Estat Japan

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

No annotations provided, so description carries full burden. Describes routing and result retrieval but does not explicitly state that it is read-only or disclose any side effects. However, behavior is implied as non-destructive querying.

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 informative but slightly verbose. Front-loaded with purpose and usage, includes multiple examples and source list. Could be trimmed slightly without losing meaning.

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 parameter and no output schema or annotations, the description is complete: it explains the tool's functionality, when to use it, and provides examples. No critical gaps.

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

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

Schema has 100% coverage with description 'Your question or request in natural language'. The tool description adds value by elaborating on the parameter's purpose, providing examples, and explaining how the question is processed.

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 verb 'answer' and the resource 'natural-language question'. Describes automatic routing across many sources, distinguishing it from sibling tools that are specific to certain data packs or operations.

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

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

Explicitly states 'Use when a user asks...' with concrete examples of question types, and advises against manually figuring out which tool to call, providing clear context for when to use this tool versus 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, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral details: it resolves markets, classifies bets (crypto price / Fed rate / etc.), fans out to appropriate data packs, and returns an evidence packet with comparison. This extends beyond the annotations without contradicting them.

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 primary purpose and flows logically. It could be slightly more concise, as some phrases (e.g., the enumeration of bet types) are somewhat verbose. However, it remains well-structured and efficient for a complex tool.

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

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

Given the tool's complexity and the absence of an output schema, the description fairly covers inputs, process, and output (evidence packet + comparison). It misses details on what the evidence packet contains, but the overall intent and usage are adequately conveyed. The openWorldHint annotation compensates for some variability.

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

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

Schema coverage is 100% with both parameters described. The description rephrases the market parameter but does not add new details about the depth parameter. It provides high-level context about the fan-out process but does not augment the schema's parameter descriptions meaningfully beyond what is already present.

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 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call,' specifying a concrete verb and resource. It distinguishes itself from sibling tools like ask_pipeworx and get_data by focusing exclusively on Polymarket bets and providing a market-vs-model comparison, making its purpose unmistakable.

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

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

The description explicitly lists usage scenarios: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It provides clear context but does not explicitly state when not to use it or mention alternatives among siblings, though the specificity implies exclusivity for Polymarket research.

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?

With no annotations, the description carries the full burden. It explains what data is pulled for each type (revenue, net income for companies; adverse events, trials for drugs), sources (SEC EDGAR/XBRL, FAERS), and output (paired data + citation URIs). It is transparent about the behavior but does not mention potential limitations or safety aspects.

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

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

The description is concise (4 sentences) and front-loaded with the main purpose. Every sentence adds value: trigger phrases, data details, sources, and efficiency benefit. 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?

Given the tool's complexity (multi-type, multi-source), the description covers what each type returns, sources, and output format. It lacks an output schema but mentions 'paired data + citation URIs,' which is helpful. Slight gap in specifying the exact return structure.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant value beyond the schema by explaining how 'type' determines the data fields retrieved and providing concrete examples for 'values' (e.g., ["AAPL","MSFT"]). It enriches parameter understanding.

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

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

The description clearly states the tool's purpose: 'Compare 2–5 companies (or drugs) side by side in one call.' It specifies the verb 'compare' and the resource types (companies/drugs). It implicitly distinguishes from siblings like entity_profile by focusing on multi-entity comparison.

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

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

The description provides explicit trigger phrases ('compare X and Y', 'X vs Y', etc.) and use cases (tables/rankings). It notes the tool replaces 8-15 sequential calls, indicating when it is more efficient. However, it does not explicitly state when not to use it (e.g., for a single entity).

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?

No annotations are provided, so the description must handle behavioral disclosure. It states it returns 'top-N most relevant tools with names + descriptions'. However, it does not mention any side effects, read-only nature, rate limits, or authentication requirements. For a simple search tool this is a minor 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?

The description is a single sentence followed by a list of examples and usage advice. It is front-loaded with purpose and avoids redundancy. No wasted words, but slightly longer due to 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?

Given the tool's simplicity (no output schema, few parameters), the description is sufficiently complete. It explains purpose, usage domain, return format, and when to call it. No output schema is needed for a tool that returns a list of tool names and descriptions.

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

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

Schema coverage is 100%, so baseline 3. The description adds examples for the query parameter and mentions default limit and max, but these are already in the schema description. Little additional value beyond schema.

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

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

The description clearly states that the tool finds tools by describing data or task, explicitly listing many domains. It distinguishes from siblings by positioning itself as a first-step discovery tool ('Call this FIRST...'), which differentiates it from domain-specific tools in the sibling list.

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 ('when you need to browse, search, look up, or discover what tools exist') and advises calling it first. It does not explicitly mention when not to use, but the advice is clear enough.

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?

No annotations provided, so description carries full burden. It discloses what data is returned (SEC filings, fundamentals, patents, news, LEI) and that URIs are provided. Does not mention rate limits or auth, but is comprehensive enough for a read tool.

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

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

Single paragraph with front-loaded purpose, then usage, then return details, then input note. Efficient but could be slightly more structured with bullet points. 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?

Given tool complexity (aggregating multiple data sources), description covers key return categories and input constraints. No output schema, but listing what is returned suffices. Lacks details on result size or pagination, but overall complete for intended use.

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

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

Schema coverage is 100%, baseline 3. Description reinforces param meaning with examples and notes about name resolution, but adds minimal new info beyond schema. No significant enrichment.

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 'Get everything about a company in one call' with specific verb and resource. It lists multiple example queries and distinguishes from other tools by noting it replaces calling 10+ pack 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 ('when a user asks tell me about X...') and when not to use ('Names not supported — use resolve_entity first'). Provides clear context and 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?

No annotations provided, but description clearly states destructive nature ('delete'). Does not mention error handling or permissions, but sufficient for a simple deletion.

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 wasted words. Essential information 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?

For a simple tool with one required parameter and no output schema, description fully covers purpose, usage, and 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?

Single parameter 'key' with description in schema. Description adds no extra meaning beyond schema. Schema coverage 100% so baseline 3.

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

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

Description uses specific verb 'Delete' and resource 'memory by key'. Clearly distinguishes from siblings 'remember' and 'recall' by mentioning pairing.

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: stale context, task done, or clearing sensitive data. Mentions pairing with related tools for alternative behavior.

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?

With no annotations provided, the description must fully convey behavioral traits. It indicates a read operation ('Fetch') but lacks details on authorization, rate limits, pagination behavior, or what happens when no filters are applied. The description is adequate but minimally transparent.

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 sentence, making it very concise and front-loaded. It contains no fluff, but could be slightly expanded to include usage hints without losing brevity.

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

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

Given the tool has 5 parameters and no output schema, the description is too minimal. It does not explain the return format, pagination details, or how filters interact. The tool is moderately complex, and the description lacks sufficient context for confident use.

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

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

All parameters have descriptions in the input schema (100% coverage), so the tool description's mention of 'Optionally filter by dimension codes' adds marginal value by summarizing the optional nature of the 'filters' parameter. However, it does not provide additional semantics beyond what the schema already offers.

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 'Fetch' and the resource 'observations from a stats table', and mentions optional filtering by dimension codes. This distinguishes it from sibling tools like search_stats or get_metadata, which serve different purposes.

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 (e.g., search_stats). It does not specify prerequisites, when not to use it, or any context for prioritization among sibling tools.

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

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

With no annotations, the description carries the full burden. It indicates a read operation ('fetch') without warnings about destructive actions or permissions. However, it lacks details like whether it requires authentication or the nature of the fetch (e.g., network call, database).

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, concise sentence that directly states the tool's purpose. No extraneous words or redundancy.

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

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

The description is adequate for a simple metadata fetch, but it does not explain what is returned (e.g., format of dimensions or code lists) and there is no output schema to compensate. Could be more complete for an agent unfamiliar with the domain.

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

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

The input schema covers 100% of parameters with descriptions (e.g., 'J | E' for lang, 'Table ID (statsDataId)' for stats_data_id). The description adds no extra meaning beyond what the schema already provides.

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

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

The description clearly states the verb 'fetch' and the specific resource 'dimensions and code lists' for a 'stats table', which distinguishes it from sibling tools like get_data (retrieves data values) and list_data_catalog (lists tables).

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

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

No guidance is provided on when to use this tool versus alternatives, nor any conditions or exclusions. The usage is only implied by the tool's name and description.

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 empty, so the description carries full burden for behavioral context, but it only says 'browse', implying a read operation. It does not describe pagination behavior, response format, or any side effects. The presence of parameters like limit and start_position suggests pagination, but the description omits this.

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

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

The description is extremely concise (6 words), front-loading the verb and resource. However, it is so short that it omits potentially helpful information about parameter usage or return format, making it slightly underspecified for a tool with 4 optional parameters.

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

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

No output schema exists, so the description should indicate what the tool returns (e.g., list of grouping names or IDs). It only says 'browse' without specifying the output structure. For a simple list tool, this is a notable gap.

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 provides 100% coverage with descriptions for all 4 parameters (lang, limit, query, start_position). The tool description adds no additional meaning beyond the schema, so per guidelines, baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'browse' and the resource 'high-level data catalog (table groupings)', distinguishing it from sibling tools like get_data or get_metadata which deal with specific data or metadata. However, it could be more explicit about what it does not return.

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. With 14 sibling tools including get_data, get_metadata, and search_stats, the agent receives no hints about the appropriate context for browsing the catalog vs. querying specific data.

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?

No annotations are provided, so the description carries full behavioral disclosure. It discloses rate limiting (5 per identifier per day), that it is free and doesn't count against tool-call quota, and that the team reads digests daily, affecting roadmap. No contradictions exist.

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 concise and well-structured. It front-loads the purpose, then provides usage guidelines, and ends with constraints. 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?

The description is fully complete for the tool's role. It explains when to use, what to include, limitations (rate limit, quota), and outcome (feedback informs roadmap). With a nested object and no output schema, the description covers all needed 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% with all parameters described. The description adds significant value: it explains the enum values in detail, clarifies the optional context parameter's purpose, and provides guidance on writing the message (be specific, 1-2 sentences, 2000 chars max). This goes beyond the schema's 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 is for providing feedback to the Pipeworx team about bugs, features, data gaps, or praise. It enumerates specific types and their meanings, and distinguishes the tool from sibling tools (which are mainly data retrieval or entity tools) by its feedback-only purpose.

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

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

The description gives explicit when-to-use guidance by mapping each enum value to a specific scenario (bug, feature, data_gap, praise). It also advises against pasting end-user prompts and notes rate limits and quota, providing clear context for appropriate use.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds details like walking child markets, extracting dates/thresholds, sorting, and reporting violations, which are consistent 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 yet complete, with a clear front-loaded purpose and specific example. 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?

For a single-parameter tool with no output schema, the description fully covers what the tool does, how it works, and what it returns. The logic and output format are explicitly stated.

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 slight context beyond the schema (e.g., example slug). Baseline 3 is appropriate as the schema already defines the parameter well.

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 by checking monotonicity violations. It specifies the resource (Polymarket event) and distinguishes from siblings like polymarket_edges, which likely cover other analyses.

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?

It explains when to use (when there are multiple markets with dates/thresholds) and what input to provide (event slug/URL). However, it does not explicitly mention when not to use or alternative tools.

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

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

Annotations indicate read-only, open-world, non-destructive behavior. The description adds substantial behavioral detail: V1 covers crypto-price bets using a lognormal model from FRED and live coinpaprika price, scans top markets, groups by asset, fetches price history once, computes model probability, ranks by |edge|, and returns suggestions. No contradiction with annotations.

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

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

The description is a single paragraph with clear front-loading of the main purpose. It includes necessary details about the model and process, but is somewhat lengthy at multiple sentences. Could be slightly more concise, but still effective.

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

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

The tool is complex with no output schema, but the description explains the model process, ranking logic, and output (top N with trade direction) adequately. It omits error handling or edge cases, but provides enough for an agent to understand the tool's behavior.

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

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

The input schema has 100% description coverage for all three parameters (limit, window, min_edge_pp). The description does not add new meaning to the parameters beyond what the schema already provides (e.g., defaults and constraints are already in schema). Baseline of 3 is appropriate.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It specifies the verb 'scan' and resource 'Polymarket markets', and distinguishes by noting it's built for discovering opportunities without manual paging, though it doesn't explicitly differentiate from sibling polymarket_arbitrage.

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

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

The description explicitly targets the 'what should I bet on today' question and mentions discovering opportunities without paging. It provides clear context for when to use the tool, but lacks explicit guidance on when not to use it or alternatives (e.g., polymarket_arbitrage).

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?

No annotations provided, so description carries full burden. It discloses two modes (key present vs omitted), scoping to user identifier, and relation to remember/forget. Lacks details on error handling or return format, but sufficient for a simple retrieval tool.

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

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

Description is three sentences with no wasted words. The main action is front-loaded, and each sentence adds value (purpose, use case, scoping, pairing).

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

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

No output schema, but tool is low-complexity with one optional parameter. Description covers scoping and pairing. Could mention return type, but not essential for effective use.

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

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

Input schema coverage is 100% and the schema's parameter description already explains the 'omit to list keys' behavior. The tool description reiterates this without adding new semantic information, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves a saved value or lists all keys, with verb+resource specificity. It distinguishes from sibling tools (remember, forget) by describing the operation and pairing.

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 (to recall stored context like ticker, address) and mentions pairing with remember/forget. Does not explicitly list when not to use, but context is clear enough for an agent to decide.

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?

No annotations are provided, so the description carries the full transparency burden. It discloses the parallel fan-out to three sources (SEC, GDELT, USPTO) and the output structure (changes, count, URIs). However, it does not address error conditions, rate limits, or side effects. The behavior is well-covered but not exhaustive.

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

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

The description is a single paragraph that efficiently front-loads the purpose, provides examples, and then gives technical details. Every sentence is informative and earns its place with no redundancy.

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

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

Given no output schema and no annotations, the description covers the tool's core functionality well: it explains the data sources, return format, and parameter formats. However, it omits details on error handling or behavior when no changes are found, leaving minor gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the 'since' parameter formats (ISO date vs relative shorthand like '7d'), the 'value' parameter (ticker or CIK), and the limited 'type' option. This enriches the schema beyond the raw property 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: retrieving recent changes for a company. It provides specific verb-resource pairs ('What's new with a company') and example user queries, distinguishing it from sibling tools like entity_profile or compare_entities.

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

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

The description gives explicit use cases and example queries ('when a user asks...'), but does not mention when not to use this tool or suggest alternatives. The guidance is strong but lacks exclusions.

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

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

With no annotations, the description fully bears the transparency burden. It discloses scope (across sessions), scoping by identifier, persistence behavior (authenticated users persistent, anonymous 24 hours), and pairs with forget. Could mention overwrite behavior but is otherwise transparent.

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, and each sentence adds value. No wasted words; covers purpose, usage, pairing, and persistence efficiently.

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

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

For a simple two-parameter tool with no output schema, the description is complete. It explains what, when, how, scope, persistence, and pairing with siblings. No apparent gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds examples for key ('subject_property', 'target_ticker') and explains value as 'any text.' This adds meaning beyond the schema's 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 verb 'Save' and the resource 'data the agent will need to reuse later.' It distinguishes itself from siblings by mentioning pairing with 'recall' and 'forget,' and provides specific examples of data to save (resolved ticker, target address, user preference).

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 'Use when you discover something worth carrying forward' and gives concrete examples. It also mentions pairing with recall and forget. Lacks explicit 'when not to use' but provides sufficient guidance for common 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?

No annotations provided, so description carries full burden. It mentions returns IDs and pipeworx:// citation URIs and implies a read operation. However, it does not disclose error handling, permissions, or what happens if the entity is not found, limiting transparency to a 3.

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

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

Description is well-structured, front-loads the main purpose, and uses bullet-like examples. It is concise but not overly terse, earning a 4.

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 format (IDs plus URIs). Covers primary use cases and includes replacement claim. Lacks edge-case details, but for a lookup tool this is reasonably complete, scoring a 4.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters. Description adds examples and context but does not provide additional parameter semantics beyond what the schema offers, 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?

Description specifically states 'look up canonical/official identifier' for company or drug, lists exact ID systems and examples, and contrasts with sibling tools by highlighting that it provides identifiers needed by other tools, making purpose unambiguous.

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

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

Clearly says to use when needing official identifiers and to call before other tools, providing explicit context. Does not mention when not to use, but the positive guidance is strong enough for a 4.

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?

No annotations exist, so the description must disclose behaviors. It states that it returns IDs and names, which is useful. However, it does not mention pagination behavior, default parameter values beyond schema, or any side effects. This is adequate but not thorough.

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

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

The description is extremely concise, consisting of two sentences. Every sentence serves a purpose: the first explains the core function, the second provides next-step guidance. No wasted words.

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

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

For a search tool with 4 parameters and no output schema, the description covers the main output (IDs and names) and how to use results. It lacks mention of total counts or empty result handling, but given the schema richness, it is nearly 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?

The input schema has 100% coverage with descriptions, so the baseline is 3. The description adds no additional parameter meaning beyond what the schema provides, maintaining parity.

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 searches e-Stat statistical tables and returns IDs and names, using specific verb 'Search' and resource 'e-Stat statistical tables'. It distinguishes itself from sibling tools like 'get_data' and 'get_metadata' by specifying that the output IDs are used with those 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 guidance on what to do with the results ('Use the IDs with get_data / get_metadata'), which implies the tool is for discovery. However, it does not explicitly state when to use this tool over alternatives or mention exclusions, leaving some clarity to the agent.

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?

Given no annotations, the description fully carries the burden. It discloses supported claim types, data sources (SEC EDGAR + XBRL), and return structure (verdict types, actual value, citation). Missing limitations like only US public companies, but still comprehensive.

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

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

The description is front-loaded with purpose, then usage, scope, returns, and efficiency. It is clear and well-structured, though slightly verbose with multiple examples. Still efficient for the information conveyed.

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 1-parameter tool with no output schema, the description covers purpose, usage, scope, return types, and underlying data sources. Minor gap: does not explain verdict terms like 'approximately_correct'. Overall, it is fairly complete.

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

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

Schema description coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the schema's parameter description for 'claim'. The examples in the description are helpful but already implied by 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 uses specific verbs (fact-check, verify, validate, confirm/refute) and a clear resource (natural-language factual claim). It distinguishes itself from sibling tools like ask_pipeworx or compare_entities by focusing on claim validation.

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 usage examples and context (e.g., 'Is it true that…?') and mentions scope (company-financial claims). However, it lacks explicit do-not-use scenarios, which is a minor gap.

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