Deno Land

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

Description adds beyond annotations: reveals it routes to 2,520 tools, fills arguments, outputs structured answers with pipeworx:// citation URIs. Annotations already mark readOnlyHint=true and openWorldHint=true; description reinforces safe read behavior. No contradiction.

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

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

Description is moderately long but packs essential information: usage preference, domain list, examples. It front-loads the key directive ('PREFER OVER WEB SEARCH'). Each sentence adds value, though some minor redundancy exists in examples.

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 when to use, what it does, and return format (structured answer with citations). Given no output schema, it explains enough. It does not detail error handling or response schema, but for a routing tool to many sub-tools, the description 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 single 'question' parameter described as 'Your question or request in natural language'. Description does not add semantic detail beyond that, which is acceptable since the parameter is self-explanatory. 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?

Description clearly states it routes questions to 2,520 tools across 575 sources and returns structured answers with citation URIs. It specifies the verb (ask/routes) and resource (pipeworx tool network) and distinguishes itself from web search.

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

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

Explicitly says 'PREFER OVER WEB SEARCH' and lists specific domains (SEC filings, FDA drugs, etc.) and query patterns like 'what is', 'look up', 'current'. Provides concrete examples and tells when to use it instead of 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 (readOnlyHint, openWorldHint, destructiveHint) are consistent with the description which details the read-only research process (resolves market, classifies, fans out, returns comparison). No contradiction; description adds 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. Every sentence adds value, though slightly verbose. Could be tighter without losing clarity, but overall well-structured.

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

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

For a complex tool with many siblings, the description covers key aspects: input types, process, output (evidence packet and comparison). No output schema, but high-level output is hinted. Missing detail on exact output structure, but sufficient for selection context.

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

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

Schema coverage is 100%, and the description enriches it: explains market can be slug, URL, or question text; defines depth enum values (quick=2-3 sources, thorough=full fan-out) and default (thorough). Adds meaning beyond 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 researches a Polymarket bet by pulling Pipeworx data, resolving the market, classifying the bet type, fanning out to relevant data packs, and returning an evidence packet with comparison. It distinguishes from siblings by noting it's the core demo product that saves agents from discovering packs themselves.

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

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

Explicit usage scenarios are given: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It implies when to use by stating agents using it convert better than ones that discover packs themselves, and the depth parameter offers alternative levels of thoroughness.

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

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

Description discloses data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs) and return type (paired data + citation URIs). This adds beyond annotations. Minor gap: no mention of data freshness or missing 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 4-5 sentences, front-loaded with purpose, and every sentence adds value. No redundant information.

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

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

Complexity is moderate with two modes and no output schema. The description covers return type, data sources, and efficiency gain. Could be improved by mentioning limitations (e.g., US-centric data).

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

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

Schema coverage is 100%, but description enriches the meaning significantly by explaining each 'type' mode and giving concrete examples for 'values' (tickers vs drug names).

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

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

The description clearly states the tool compares 2-5 companies or drugs side by side, with a specific verb and resource. It distinguishes from sibling tools like 'entity_profile' by noting it replaces many sequential calls.

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

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

Explicit usage scenarios are provided (e.g., 'compare X and Y', 'X vs Y', tables/rankings). However, it lacks explicit exclusions (e.g., when not to use) or named sibling 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 indicate read-only, non-destructive behavior. Description adds the 'best-effort' qualifier, which is valuable context beyond annotations. However, no details on behavior like caching, rate limits, or error conditions.

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 highly concise and front-loaded with purpose. However, it may be too brief at the expense of needed detail.

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

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

No output schema and 0% parameter description coverage means the description carries full burden. It fails to explain return format, pagination, or error handling. Incomplete for a tool with two untyped string parameters.

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 0% schema description coverage, the description should explain parameters. It only mentions 'version metadata' without clarifying what 'name' and 'version' refer to (e.g., package name, ecosystem). Little added value over raw schema.

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

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

Description clearly states the tool returns a dependency list using version metadata. However, it does not differentiate this from siblings like 'versions' or 'version' which might have overlapping functionality.

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 such as 'search' or 'versions'. No prerequisites or context 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?

Annotations show readOnlyHint=true, destructiveHint=false. Description adds that it returns top-N most relevant tools with names and descriptions, and advises using it for initial discovery. No contradictions; behavior is well-communicated.

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 clear and well-structured, with purpose and usage front-loaded. While somewhat lengthy, each sentence adds value; could be slightly more compact but remains 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, description explains return type (top-N tools with names+descriptions). Covers parameter semantics, usage guidelines, and purpose fully. Adequately explains what agent needs to know.

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 examples for 'query' (e.g., 'analyze housing market trends') and notes default/max for 'limit', exceeding the schema's bare definitions.

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 explicitly states 'Find tools by describing the data or task' with specific verb and resource. It distinguishes from sibling tools by advising 'Call this FIRST' and lists concrete domains like SEC filings, financials, FDA drugs, etc.

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?

Gives clear guidance: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer)'. Also provides examples of when to use, covering a wide range of tasks.

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

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

Annotations indicate read-only, open-world, non-destructive. Description adds details: returns SEC filings, fundamentals, patents, news, LEI with citation URIs.

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

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

Single dense paragraph with front-loaded purpose. Every sentence adds value; 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?

Despite no output schema, description fully explains return data and parameter constraints. Sufficient for agent to understand tool's function and 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 coverage is 100%, but description adds value: type only 'company', value can be ticker or CIK, and names not supported – referencing resolve_entity.

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 'Get everything about a company in one call' with specific examples of user queries. It distinguishes from siblings by noting it replaces multiple 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?

Explicitly tells when to use (e.g., 'tell me about X') and provides a constraint: names not supported, use resolve_entity first. Clearly states returned data categories.

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, so the description's mention of 'delete' is consistent but adds minimal new behavioral context. It does not disclose irreversibility or side effects beyond what annotations imply.

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, no redundancy. The first sentence states the core action, the second provides usage rationale. Every word adds value.

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

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

For a simple one-parameter tool with no output schema, the description covers purpose, usage context, and pairing recommendations. Annotations complete the behavioral picture. Nothing essential is missing.

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 100% coverage with a clear description for the 'key' parameter. The description merely restates 'by key' without adding formatting, constraints, or examples.

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

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

The description explicitly states 'Delete a previously stored memory by key,' clearly identifying the action (delete) and resource (memory by key). It distinguishes from siblings 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?

The description provides specific use cases: 'when context is stale, the task is done, or you want to clear sensitive data.' It does not explicitly state when not to use, but the context is clear and includes pairing advice with related tools.

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

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

Annotations already convey read-only, non-destructive, and open-world behavior. The description adds no additional behavioral context beyond the annotations, which is acceptable but not enriched.

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

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

The description is very brief but does not waste words. However, it sacrifices clarity and completeness for brevity, making it minimally adequate.

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

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

Given the low complexity (single parameter, no output schema), the description still fails to explain what 'module' refers to, what 'metadata' includes, or the output format, leaving significant gaps.

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

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

The input schema has one parameter ('name') with no description and 0% schema description coverage. The tool description does not explain the parameter's meaning or usage, leaving agents without needed 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 indicates the tool returns module metadata and the latest version. However, it lacks a verb (e.g., 'Get') and does not differentiate from sibling tools like 'version' or 'versions' which may handle version-specific 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?

No guidance is provided on when to use this tool versus alternatives like 'version' or 'versions'. The description does not include any context for selection.

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

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

Annotations are largely neutral (all false), so the description carries the burden of behavioral disclosure. It adds that the tool is free, rate-limited, and that feedback directly affects the roadmap. This is sufficient, though it doesn't explicitly state that it's non-destructive beyond the annotation.

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 concise for the amount of information it conveys. It is front-loaded with the main purpose and then covers usage guidelines. A minor improvement could be breaking into bullet points, but it's well-structured.

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

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

Given the tool's simplicity (3 parameters, no output schema), the description covers all necessary aspects: input semantics, usage guidelines, rate limits, and impact on roadmap. It leaves no major gaps for an agent to understand how and when to use it.

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 of parameter descriptions. The description adds extra context: it explains the type enum values, suggests message length, and clarifies that context is optional. This goes beyond the schema, earning a score above baseline.

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

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

The description clearly states the tool's purpose: sending feedback (bugs, features, data gaps, praise) to the Pipeworx team. It distinguishes itself from sibling tools by being the dedicated feedback mechanism, not a search or data retrieval tool.

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

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

The description provides explicit guidance on when to use the tool (bug, feature, data_gap, praise) and what to avoid (pasting end-user prompts). It also mentions rate limits (5 per identifier per day) and that it's free and doesn't count towards quota.

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 behavioral details: walking child markets, grouping markets, returning ranked opportunities with 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 moderately long but well-structured with clear section headers for modes. Every sentence adds essential information; 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 lacking an output schema, the description explains the return format (ranked opportunities with suggestions). It covers all aspects needed for tool selection and invocation, given the tool's complexity and sibling context.

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

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

Schema coverage is 100%, and the description adds value beyond the schema by explaining the dual-mode concept and providing examples for both event and topic parameters, enhancing understanding of their roles.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations. It explicitly names two modes (event and topic) and explains what each does, making its purpose distinct and specific.

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

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

The description provides explicit guidance on when to use each mode, including a concrete example where cross-event mode is necessary ('May≤June rule'). It implies the correct context for each mode and contrasts with single-event mode limitations.

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 internal details: uses lognormal model from FRED, live coinpaprika price, grouping by asset, fetching price history once, and ranking by edge. No contradictions.

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

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

The description is a single paragraph that is informative and front-loaded with the main action. Every sentence adds value, though could be slightly more concise.

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

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

Given no output schema, the description explains return details: top N ranked by edge magnitude with suggested trade direction. It covers model, data sources, and internal logic adequately for an agent to understand.

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

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

Schema coverage is 100% with clear descriptions. The description adds minor context (e.g., 'Top N edges to return after ranking') but does not significantly enhance beyond schema.

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

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

The description clearly states the tool scans Polymarket markets, identifies where Pipeworz data disagrees with market price, and returns top edges. It uses specific verbs and distinguishes from sibling 'polymarket_arbitrage' by focusing on opportunity discovery for 'what should I bet on today'.

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

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

The description explicitly says it's built for the 'what should I bet on today' question, implying when to use. It does not provide explicit when-not-to-use or alternative tools, but the context is clear.

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

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

Annotations already declare readOnlyHint=true. The description adds value by explaining the list-all behavior when key omitted, scoping to identifier, and pairing with other tools, 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?

Three sentences, each earning its place: action, use case, scope and pairing. No fluff, front-loaded with the primary action.

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

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

For a simple tool with one optional parameter and no output schema, the description covers input behavior, scope, and relationships to siblings completely.

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 one param with basic description; description adds critical semantics by explaining that omitting the key lists all stored keys, which is beyond the schema's coverage.

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 retrieves a saved value or lists all keys, using specific verbs and resource (previously saved data). It distinguishes from siblings 'remember' and 'forget' by naming them.

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

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

Provides context for when to use (retrieve stored context without re-deriving) and mentions scoping. Does not explicitly state when not to use, but the pairing with remember/forget implies 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 indicate readOnlyHint=true and destructiveHint=false, so the agent knows it's safe. The description adds valuable behavioral context: it fans out to multiple sources (SEC EDGAR, GDELT, USPTO) in parallel, and details the return format including structured changes and citation URIs.

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

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

The description is concise yet packed with essential information. It front-loads the purpose, provides usage examples, explains data sources, parameter syntax, and return structure in a logical order. Every sentence serves a clear purpose without unnecessary 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?

The description is largely complete for a read-only retrieval tool. It explains return values (structured changes, total_changes count, citation URIs) despite the lack of an output schema. Minor gaps: no mention of pagination, rate limits, or maximum date range, but these are not critical for basic usage.

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

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

Schema coverage is 100%, so the schema already describes all parameters. The description enhances the 'since' parameter by explaining the accepted formats (ISO date and relative shorthand) with examples, and provides guidance on typical usage ('Use '30d' or '1m' for typical monitoring'). This adds practical 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 starts with a clear, specific verb-resource pair: 'What's new with a company in the last N days/months?' It immediately tells the agent the tool's purpose and provides concrete example queries, making it easy to decide when to call it.

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

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

The description explicitly states when to use this tool ('Use when a user asks...') and gives several example phrasings. It does not explicitly mention when NOT to use it or compare it to siblings, but the examples cover the primary use case 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 indicate readOnlyHint=false and destructiveHint=false. The description adds that data is stored as key-value pairs scoped by identifier, with 24-hour retention for anonymous sessions and persistence for authenticated users. 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?

Four sentences, front-loaded with purpose and examples. Every sentence adds value. No unnecessary words.

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

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

For a simple key-value store with two required parameters and no output schema, the description covers purpose, usage, pairing, scope, and retention completely.

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

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

Schema covers 100% of parameters with descriptions. The description adds example key formats and value types, but does not significantly deepen 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 saves data for reuse, provides concrete examples of use cases (resolved ticker, target address, user preference), and distinguishes it from sibling tools 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 says when to use: 'discover something worth carrying forward' and pairs with recall and forget. Also explains scoping and retention differences for authenticated vs anonymous sessions.

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

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

Beyond annotations (readOnly, destructive), description adds that it returns IDs plus pipeworx:// citation URIs, gives examples, and notes it replaces 2-3 lookup calls. Adequately discloses behavioral traits for a read-only lookup.

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 a single dense paragraph that front-loads purpose. Every sentence contributes value (usage context, examples, output hints). Could be more structured but is efficient and not verbose.

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 full schema coverage, helpful annotations, and no output schema needed, the description provides complete context: purpose, when to use, examples, output types (IDs, citation URIs), and behavioral notes. No gaps for an agent to select/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 has 100% coverage with descriptions for both parameters. Description enriches by explaining value formats (ticker, CIK, name for company; brand/generic for drug) with concrete examples, adding meaning beyond schema.

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

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

Clearly states the tool looks up canonical identifiers for companies or drugs, specifies the verb and resource, and distinguishes it from sibling tools by noting it replaces multiple lookup calls and should be used before other identifier-dependent 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?

Explicitly states when to use (when a user mentions a name needing official IDs like CIK, ticker, RxCUI, LEI) and provides ordering guidance ('Use this BEFORE calling other tools'). Lacks explicit when-not-to-use but context is clear.

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

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

While annotations declare readOnlyHint=true and destructiveHint=false, the description adds no behavioral context beyond that. It does not describe return format, pagination, or any side effects. For a search tool, missing behavioral details is a significant gap.

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 just three words, the description is extremely concise but fails to impart sufficient information. It does not earn its place as it lacks substance and structure, making it under-specified rather than 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 2 unannotated parameters and no output schema, the description should compensate with context. It does not explain return values, search scope, or behavior, leaving the tool severely under-documented.

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 0% description coverage, and the description does not mention any parameters. The agent receives no guidance on semantics for 'query' or 'limit' beyond their types.

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

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

The description states 'Name + description search,' which indicates the tool searches over names and descriptions. However, it fails to specify what is being searched (tools? records?) and does not distinguish it from sibling tools like 'discover_tools' or 'entity_profile'. The purpose is vague and lacks specificity.

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. There are no notes on prerequisites, context, or exclusions, leaving the agent without direction on appropriate usage.

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

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

The disclosures beyond annotations are substantial: the verdict types (confirmed, approximately_correct, refuted, etc.), the structured form extraction, the actual value with citation (pipeworx://), and the percent delta. It also notes the current version scope (v1, company-financial). These add significant value on top of the readOnlyHint/openWorldHint annotations, 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?

The description is a single paragraph but efficiently packs all necessary information: purpose, usage guidance, scope, return values, and value proposition. Every sentence is meaningful, with no redundancy or filler.

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

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

Given that the tool has a simple input (one required string), no output schema, and the annotations are present, the description covers purpose, when to use, actions, returns, and scope limitations. It is nearly complete; only a brief note on error handling or out-of-scope handling is missing.

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%, and the single parameter 'claim' is already well-described in the schema as 'Natural-language factual claim' with examples. The tool description does not add additional semantic nuance beyond restating the parameter's purpose, 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 verb phrase 'Fact-check, verify, validate, or confirm/refute' and identifies the resource as 'natural-language factual claim'. It distinguishes itself from sibling tools by describing its specific function and value proposition (replacing 4-6 sequential calls).

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

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

The description provides clear usage context with example phrasings ('Is it true that…?', 'Verify the claim that…') and specifies the scope (company-financial claims for public US companies). However, it does not explicitly mention when not to use the tool or name alternative tools, though the scope inherently limits its applicability.

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 provide read-only and non-destructive hints, but the description adds no behavioral context beyond the name. It does not disclose that this tool is purely for lookups, which is already known from 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?

While extremely concise at 3 words, the brevity sacrifices utility. The description is a fragment and lacks structure, making it unhelpful.

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 and the minimal input schema, the description is highly incomplete. It does not cover what the tool returns or how to use it effectively.

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

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

The description does not explain the parameters 'name' and 'version'. With 0% schema description coverage, the description should compensate but fails entirely.

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 version metadata.' is vague and lacks a verb indicating the action (e.g., retrieve, list). It is only slightly better than a tautology because it specifies 'metadata', but it does not clearly state what the tool does.

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

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

No guidance on when to use this tool versus alternatives like 'versions' (plural). The description provides no context for selection.

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

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

Annotations already indicate read-only and non-destructive behavior. Description adds no additional behavioral context (e.g., result order, limits). Meets baseline but adds no value.

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

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

Description is extremely concise (two words) but sacrifices clarity. It is not informative enough to be 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?

No output schema, no parameter explanations, and no usage context. Agent lacks essential information to correctly invoke this tool.

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

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

Schema description coverage is 0% and description does not explain what 'name' refers to. Agent cannot determine the correct input format or purpose.

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 'List versions' indicates an action (list) but lacks specification of which entity's versions. The required 'name' parameter implies it lists versions of something, but the description is vague.

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 'version' (singular) or other tools. Agent has no context for selecting this tool.

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