Thesportsdb

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

Description provides extensive behavioral context beyond annotations: default model is Workers AI Llama-3.3-70b (free), probing Anthropic requires BYO key, returns per-model and combined results. No contradictions with annotations.

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

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

Two well-structured sentences. First sentence states core purpose and scoring. Second sentence covers models, API key, and return details. No filler.

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

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

Given no output schema, description explains return structure (per-model fields and combined view). Covers all parameters and use cases. Fully adequate for a probing 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?

Adds significant meaning beyond schema: explains default model, that _apiKey enables Anthropic probing and direct payment, and that context helps disambiguate. Schema coverage is 100%, but description enriches each parameter.

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 probes LLMs for knowledge of a brand/product/topic and returns visibility scores. It uses specific verbs ('probe', 'score') and distinguishes from siblings like 'scan_competitor_ai_presence' by being more generic.

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?

Description outlines use cases (AI-marketing audits, pre-launch checks, competitive monitoring) and explains when to use the _apiKey parameter. However, it does not explicitly state when not to use the tool or compare with alternatives among siblings.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: it routes to 4,774 tools, returns structured answers with stable pipeworx:// citation URIs, works on every tier, and is one fast call. No contradictions. Could mention rate limits or authentication briefly, but overall sufficient.

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 lengthy but well-structured: it starts with a clear directive ('PREFER OVER WEB SEARCH'), then states capabilities, provides usage guidance, examples, and sibling differentiation. Every sentence adds value. Could be slightly more concise, but the thoroughness is beneficial for an agent.

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, the description covers all necessary aspects: when to use, what it does, what it returns (structured answer with citations), examples, and how it relates to siblings. No output schema exists, so the description compensates by explaining the return format. 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% (each parameter is documented in the schema). The description does not add semantic detail beyond the schema; it just repeats that the parameter is a natural language question and lists aliases. Baseline 3 is appropriate since the schema covers the parameters adequately.

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: answering factual questions with structured data from many sources. It uses specific verbs like 'route', 'fill arguments', 'return structured answer', and distinguishes from siblings like ask_pipeworx_grounded and deep_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 says 'PREFER OVER WEB SEARCH' and provides detailed when-to-use guidance. It also clarifies when to step up to other tools: ask_pipeworx_grounded for hallucination-resistant answers and deep_research for broad questions. Examples of appropriate queries are given.

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 routing across 4,774 tools, argument filling, data fetching, and extraction only from tool result; details refusal reasons. No contradiction with annotations (readOnlyHint, etc.) and adds significant context beyond annotations.

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

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

Well-structured with front-loaded purpose. Each sentence adds value, though slightly verbose; could be tightened without losing clarity.

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

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

Despite no output schema, the description fully details the return structure (success and refusal) and covers failure modes. Complete for a complex tool with routing and grounding.

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

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

With 100% schema description coverage, baseline is 3. Description adds that the question is used for routing and argument filling, providing meaning beyond the schema listing aliases.

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 it is a hallucination-resistant answer mode for high-stakes reads, explicitly distinguishes from sibling ask_pipeworx by describing the grounded extraction process. Uses specific verbs and resources.

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

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

Explicitly provides when to use (when answer will be quoted, cited, or acted on, with examples like financial verdicts or medical lookups) and when not to use (prefer ask_pipeworx for casual lookups due to extra cost).

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

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

The description extensively details behavioral traits beyond annotations: market resolution process, fan-out behavior, response shapes, safety mechanisms (low-confidence short-circuit, closed market handling, wide-spread warnings), and resolution-rule risk. Annotations (readOnlyHint, idempotentHint, destructiveHint) are consistent, and the description adds significant 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?

The description is extremely verbose, containing exhaustive enumerations of classifiers, fan-out examples, and detailed response shapes. While comprehensive, it lacks conciseness and could be structured more efficiently, e.g., by separating core purpose from detailed reference 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 (multiple data sources, classifiers, safety paths, cancellation rules), the description is remarkably complete. It covers all behavioral aspects, return value shapes (market, analysis, evidence, resolver contract, parent events, news fallback), and edge cases (low confidence, closed markets, wide spreads). Without an output schema, this level of detail is necessary and 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 coverage is 100% for all three parameters. The description adds meaning to each parameter: depth enum with explanations, market with examples, and include_raw with behavioral rationale and size implications. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool's purpose: researching Polymarket bets by pulling Pipeworx data. It specifies input types (slug, URL, question text), outputs (evidence packet, market-vs-model comparison), and distinguishes it from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on single-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 states when to use the tool ('Use for "should I bet on X"...') and provides extensive examples for various bet types. However, it does not explicitly mention when not to use it or point to alternative tools, though the context of sibling tools provides some implicit guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds rich behavioral context: it explains the data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and output format (paired data + citation URIs). No contradiction with annotations.

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

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

The description is slightly long but well-structured with front-loaded example queries. Every sentence adds value, explaining data sources, sorting, and output. Could be slightly more concise, but still efficient.

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

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

Given the tool's complexity (2 entity types, multiple data sources) and the absence of an output schema, the description provides complete context: purpose, usage, data sources, sorting behavior, and output format (paired data + citation URIs). It covers all necessary information for an agent to use the tool 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% with descriptions for both parameters. The description adds meaning beyond schema: it provides examples of values ('AAPL','MSFT' for companies, 'ozempic','mounjaro' for drugs) and explains how each type affects the data pulled. This helps the agent understand parameter semantics beyond the enum and array constraints.

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 performs side-by-side comparison of 2-5 companies or drugs. It provides example queries like 'X vs Y' and 'rank these companies', and distinguishes itself from sibling tools like entity_profile (single entity) by explicitly noting 'ALWAYS PREFER over sequential single-pack lookups'.

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 usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities'. It also specifies when to use each type (company vs drug) with detailed data sources (10-K for companies, FAERS for drugs). No ambiguity about when to use this tool vs 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?

Description adds significant behavioral context beyond annotations: account/plan requirements, parallel decomposition, return format (evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[] array), expected latency (15-60s), and limitations (not open-web search). 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?

Description is fairly long but front-loads critical info (account required). Every sentence adds value, covering purpose, usage, behavior, and parameters. Could be slightly more concise, but structure is logical and no redundant filler.

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

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

Despite no output schema, description thoroughly explains return format (findings packet with evidence, confidence, source, fetched_at, citations, gaps[] array). Also covers edge cases (breaking news returns empty gaps). With only 2 parameters, this provides complete operational 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%, so baseline 3. Description adds meaning by explaining depth enum values (quick=3, standard=5, thorough=8) and linking thorough to paid plan. Also clarifies that question accepts broad/multi-part queries. These details enhance schema beyond the basic enum and string 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 performs grounded multi-source research across Pipeworx's structured data sources in one call, contrasting with sibling tools like ask_pipeworx and ask_pipeworx_grounded. It uses specific verbs ('researches', 'decomposes', 'routes') and specifies the resource (1242 structured data sources).

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

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

Explicitly provides when to use and when not: for single lookups use ask_pipeworx, for breaking/current news prefer ask_pipeworx. Also notes account requirement and that ask_pipeworx works without sign-in. This helps the agent select the correct tool.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so description adds value by explaining return format: 'Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly.' No contradiction.

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

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

Three concise sentences that front-load the purpose, provide usage guidance, and describe behavior. 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 discovery tool with no output schema, the description adequately explains purpose, input semantics, output format, and usage context. Fully covers what an agent needs to know.

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

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

Schema coverage is 100%, so description doesn't add much beyond examples and explaining the output nature. The mention of 'curated examples' adds some semantic value, but 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 clearly states 'Find tools by describing the data or task', lists many specific domains, and distinguishes from sibling tools by being a discovery/browsing tool. Verb+resource is specific and actionable.

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 (not just one answer).' Provides clear context for when to use this tool, but doesn't explicitly state when not to use it or list alternatives by name.

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

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent. Description adds behavioral context: parallel call, fan-out across sources, soft-fail on patents, and data sources (GDELT→GNews fallback). No contradictions.

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

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

Dense but well-structured single paragraph with clear line breaks for output fields. Examples are front-loaded. Slightly long, but every sentence adds value. Could be trimmed slightly but 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?

Despite no output schema, description thoroughly explains return structure (cik, company_name, recent_filings with URIs, fundamentals sorted, patents status, news fallback, LEI). Addresses limitations and alternatives. Complete 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 clear descriptions for both parameters. Description reinforces that value accepts ticker or CIK and explicitly states names not supported, adding the zero-padded CIK requirement and reference to resolve_entity.

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

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

Description clearly states the tool provides a 'full cross-source profile of a US public company' and lists specific resources (SEC EDGAR, XBRL, USPTO, news, GLEIF) and return fields. It explicitly distinguishes from siblings by recommending it over chaining single-pack lookups.

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 guidance ('when the user asks for a holistic view'), when-not-to-use ('names not supported'), and suggests an alternative ('use resolve_entity first'). Includes multiple example 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description confirms the read-only nature but adds no new behavioral traits beyond the scope and filtering capability, so minimal added value.

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

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

Two sentences with embedded examples; front-loaded with the main purpose. Efficient but could be slightly more compact by removing redundant phrasing. Appropriate length for the content.

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

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

With an existing output schema, the description need not cover returns. It adequately covers usage, filters, and date conventions. Lacks mention of pagination or result limits, but for a schedule tool this is acceptable.

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

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

Schema coverage is 100% with clear parameter descriptions. The description adds practical value by providing concrete sport name examples (e.g., 'Ice Hockey' for NHL) and the date format YYYY-MM-DD, enhancing usability beyond the schema alone.

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

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

The description explicitly states it returns 'sports schedule/fixtures for a given date' with optional filtering, and directs agents to prefer it over web search for specific queries, clearly distinguishing its purpose from generic tools.

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

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

Provides clear when-to-use guidance with examples like 'what games are on today/tomorrow' and explains how to handle 'next 24h' by passing two dates. However, does not explicitly exclude sibling tools like team_events_last.

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

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

Annotations already convey destructiveHint=true. Description adds context about when to delete (stale, done, sensitive), which is behavioral guidance beyond annotations.

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

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

Two sentences with no wasted words. First sentence defines core action; second sentence provides usage guidance and sibling context. Front-loaded and efficient.

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

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

Given single parameter, no output schema, and informative annotations, the description covers what the tool does, when to use it, and how it relates to siblings. No 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% and schema already describes the only parameter 'key' as 'Memory key to delete'. Description adds no additional parameter meaning, 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 clearly states 'Delete a previously stored memory by key' – specific verb ('delete'), resource ('memory'), and method ('by key'). Distinguished from siblings 'remember' and 'recall' by explicit 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 use cases: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Also mentions pairing with related tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds context by explaining it fetches the page, extracts title/description/key links, and emits standard markdown format. No contradictions; it enhances transparency.

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

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

The description is concise: two sentences plus a bullet list of use cases. It's front-loaded with the core action and output, followed by practical applications. Every sentence adds value without redundancy.

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

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

Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description covers purpose, process, output format, and usage scenarios completely. No additional context is necessary for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining 'Full URL' and the purpose of max_links (limit link entries), and it clarifies the output format (single text blob ready to drop at site-root/llms.txt), which is not in 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 generates a production-ready llms.txt file for a given URL, specifying the verb 'generate' and the resource 'llms.txt file'. It distinguishes from sibling tools like ai_visibility_check or scan_competitor_ai_presence by focusing on the specific output format and use case for AI crawler indexing.

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

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

The description provides explicit use cases: getting a client's site indexed, drafting your own llms.txt, or auditing how an AI crawler sees a competitor. While it doesn't explicitly state when not to use it, the 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that it returns a 'full player profile' with listed fields (name, team, etc.), which provides expected output details. However, it doesn't go beyond what annotations suggest; it is consistent but not rich with additional behavioral traits like rate limits or auth needs.

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, front-loaded with the core purpose, and lists key return fields efficiently. Every part earns its place with no waste.

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

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

Given the tool's simplicity (one parameter, output schema exists), the description is complete: it clarifies input type and expected return fields. It could optionally mention that the ID must be an integer (implied by 'numeric'), but that is minor. Overall, it sufficiently equips an agent to use the 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?

There is only one parameter (player_id) with 0% schema description coverage. The description adds 'numeric player_id', clarifying the required type (numeric string) beyond the schema's generic 'string' type. This adds significant meaning, especially given the simplicity of the parameter.

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

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

The description clearly states it retrieves a 'Full player profile' from TheSportsDB using a numeric player_id, listing specific returned fields (name, team, nationality, etc.). This is a specific verb+resource combination that distinguishes it from sibling tools like search_players (which uses search terms) or get_team (which returns team data).

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

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

The description implies usage when you have a numeric player_id, but it does not explicitly state when to use this tool versus siblings like search_players. For example, it could mention 'Use this if you have a specific player ID; use search_players if you only have a name.' This guidance is absent, so the score is average.

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, openWorldHint, idempotentHint, and destructiveHint false. Description adds value by detailing returned fields and confirming the tool's safety and idempotence, with no contradictions.

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

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

Single sentence that front-loads the purpose and lists key outputs. No unnecessary words; every part 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?

For a simple read-only tool with one parameter and a defined output schema, the description provides all necessary context: data source, input format, and return fields. Complete and 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?

Only one parameter (team_id) with 100% schema coverage. Schema describes it as 'TheSportsDB team id', and description reinforces it as numeric. This adds minimal extra meaning beyond the schema, 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?

Description clearly states it retrieves a full team profile from TheSportsDB using a numeric team_id, listing specific fields like name, sport, country, etc. It distinguishes itself from sibling tools like get_player or search_teams by focusing on team data and requiring an ID.

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 mentions the required input (numeric team_id) and the output scope. While it doesn't contrast with alternatives like search_teams or league_teams, the context implies use when the ID is known, which is clear enough for an AI 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description's claim of returning standings is consistent but adds little beyond that. The description could clarify ordering (e.g., by rank) or that it returns a table format, but with strong annotations, this is adequate.

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

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

Extremely concise single sentence. It front-loads the core purpose. However, it is slightly under-specified; a brief addition on what 'standings table' entails would not compromise 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?

With an output schema present, return values don't need description, but the tool has two parameters, one of which ('league_id') is completely undocumented in both schema and description. Given sibling tools like 'list_leagues' exist, a cross-reference would improve completeness. Overall, the description leaves significant gaps for a user to correctly invoke the tool.

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

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

Schema description coverage is 50% (only 'season' has a description). The tool description does not add any details about 'league_id' format or how to obtain it, and does not clarify the default for 'season'. It relies entirely on the schema, which is insufficient for users without domain knowledge.

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 'Standings table for a league/season' clearly states the tool returns standings, differentiating it from sibling tools like 'league_teams' or 'list_leagues' by specifying it provides a table (ordered rankings). However, it lacks an explicit verb like 'get' or 'retrieve' and could more precisely contrast with similar 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?

No guidance on when to use this tool versus alternatives such as 'league_teams' (which might list teams without standings) or 'list_leagues'. There is no mention of prerequisites or contexts where this tool is preferred.

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, destructiveHint=false, so the agent knows it's a safe read operation. The description adds the return format (id, name, badge, country) but does not disclose whether pagination applies, rate limits, or if the data is cached. This adds some value beyond annotations 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?

A single sentence that front-loads the verb and resource, then specifies the parameter and output. 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?

The tool has an output schema (though not shown), and the description mentions return fields, which is sufficient for basic usage. It does not address pagination, ordering, or error cases, but for a simple list tool with a single parameter, it is adequately 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 0%, so the description should compensate. It mentions 'league_id' in context ('by league_id') but does not explain what a league_id is, its format, or how to find it. The parameter name is self-explanatory to a degree, but for a data source like TheSportsDB, more guidance would be helpful.

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

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

The description specifies the verb 'List', the resource 'all teams in a league', the required parameter 'league_id', and explicitly lists the returned fields (id, name, badge, country). This clearly distinguishes it from siblings like get_team (single team) and search_teams (search by name).

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 clearly states when to use the tool: when you need to list all teams for a given league via league_id. It implies when not to use (e.g., for a single team, use get_team; for search by name, use search_teams) but does not explicitly provide alternatives or 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds the filtering capability but does not disclose pagination, data freshness (openWorldHint), or response structure. Minimal added value beyond annotations.

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

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

Single sentence, no fluff. Extremely concise and front-loaded with the core action 'List leagues'.

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

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

With an output schema present, the description does not need to detail return values. The description is adequate for a simple listing tool with two optional parameters, though it could mention that unfiltered returns all leagues.

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

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

Schema coverage is 100% with descriptions for both parameters. The description merely restates that filters are optional, adding no new semantic meaning. Baseline score is appropriate when schema already explains 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 lists leagues, with optional filtering by sport and country. It distinguishes from sibling tools like 'list_sports' and 'league_teams', though it could explicitly differentiate from other list 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?

No guidance on when to use this tool versus alternatives (e.g., 'list_sports' for available sports, 'league_table' for specific league data). The description only mentions optional filters without clarifying when filtering is needed.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, which cover safety and idempotency. The description adds minimal behavioral context beyond the return fields, which are also present in the output schema. It does not elaborate on rate limits, data freshness, or 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 sentence that efficiently conveys purpose, examples, and return fields without unnecessary words. It is front-loaded with the core action.

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

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

For a simple list tool with no parameters and an output schema, the description is complete. It covers what the tool returns and gives concrete examples, leaving no ambiguity.

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 zero parameters with 100% coverage, so the description does not need to add parameter semantics. The description's mention of example sports and fields provides context but adds little 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 what the tool does: 'Return all sports tracked by TheSportsDB', including specific examples and the returned fields (id, name, forum). This distinguishes it from sibling tools that operate on individual entities or different aspects.

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 sports but does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or alternative tools. However, given the simplicity of the tool and the context of sibling tools, the usage is reasonably 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?

Adds context beyond annotations by specifying return fields and the effect of the include_inactive parameter. Annotations already indicate read-only and idempotent behavior.

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

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

Two efficient sentences covering purpose, return fields, and usage. 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?

Covers purpose, return values, usage, and parameter effect. Lacks mention of potential pagination for large lists, but overall adequate for this 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 coverage is 100% and description doesn't add new meaning to the parameter beyond what's in the schema, but implies the default behavior (active only).

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 it lists the caller's active subscriptions with specific return fields. Distinguishes from sibling tools like subscribe and unsubscribe.

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

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

Explicitly advises using it to review subscriptions before adding more or to find an id to cancel, providing clear context for when 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?

Discloses rate-limiting (5/day/identifier), free usage, and non-counting against quota. Annotations are neutral (no read-only or destructive hints), and description adds valuable behavioral context beyond annotations.

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

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

Well-structured: purpose first, then use cases, etiquette, constraints. Every sentence adds value, no redundancy. Front-loaded with key info for quick agent 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?

Provides sufficient context for a feedback tool: purpose, usage, constraints, and outcome (digests, roadmap impact). Without an output schema, the description covers what the agent needs to know to use the tool effectively.

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

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

Schema covers all parameters with descriptions, but the description adds significant value by elaborating on enum meanings and providing examples for message content. This enhances understanding beyond the schema alone.

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

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

The description clearly states the tool's purpose: to send feedback about bugs, feature requests, data gaps, or praise. It distinguishes itself from sibling tools by being the only feedback channel, and lists specific use cases.

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 (bug, feature/data_gap, praise) and what not to do (don't paste end-user prompt). Also includes rate limits and quota, providing clear decision guidance vs. 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 declare readOnlyHint, openWorldHint, idempotentHint. Description adds context: data derived from CF analytics-engine, no PII, cached 5min-1h. No contradictions.

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

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

Concise yet information-dense: three sentences covering purpose, use cases, and technical details. 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 read-only tool with one param and good annotations, the description fully explains what to expect (top tools, packs, call volume) and how it works (aggregated, cached, no PII). No 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?

Single parameter 'window' with enum and description in schema (100% coverage). Description adds value by explaining trade-off: 'shorter windows surface what's hot right now; longer windows show steady-state demand.' Exceeds 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 states specific verb+resource: 'Returns the top tools, top packs, and total call volume' over a window. Distinguishes from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on trending data from other agents.

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?

Lists three explicit use cases (discovering hot data, confirming canonical tool, checking alignment). Lacks explicit when-not-to-use or alternatives, but the use cases effectively guide selection.

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

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

Annotations already mark the tool as read-only and idempotent. The description adds rich behavioral context: monotonicity and partition checks, similarity filter, placeholder fraction, and the fill check with CLOB depth. No contradictions with annotations.

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

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

The description is well-structured with a front-loaded summary and clear sections for each mode. However, it is somewhat verbose and could be tightened without losing essential information. Still, 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 the tool's complexity (three modes, multiple checks) and no output schema, the description is remarkably complete. It explains inputs, behavioral logic, response structure (including fields like opportunities[], partition_check, and fill_check), and edge cases.

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

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

Schema coverage is 100%, but the description adds significant value beyond the schema: it explains that event accepts slugs or full URLs, topic accepts seed questions, and passing no args triggers trending_scan. This clarifies the exact usage of each parameter.

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: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) and uses specific verbs and resources, setting it apart from sibling tools like polymarket_edges or polymarket_fill_risk.

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

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

The description provides explicit guidance on when to use each mode: no args for a trending scan, event for a specific market, topic for cross-event scanning. It also explains when not to trade (based on fill check) and mentions the sibling tool polymarket_fill_risk for custom sizing.

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

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

The description adds extensive behavioral context beyond the annotations, including detailed explanations of the three model families, edge calculation, caching (1h at KV level), Fed data disclaimer, and diagnostic information. No contradiction with annotations is present.

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 with sections but is quite verbose at over 1,000 words. It front-loads the purpose but includes many details that could be summarized or omitted, such as the extensive explanation of each model family. The length may hinder 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 tool's complexity (9 parameters, no output schema), the description is very complete. It explains the response structure (by_segment, diagnostics), edge calculation details, tradeable-edge knobs, caching, and limitations (e.g., Fed data unreliability). An agent has all necessary context to use the tool 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?

All 9 parameters have schema descriptions (100% coverage), so baseline is 3. The tool description adds extra meaning, such as explaining the default for slippage_pp and the design rationale for min_partition_leg_kelly, which helps the agent understand parameter behavior 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: scanning Polymarket markets to find edges where Pipeworx data disagrees with market prices. It explicitly mentions 'Built for what should I bet on today' and distinguishes its function from other tools like polymarket_arbitrage by focusing on Pipeworx data disagreement.

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

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

The description provides clear guidance on when to use this tool (for discovering betting opportunities) and explains the three segments and filter knobs. However, it does not explicitly state when not to use it or directly compare it to siblings like polymarket_arbitrage, leaving some ambiguity for 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?

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds substantial detail: response structure (tracked, expired, snapshot_dates), trend computation, decay calculation, and data gap explanation, enhancing transparency beyond annotations.

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

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

The description is well-organized: purpose, args, response format, limits. It front-loads the key question. Slightly lengthy but efficient for the complexity; could be tightened but not wasteful.

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 thoroughly explains the response: tracked objects with fields, expired objects with lifespan, and snapshot_dates. It covers data gaps, trend computation, and limits, making the tool's behavior fully understandable.

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 both parameters with descriptions. The description adds default values (days=14, window='1wk'), maximum for days (30), and elaborates on the meaning of each parameter (lookback, snapshot family), providing value beyond the schema.

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

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

The description clearly defines the tool's purpose: tracking edge persistence and decay over time, answering a specific question about edge age and trend. It distinguishes itself from related tools like polymarket_edges by focusing on historical telemetry rather than current snapshots.

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 context on when to use the tool (to understand edge longevity) and includes limits (TTL, snapshot start). However, it does not explicitly state when not to use it or name alternatives, though sibling tool names suggest differentiation.

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

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description details the tool's behavior: walking the order book ladder, returning top-of-book, VWAP, slippage, verdict (clean/degraded/cannot_fill), and for basket mode, forced directional risk and thin legs. No contradictions with annotations.

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

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

The description is dense but well-structured with clear sections (REQUIRES, mode explanations, return fields). It is front-loaded with the core purpose and usage directives. While lengthy, every sentence adds necessary detail for correct use.

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

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

Despite no output schema, the description thoroughly enumerates all returned fields for both modes, including verdicts and risk indicators like thin_legs and forced_directional_risk. It is slightly lacking in error handling or parameter validation details, but overall is comprehensive for a complex tool.

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

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

Schema coverage is 100%, but the description adds meaningful context: it clarifies that size_usd is interpreted as max spend on buys vs target proceeds on sells, and for basket mode as settlement notional. It also explains default side behavior (auto from partition sum). This adds value beyond the parameter descriptions in 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 as a 'realizable-vs-theoretical edge check against live CLOB order-book depth', specifying single-market and basket modes with distinct return structures and example use cases. This distinguishes it well from siblings like polymarket_arbitrage and polymarket_edges.

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 to use this tool before acting on polymarket_arbitrage signals or for trades above ~$500, explaining the risks of theoretical overround on thin books and partial basket fills converting into directional positions. This provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already declare the tool as read-only, idempotent, open-world, and non-destructive. The description adds substantial behavioral context beyond annotations: it explains safety fields (compatibility_warning, temporal_alignment, skipped_cross counters), discloses that real spreads are rare, and warns that pre-mapped does not mean tradeable.

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 with clear headings (TWO MODES, RESPONSE, SAFETY FIELDS) and uses capitalization for emphasis. However, it is somewhat verbose, particularly in the safety fields section. A more concise version could retain all key 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 has no output schema, the description comprehensively covers the response format, including venue prices, top spreads, and safety fields. It addresses edge cases like temporal misalignment and shape incompatibility. The context is complete for an agent to understand the tool's capabilities and limitations.

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 by explaining the two modes (topic vs explicit tickers), enumerating topic values, providing examples, and clarifying the relationship between kalshi_event_ticker and polymarket_event_slug. This goes well beyond the schema's minimal 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: computing cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes itself from siblings by focusing on a specific dual-venue comparison, not single-venue analysis.

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

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

The description explains when to use the tool (to find arbitrage opportunities or compare pricing across venues) and provides important contextual guidance: pre-mapped topics are not guaranteed to return tradeable spreads, and compatibility warnings indicate no arb exists. It could be improved by explicitly stating when to prefer sibling tools like polymarket_arbitrage or bet_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?

Annotations cover safety (readOnlyHint, idempotentHint, destructiveHint). Description adds scoping detail (anonymous IP, BYO key hash, account ID) and behavior of omitting key (list all). No contradictions.

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

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

Three short sentences, each earning its place. Front-loaded with main action. 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, description covers invocation and scope. Returns are implied but not specified. Adequate for simple tool with rich annotations.

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 100% for single parameter. Description adds explanation that omitting key lists all saved keys, beyond what schema 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?

Description clearly states verb (retrieve/list) and resource (values saved via remember). 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 explicit when-to-use context (look up stored data like ticker, address, notes). Mentions pairing with remember/forget. Lacks explicit when-not-to-use but implies it's for retrieval only.

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 readOnly, openWorld, idempotent, and non-destructive. The description adds context about mark_read:true changing behavior for subsequent calls and mentions the feed persistence, exceeding annotation burden.

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 cover purpose, return values, filtering, and alternative access. Front-loaded with main action. No filler; each sentence adds information. Could be slightly tighter but effective.

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

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

Given 0 required params, no output schema, and annotations, the description explains return format, filtering, mark_read semantics, and polling viability. Missing explicit mention of limit behavior or pagination, but adequate.

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

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

With 100% schema coverage, baseline is 3. The description adds value by explaining the effect of mark_read (consuming events) and unread_only, and clarifies that since is an ISO timestamp, which is not in schema descriptions.

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

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

Verbs 'pull' and 'returns' clearly indicate the action, and 'fired events from your subscription feed' specifies the resource. The description mentions return fields (source, citation_uri, raw payload) and filtering options, making it distinct from siblings like events_by_day.

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 that polls work fine and provides an alternative direct endpoint for scripts/dashboards, guiding when the tool is appropriate. Does not explicitly exclude scenarios but implies trade-offs.

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

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

Beyond annotations, the description details multi-source fan-out, fallback logic (GDELT to GNews), and soft-fail for USPTO. This gives the agent deep understanding of behavior.

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

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

The description is efficient with examples and structured, though slightly verbose. Every sentence adds value, but could be tightened slightly.

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

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

For a complex multi-source tool, the description covers purpose, parameters, behavior, and output format (changes grouped by source, count, citation URIs) without needing an 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?

Schema coverage is 100%, but the description adds context beyond the schema: relative date shorthand examples, ticker vs CIK notation, and typical monitoring recommendation for 'since'.

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

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

The description clearly defines the tool as a change feed for companies, listing concrete use cases ('What's new with X'). It specifies the resources (SEC, GDELT, USPTO) and explicitly distinguishes from the sibling tool 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?

The description provides explicit when-to-use examples and an alternative (entity_profile for static profiles). No missing guidance.

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

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

Annotations already show idempotentHint=true and destructiveHint=false. Description adds valuable context: memory persistence based on authentication, 24-hour retention for anonymous sessions, and scoping by identifier. No contradictions.

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

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

Description is front-loaded with main purpose and usage examples. Each sentence adds value, though could be slightly more structured. 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 2-parameter tool with no output schema, the description adequately covers purpose, usage, and behavioral aspects. No obvious gaps given tool 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?

Input schema has 100% coverage with descriptions for key and value parameters. Description adds minimal extra meaning (e.g., example keys), but schema already does primary job. 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 uses specific verb 'save' and resource 'data', lists concrete use cases (resolved ticker, target address, user preference), and distinguishes from sibling tools 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 discovering something worth carrying forward) and pairs with recall/forget for retrieval and deletion, providing clear usage context.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds significant behavioral context: cascading through multiple lookup endpoints internally, auto-disambiguation for companies, and specific return values (ticker, CIK, RxCUI). No contradiction.

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

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

Description is well-structured with a clear front-load of purpose and use case. It uses examples and bullet-like formatting for types. While slightly lengthy, every sentence adds value and there is 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 the tool has only 2 parameters, no output schema, and rich annotations, the description adequately covers expected inputs and outputs for each type. It mentions that it replaces multiple manual lookups, setting expectations. No missing critical information.

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

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

Schema coverage is 100%. Description adds value beyond schema by providing concrete examples and explaining acceptable inputs for each entity type (e.g., company can be ticker, CIK, or name; drug accepts brand or generic name). This enriches understanding beyond the schema's enum and 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 resolves user-spoken names to canonical IDs, provides multiple examples ('What's the ticker for...'), and explicitly distinguishes it from siblings with 'Use FIRST whenever you have a name but need an ID.' Specific verb+resource with strong differentiation.

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 FIRST whenever you have a name but need an ID,' indicating primary use case. It details supported types and what each returns, providing clear context. No explicit when-not-to-use, but the directive is strong enough to guide selection among siblings.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, establishing safety. The description adds value by detailing the process: 'Probes each entity... ranks by score, surfaces which is most/least recognized' and the output format (ranked list with score, confidence, signal density). No contradictions with annotations.

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

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

The description is extremely concise, consisting of two sentences that immediately convey the main action and a practical use case. Every sentence earns its place with no fluff.

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

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

Given the tool's moderate complexity (4 parameters, no output schema), the description covers the core functionality, process, and output format. It lacks details on error handling or edge cases, but for a read-only tool with strong annotations, this is 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%, so baseline is 3. The description adds one valuable detail: 'First entry treated as the subject for narrative; rest are competitors.' This provides semantic context beyond the schema, justifying a score of 4.

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 AI visibility across multiple entities side-by-side.' It specifies the action ('compare'), the resource ('AI visibility'), and the scope ('multiple entities'). This distinct purpose differentiates it from siblings like 'ai_visibility_check' (single entity) and 'compare_entities' (general 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 a concrete use case: 'Useful for competitive AI-marketing audits: does Claude know about us as well as our competitors?' It implicitly guides when to use the tool but does not explicitly state when not to use it or mention alternative tools among siblings.

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

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

Adds details beyond annotations: partial failures, bundlephobia latency (5-30s), sources_failed list. Annotations already cover safety; description adds useful context.

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?

Structured description: overview, use case, return fields, limitations. Slightly long 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?

Comprehensive for a complex multi-source tool with 7+ return fields and error handling. No output schema, but description covers all key aspects.

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 both parameters (100%). Description adds context: scoped packages accepted, version defaults to latest. Adds 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 composite check for adding npm package, covering license, advisories, bundle size. Distinguishes from unrelated 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 says when to use: when agent asks about safety/popularity/size. Mentions NPM-only limitation and fallback for other ecosystems.

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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds context about returned fields (player id, team, nationality, etc.) and the usage sequence, which goes beyond the structural annotations.

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

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

Two sentences with no wasted words. First sentence states the purpose and returns, second adds usage guidance. Highly efficient and 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?

Given the tool's simplicity (one required param, no nested objects, output schema present), the description covers purpose, return fields, and a usage hint. It lacks details on partial matching or case sensitivity, but overall it is sufficiently complete for an AI agent.

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

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

The schema has 0% description coverage, so the description must compensate. It says 'by name' for the query parameter, which adds basic meaning. The schema provides examples (Cristiano Ronaldo, LeBron James), so combined it is adequate but could be more explicit about formatting or matching behavior.

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

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

The description clearly states 'Search TheSportsDB for players by name' with a specific verb and resource. It lists returned fields and explicitly distinguishes from sibling 'search_teams' by advising to use that tool first for team IDs.

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 advises using search_teams first if team IDs are needed, providing clear context on when to prefer the sibling tool. No explicit 'when not to use' is given, but the guidance is sufficient.

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

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

Annotations already indicate read-only, idempotent, open-world, non-destructive. The description adds value by listing concrete return fields (team id, sport, league, etc.) and mentioning the data source, which are not in 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?

A single sentence followed by a bullet list of return fields. No wasted words, front-loaded with the core action and resource.

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 tool with one parameter and an output schema, the description covers purpose, usage hint, and return fields. It could mention case sensitivity or partial match behavior, but the openWorldHint partially addresses that. Adequate overall.

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 'query' parameter has no schema description (0% coverage), but the description explicitly states it's a team name ('by name'), adding crucial meaning beyond the type string.

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

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

The description clearly states it searches for sports teams by name, with specific verb 'Search' and resource 'TheSportsDB for sports teams.' It distinguishes from siblings like get_team (which uses ID) by focusing on name-based search and listing return fields.

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 usage guidance by mentioning the returned team id can be used with get_last_events/get_next_events, implying the tool is for initial lookup. However, no explicit alternatives or when-not-to-use advice is provided.

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

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

Disclosures include embedding model (BGE-base-en), windowing (500-char overlapping windows), similarity metric (cosine), character cap (200K chars), and truncation flagging. These details go well beyond the annotations (readOnlyHint, idempotentHint, etc.), providing rich behavioral context.

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 approximately 6 sentences, starting with the primary action. Every sentence provides essential information without repetition or unnecessary detail. It is well-structured and efficient.

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

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

Without an output schema, the description explains the return format (top-N passages with character offsets and similarity scores), the character cap, internal mechanics, and how to use it in conjunction with another tool. This is comprehensive for a search tool.

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

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

Schema coverage is 100% with parameter descriptions. The description adds value by contextualizing 'text' as a fetched record, giving example queries for 'query', and explaining the default and range for 'limit'. It goes beyond the schema with usage examples and constraints.

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 does 'Semantic search INSIDE a fetched record,' with a specific verb (search) and resource (fetched record). It distinguishes itself from siblings by explaining when to use it (when record is too large) and how it pairs with ask_pipeworx_grounded.

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 guidance: 'Use when the record is too big to cram into the prompt.' It also mentions pairing with ask_pipeworx_grounded and notes the character cap and truncation behavior. However, it does not explicitly state when not to use it or list alternatives beyond the mentioned sibling.

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

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

Annotations indicate readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing the side effects (proactive monitoring, event delivery) and constraints (auth requirements, rate limits). No contradictions.

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

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

The description is well-structured, starting with the core purpose, then requirements, types, and delivery options. Each sentence adds value without redundancy. It is slightly long but appropriate for the complexity.

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

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

Given no output schema, the description explains the return value ('new subscription id') and mentions the webhook signing secret. It covers auth, type-specific params, delivery channels, and limitations. Missing explicit idempotency detail but annotations fill that 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?

Schema coverage is 100%, but the description adds significant meaning: explains each subscription type with examples, clarifies the 'params' structure, details delivery channels with usage notes. This goes beyond the schema definitions.

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

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

The description clearly states the tool's purpose: 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It uses a specific verb ('create'), identifies the resource ('subscription'), and distinguishes it from sibling tools like list_subscriptions and unsubscribe.

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

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

The description provides explicit usage context: requires Pipeworx OAuth account, lists supported subscription types with examples, and explains delivery channels and their limitations (e.g., SMS 10/day cap, phone verification needed). It doesn't explicitly state when not to use the tool, but the context is clear.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds that it returns example questions from a live catalog, and that it's an onboarding tool—no contradictions.

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

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

Well-structured and front-loaded with common queries, but somewhat lengthy (4-5 sentences). Could be slightly more concise, but every sentence adds necessary 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?

For a simple tool with one optional parameter and no output schema, the description fully specifies what the tool returns (categorized examples with tool shapes) and when to use it, leaving no 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 covers parameter with description; description adds value by listing possible topic values ('finance', 'pharma', etc.), which are not enums in the schema, providing concrete guidance 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?

Description clearly states the tool is an onboarding entry point that returns category-bucketed example questions with exact tool+argument shapes, distinguishing it from sibling tools by positioning it as the first tool to use when unfamiliar with Pipeworx.

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 this FIRST' when you don't know what Pipeworx can do, and specifies variants (call with no arguments for full spread, or with topic to focus). Does not conflict with siblings.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating no mutations. The description adds value by specifying the return fields (event name, date, home/away teams, final score) and the fixed result count of 5. No contradictions with annotations.

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

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

The description is a single, well-structured sentence that front-loads the core action and immediately conveys the key details: limit of 5, recency, and return fields. Every word adds value, 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 the tool's simplicity (one parameter, fixed return of 5 items), the description covers all essential aspects: inputs, outputs, quantity, and temporal scope. The presence of an output schema further covers the return structure, so the description is fully adequate.

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

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

Schema description coverage is 0%, so the description could add more context for the single parameter 'team_id'. It mentions 'by team_id' but does not explain the format, source, or examples beyond the schema's example. However, the parameter name is self-explanatory, and the tool's simplicity reduces the need for extensive detail.

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', the resource 'games/matches for a team', and the specific scope: the 5 most recently completed ones. It distinguishes itself from the sibling 'team_events_next' by specifying 'completed' events. The purpose is immediately clear.

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 retrieving recent completed games but does not explicitly state when to use this tool vs alternatives like 'team_events_next' or 'get_team'. No when-not or exclusion criteria are provided, leaving the agent to infer context from the tool name alone.

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

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

Annotations already indicate a safe read operation (readOnlyHint, destructiveHint=false). The description adds that it returns specific fields and limits to 5 events. However, it omits details like chronological ordering, handling of fewer events, or any pagination beyond the limit.

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, front-loaded sentence with no redundant words. It efficiently conveys the purpose, limit, and return fields.

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 presence of an output schema, the description does not need to detail return values. It covers the essential behavior for a simple read tool. Missing details like error handling or edge cases, but acceptable for this simplicity.

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 0% for the single parameter 'team_id'. The description only mentions 'by team_id' without explaining what a team_id is, its format, or how to obtain it. The example in schema provides some context but description adds minimal semantic value.

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 fetches the next 5 upcoming games for a team, with specific return fields. The title and verb 'Fetch' are precise. This differentiates from sibling 'team_events_last' which likely handles past events.

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 use for upcoming events, but does not explicitly contrast with alternatives like 'team_events_last' or other team tools. Context is clear but lacks explicit 'when to use' and 'when not to use' guidance.

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

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

Adds key context beyond annotations: deactivation vs deletion, ownership. Annotations provide idempotentHint=true and destructiveHint=false; description explains the soft-delete mechanism 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?

Two sentences, no wasted words. Action, parameter, constraints, and effects all covered concisely.

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 1 required parameter and no output schema, description fully explains purpose, behavior, and side effects.

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%, description adds minimal value beyond schema (only notes id is returned by subscribe). Adequate but not enriching.

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 action ('Cancel a subscription by id') and resource (subscription). Distinguishes from sibling subscribe/list_subscriptions by specifying ownership and soft-delete behavior.

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 ownership enforcement ('you can only cancel your own subscriptions'), guiding when to use. No explicit alternatives mentioned but sufficient context for a simple action tool.

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

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

The description goes beyond annotations by detailing the types of claims supported (company-financial), the data sources used (SEC EDGAR + XBRL), and the return structure (verdict, extracted form, actual value with citation, percent delta). Annotations already indicate safety (readOnlyHint, etc.), and the 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?

The description is slightly verbose but well-structured, front-loading the key use-case indicators ('Is it true that…' etc.). Every sentence adds value, but some repetition (e.g., multiple synonyms for claim verification) could be trimmed while retaining clarity.

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

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

Given the simple input schema (one parameter) and no output schema, the description provides thorough context: domain, sources, return format, and use-case. It effectively replaces the need for an output schema by explaining the verdict types and delta. Annotations cover safety, making the description complete 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% for the single parameter 'claim', but the description adds valuable context: it specifies the domain of acceptable claims (company-financial), provides examples of well-formed claims, and explains the overall capability. This adds meaning beyond the schema's field 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 defines the tool's purpose as natural-language claim verification against authoritative sources, specifically for company-financial claims. It uses explicit verbs like 'verify', 'confirm', 'refute' and distinguishes itself by stating it replaces multiple sequential calls, making its unique value clear compared to sibling tools.

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

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

The description states 'Use whenever the agent needs to check whether something a user said is factually correct,' but does not explicitly exclude non-financial claims or provide alternatives for those cases. The domain restriction to company-financial claims is clear, but guidance on when not to use the tool could be improved.

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