Genius

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

No annotations provided, so description carries full burden. It discloses routing across many sources, picking tools, filling arguments, and returning results. However, it does not discuss limitations, error behavior, or latency. Adequate but could be more 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?

Description is moderately sized but well-structured: begins with purpose, usage context, capabilities, then examples. Every sentence adds value, though slightly verbose with list of sources.

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 (300+ sources, routing), the description covers main purpose, usage, and examples. No output schema exists, but description implies a result. Lacks details on error handling or scope limitations, but sufficient for general use.

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

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

Schema coverage is 100% with one parameter described as 'Your question or request in natural language'. Description adds rich context with examples and explains how the question is processed, exceeding schema description.

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

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

The description clearly states the tool answers natural-language questions by automatically picking the right data source, with specific examples like 'What is the US trade deficit with China?' and lists 300+ sources. It distinguishes from siblings (e.g., compare_entities, get_artist) which are more 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?

Explicitly says 'Use when a user asks...' and provides example queries. It implies this is a fallback when you don't want to figure out which specific tool to call. No explicit when-not-to-use or alternatives, but 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?

Description details the tool's internal steps: resolve market, classify bet, fan out to packs, and return evidence packet with comparison. Annotations already indicate safe read (readOnlyHint, openWorldHint), and description adds rich behavioral context without contradiction.

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

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

Description is mostly front-loaded with the core purpose in the first sentence. It contains 3 sentences, each adding value, though slightly wordy. Could be trimmed slightly without loss.

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 (2 parameters, no output schema), the description fully explains what it does, when to use it, and what it returns. No gaps in essential information for an agent to invoke correctly.

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

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

Schema has 100% coverage with descriptions for 'market' (slug/URL/text) and 'depth' (quick/thorough). Description reinforces the 'market' parameter's flexibility and the 'depth' parameter's effect, adding slight 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?

Description clearly states verb 'Research' and resource 'Polymarket bet', and specifies 'by pulling the relevant Pipeworx data'. It distinguishes from siblings like 'ask_pipeworx' by focusing on bet-specific research.

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 lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. Also notes it is the core demo product, guiding agents to prefer it over manual discovery.

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

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

Discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) and return format (paired data + citation URIs). No annotations provided, so description carries full burden; it adequately covers behavioral traits without 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?

Two sentences packed with essential info, front-loading purpose and use cases. Each phrase earns its place, though the second sentence is dense; could be slightly more structured 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?

Covers key aspects: purpose, when to use, data pulled per type, and return format. No output schema exists, so the description provides sufficient context for an agent to judge the tool's role, though output structure could be more explicit.

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 beyond schema by providing examples (e.g., tickers like AAPL, drug names like ozempic) and clarifying constraints (2-5 items). This enhances meaning for an AI agent.

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

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

The description clearly states the tool compares 2-5 companies or drugs side by side, with specific use case phrases and data sources for each type. It effectively distinguishes from siblings like entity_profile (single entity) and ask_pipeworx (general QA).

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 lists when to use (e.g., 'compare X and Y', 'stack up', 'which is bigger') and mentions it replaces 8-15 sequential calls. Lacks explicit when-not-to-use or alternatives, but sibling tools imply 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?

No annotations are provided, so the description carries the full burden. It states the output 'Returns the top-N most relevant tools with names + descriptions,' but does not disclose potential side effects, safety, or authorization needs. Basic transparency but adequate for a discovery tool.

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

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

The description is concise, front-loaded with the purpose, and includes a useful list of domains. It is efficient with no wasted words, though slightly verbose due to the domain list, which is still helpful.

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

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

For a simple search/discovery tool, the description is complete. It explains the output and provides context for when to use it among many siblings. No output schema exists but the description sufficiently covers expected returns.

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

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

Schema description coverage is 100%, so the schema already documents both parameters well. The description adds minimal extra meaning beyond schema, such as mentioning the output format. 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's purpose: 'Find tools by describing the data or task.' It lists specific domains and distinguishes itself from sibling tools by being a meta-tool for discovery, not a domain-specific 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?

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set.' Provides clear context for when to use, but does not name specific alternative tools or exclusion cases.

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 full burden. It describes the return types (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. It also notes the only supported entity type is 'company'. While it doesn't explicitly state it's read-only, 'Get' implies no mutation.

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 all sentences add value. The first sentence clearly states the purpose. The list of data sources is useful. It could be slightly more concise, but the detail is justified 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 no output schema, the description adequately explains return values (SEC filings, revenue, etc.) and mentions citation URIs. It does not mention limits or pagination, but for a profile tool this is acceptable. It provides sufficient context for an agent to understand the output.

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

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

The schema already covers both parameters with 100% coverage (type enum and value description). The description adds value by specifying that tickers or zero-padded CIKs are accepted and that names are not supported, which goes 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 specific verb and resource: 'Get everything about a company in one call.' It clearly lists the data sources (SEC filings, fundamentals, patents, news, LEI) and provides example user queries, distinguishing it from siblings like resolve_entity.

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

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

The description explicitly tells when to use this tool via example queries ('tell me about X', 'research Microsoft') and when not to use it (if only a name, use resolve_entity first). This provides clear context for the 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 bears full responsibility. It states 'delete' implying a destructive action but does not elaborate on permanence, reversibility, or side effects. For a simple memory deletion, this is adequate but could be more 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 very concise with two sentences, front-loading the core action. It could be slightly more structured but wastes no 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 tool is simple (1 required param, no output schema). The description covers purpose and usage but omits return value or confirmation of deletion. For a complete specification, this minor gap prevents a higher score.

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

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

Schema coverage is 100% for the single required parameter 'key'. The description adds no additional meaning beyond what the schema provides (e.g., format, constraints, examples). 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 'Delete a previously stored memory by key,' which is a specific verb+resource. It is also well-differentiated from siblings like 'remember' and 'recall' that are explicitly mentioned elsewhere.

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 this tool: 'when context is stale, the task is done, or you want to clear sensitive data.' It also suggests pairing with remember and recall, indicating 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 exist, so the description must carry the behavioral burden. However, it only states the basic function and does not disclose error handling, rate limits, or any other behavioral traits.

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

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

The description is a single, concise sentence that efficiently conveys the tool's 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?

For a simple retrieval tool, the description is minimally adequate but lacks details about output format or behavior on invalid IDs, which would be helpful given no output schema.

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 its two parameters, so the description adds no extra meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the action ('Single annotation by ID') and the resource ('Genius community note'), which distinguishes it from sibling tools like get_artist and get_song.

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 is provided. The description lacks any context for usage decisions.

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 empty annotations, the description carries the full burden but only gives a minimal hint. It does not disclose that the tool is read-only, what exactly is returned, or whether authentication is needed. Behavioral traits are largely opaque.

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

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

At only 4 words, the description is extremely concise. While it could be more informative without being verbose, it avoids unnecessary fluff and is front-loaded with the key idea.

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 should explain what the tool returns. It does not mention the return format, completeness of data, or any pagination. For a simple tool, this is a clear 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?

The single parameter 'artist_id' is already well-described in the schema as 'Genius artist ID'. The description adds no extra meaning or context for the parameter, so it meets the baseline for high schema 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 'Artist bio + identifiers' suggests the tool retrieves an artist's biography and some identifiers, but it is vague and does not specify what identifiers are included. It does not distinguish from sibling tools like 'get_song' or 'entity_profile'.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, when not to use, or examples of appropriate use cases.

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 empty annotations, the description carries the burden. It discloses that lyric text is not returned (a behavioral trait) and lists return fields. However, it could mention whether the action is safe/destructive, which is missing.

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

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

Two sentences with no fluff. First sentence enumerates output fields; second sentence clarifies a key limitation. Every sentence earns its place.

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

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

The tool is simple (1 parameter, no output schema). The description adequately explains what is returned and what is not. Could mention expected return format or error handling, but overall sufficient.

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 description does not add new information about the 'song_id' parameter beyond what the schema provides. The description focuses on output, not input parameters.

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 song metadata listing specific fields (title, artists, album, etc.), and it distinguishes from sibling tools like get_artist and search_songs.

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 implies usage for fetching song details but does not explicitly state when to use it over alternatives or when to avoid it. No guidance on prerequisites or 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?

No annotations are provided, so the description carries full burden. It only mentions listing and sorting, but omits details on pagination, default behavior, or any limitations, which is insufficient for a list tool.

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

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

The description is very short and front-loaded, but it is too brief to be informative. It sacrifices completeness for conciseness.

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

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

Given 4 parameters (page, sort, per_page, artist_id), no output schema, the description does not cover pagination, default sort, or response format, making it incomplete for effective agent use.

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

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

Schema coverage is 100%, so baseline is 3. However, the description adds little value and is slightly inaccurate (e.g., 'release_date' vs schema's 'release_date_with_null_last'). It does not explain pagination or required artist_id 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 'Songs by an artist' clearly specifies the resource (songs) and action (list by artist). It distinguishes from sibling tools like search_songs (general search) and get_song (single song).

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 implies usage for listing songs with sort options, but does not provide when/ when-not guidance or mention alternatives like search_songs for broader queries.

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 carries full burden. It discloses rate-limiting (5 per identifier per day), that it's free and doesn't count against tool-call quota, and that the team reads digests daily affecting roadmap. This provides clear behavioral expectations. No destructive behavior is implied. Could mention authentication or feedback confirmation, but overall good.

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 and uses clear sentences. It is somewhat long but each sentence contributes value (purpose, when to use, how to write, rate limits). It could be slightly more concise, but structure is logical and easy to parse.

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 3 parameters (one nested), the description covers usage scenarios, feedback formatting, constraints, and impact. It lacks information about what happens after submission (e.g., confirmation or response). For a feedback tool, this is a minor gap, but overall it provides sufficient context for an agent to use 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 description coverage is 100%, so the baseline is 3. The description adds context to the 'type' enum by explaining when each value is appropriate, which complements the schema. However, the schema already provides descriptions for all parameters. The description offers guidance on message content but doesn't add significant novel parameter semantics beyond the schema.

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

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

The description clearly states the tool's purpose: to tell the Pipeworx team about something broken, missing, or needed. It explicitly lists the types of feedback (bug, feature, data_gap, praise) and distinguishes the tool from others by focusing on feedback rather than queries or actions. The verb 'tell' and the resource 'Pipeworx team' are specific, and the context of use is well-defined.

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

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

The description provides explicit when-to-use scenarios: for bugs, missing features/data gaps, or praise. It guides the user on what to include (describe in terms of tools/packs, avoid pasting prompts). It mentions rate limits and usage quotas. However, it does not explicitly contrast with sibling tools like 'ask_pipeworx' for asking questions, though the context implies this is for feedback only. A direct when-not-to-use statement would improve clarity.

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

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

Annotations indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds behavior: checks monotonicity, two modes, returns ranked opportunities with suggested trade direction and reasoning. No contradictions. The description fully complements annotations with additional behavioral detail.

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

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

Description is concise but comprehensive. It front-loads the purpose, then clearly separates two modes with explanations. Every sentence adds value; no fluff. Structure aids quick comprehension.

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

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

Given the complexity (two modes, cross-event search, monotonicity checking), the description is complete. It explains what each mode does, what returns are expected (ranked opportunities with direction and reasoning), and provides practical examples. No output schema, but the return description is sufficient for an agent.

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

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

Schema description coverage is 100%, but description adds significant meaning: explains each parameter's purpose (event slug vs. topic), provides examples, and clarifies how each mode uses the parameter. This goes beyond the schema's simple type/description.

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

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

Description clearly states it finds arbitrage opportunities via monotonicity violations. It distinguishes two modes (event and topic) and explains the resource (Polymarket markets). The verb 'Find' and resource 'arbitrage opportunities' are specific and differentiate from siblings.

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

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

Explicitly describes when to use each mode: single-event mode for a single event's child markets, cross-event mode for related markets across separate events. Provides examples and explains why cross-event is needed for cases like cutoff dates split into separate events. No exclusions needed, 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?

The annotations (readOnlyHint=true, etc.) already provide safety context. The description adds substantial behavioral details: data sources (FRED + coinpaprika), grouping by asset, caching price history once, model probability computation, ranking by |edge|, and return of top N with trade direction. This goes beyond annotations, though it could mention data freshness or rate limits.

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

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

The description is well-structured and front-loaded with the main action. It covers all necessary aspects without excessive verbosity, though it could be slightly more concise by removing some technical details that might be redundant.

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

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

Given the complexity (model, grouping, ranking), the description explains the overall process and return value (top N with edge and direction) adequately. There is no output schema, but the description covers what is returned. Minor gaps: output format details (e.g., field names) are not specified, but sufficient for agent 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?

The input schema has 100% description coverage with defaults and constraints clearly stated (limit max 25, window enum, min_edge_pp default 0.5). The description does not add any significant new meaning beyond what the schema already provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It specifies the scope (crypto-price bets using a lognormal model) and the outcome (ranked by edge magnitude with suggested trade direction), distinguishing it from sibling tools like polymarket_arbitrage or bet_research.

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

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

The description explicitly frames the tool for the 'what should I bet on today' question, indicating its purpose for discovering opportunities. It provides clear context on when to use it, though it does not explicitly state when not to use it or compare to 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?

Without annotations, the description covers core behavior (retrieve/list) and scoping, but does not detail error cases like missing keys or authorization beyond scoping. Adequate 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?

Three sentences, front-loaded with action, no redundant words. Efficient and clear.

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 retrieval, listing, scoping, and pairing with siblings. Minor gaps in error handling but overall sufficient for a simple tool.

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

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

Schema already describes the key parameter; description adds value by explaining that omitting it lists all keys and contextualizing its purpose as a memory key.

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 retrieves a value saved via remember, or lists all keys if omitted. Explicitly 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 clear context for when to use (look up stored context) and mentions scoping and pairing with siblings, but does not explicitly state when not to use.

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

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

No annotations were provided, so the description carries full burden. It discloses parallel fan-out to multiple data sources (SEC, GDELT, USPTO), accepted date formats, and return structure (structured changes, count, URIs). This gives good behavioral insight but could mention rate limits or error handling.

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

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

The description is well-structured and concise. It front-loads the purpose and usage examples, then efficiently covers key details (data sources, date format, return format) without redundancy. Every sentence adds value.

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

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

Given no output schema, the description adequately describes the return format. It covers all parameters and behavioral aspects. Could mention edge cases or limitations, but is sufficient for the tool's complexity.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds moderate value by clarifying date format usage ('Use '30d' for typical monitoring') and value formats, but does not significantly extend beyond the schema.

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

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

The description clearly states the tool's purpose: to get recent changes for a company. It provides specific verb-resource combinations ('What's new with a company') and includes many example queries that distinguish it from siblings like 'entity_profile' or 'validate_claim'.

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

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

The description explicitly tells when to use the tool, listing natural language queries and monitoring scenarios. It lacks explicit exclusions or alternatives, but the examples strongly imply the usage context.

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

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

No annotations provided; description discloses key-value scoping, persistence rules (authenticated vs anonymous), and pairing behavior, which is largely sufficient for safe use.

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

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

Well-structured with purpose first, then usage, then storage details. Could be slightly shorter but every sentence adds value.

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

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

Given a simple key-value store with no output schema, the description fully covers purpose, usage, persistence, and integration with other tools.

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

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

Schema coverage is 100% and descriptions are already clear; the description adds contextual naming conventions and examples, enhancing usability 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?

Clearly states 'save data' with verb and resource, provides concrete examples, and distinguishes from siblings 'recall' and 'forget'.

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

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

Explicitly states when to use ('when you discover something worth carrying forward'), scope, persistence differences, and names alternatives ('Pair with recall to retrieve later, forget to delete').

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?

Describes return values (IDs plus citation URIs) and gives examples. Since no annotations exist, description carries full burden; it sufficiently explains read-only lookup behavior without side effects.

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

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

Four sentences with front-loaded purpose, examples, and guidance. Every sentence adds value; 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 low complexity (2 simple params) and no output schema, the description fully explains the tool's purpose, input, output, and usage sequence. It leaves no major gaps for the agent.

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

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

Schema covers all parameters with descriptions; description adds contextual examples and clarifies acceptable input formats (e.g., ticker, CIK, or name for company). This significantly enhances agent 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?

Description clearly states the tool resolves entity names to canonical identifiers (CIK, ticker, RxCUI, LEI). It provides concrete examples and distinguishes itself from siblings by focusing on identifier lookup before other calls.

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

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

Explicitly advises use when a user provides a name and you need official identifiers, and instructs to call this before other tools requiring identifiers. However, it does not explicitly exclude cases where identifiers are already known.

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 burden. It describes the search operation and return format but omits details like pagination behavior (despite schema coverage), rate limits, or potential side effects. The description is adequate but not 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 only two sentences long, efficiently stating the tool's purpose and output. It is front-loaded with the key action and resource, with 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 the tool has three parameters, no output schema, and no annotations, the description provides enough information to understand the tool's function and output shape (top hits with specific fields). It covers the essential aspects, though behavioral details like pagination default are absent.

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 descriptions cover all three parameters (query, page, per_page). The tool description adds value by specifying that the query can be a title, artist, or lyrics excerpt, which enriches the semantic meaning beyond the schema's generic 'Search term'.

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 main action ('Search Genius for songs') and specifies search fields (title/artist/lyrics excerpt) along with return fields (song ID, title, primary artist, full title, URL). It differentiates from sibling tools like get_song or list_artist_songs, which serve more specific retrieval 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 does not explicitly state when to use this tool versus alternatives such as get_song (for a known song ID) or list_artist_songs (for songs by an artist). The usage context is implied but could be more direct to guide 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?

No annotations provided, so description carries the burden. It mentions using SEC EDGAR + XBRL, returns a verdict, and replaces multiple calls. However, it doesn't discuss edge cases, failure modes, or performance trade-offs.

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, all essential and well-structured. No redundancy or unnecessary 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?

Given the tool has only one parameter and no output schema, the description provides sufficient context: input, sources, output types, and efficiency benefit. Could mention limitations on claim types.

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

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

Schema coverage is 100% for the single 'claim' parameter. Description adds context with example claim formats, enhancing 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 fact-checks natural-language factual claims, with a specific scope of company-financial claims. It distinguishes itself from siblings by being a verification tool, while siblings are music or memory related.

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 'Use when an agent needs to check whether something a user said is true' and provides example queries. It doesn't mention when not to use, but the scope is well-defined.

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