well-structured and front-loaded. Minor room for trimming but very effective.

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

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

Given no output schema, the description adequately explains return values (verdicts, citation, delta). It does not cover error handling or out-of-domain cases, but for the targeted use case, it is sufficiently complete.

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

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

Schema coverage is 100% with a detailed description for 'claim'. The tool description adds meaning by specifying the domain ('v1 supports company-financial claims') and providing examples, which enriches the parameter semantics beyond the schema alone.

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

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

The description clearly states the tool's purpose: fact-checking natural-language claims against authoritative sources. It specifies the domain (company-financial claims) and uses strong verbs like 'fact-check', 'verify', 'validate', 'confirm/refute'. This distinguishes 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?

The description provides explicit usage guidance: 'Use when an agent needs to check whether something a user said is true', along with example phrasings. However, it does not explicitly state when not to use it or directly compare to alternatives, which would make it a 5.

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