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 indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds that it returns structured answers with stable citation URIs and auto-fills arguments, providing useful context beyond annotations.

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

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

The description front-loads the key instruction 'PREFER OVER WEB SEARCH' and then lists domains and examples. It is fairly concise for the amount of information, though it could be slightly tightened.

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 meta-search tool with high complexity, the description covers purpose, usage, and output format (structured answer with citation URIs). It does not explain how results are presented (e.g., table, text), but the citation mention and examples are 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% with all parameters being aliases for 'question'. The description adds that aliases (query, q, etc.) are accepted, but this is already in the schema descriptions. Minimal added value beyond schema.

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

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

The description clearly states that the tool routes questions to verified sources for authoritative structured data, listing many specific domains. It contrasts with web search, making the purpose unambiguous.

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

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

Explicitly tells agents to prefer this over web search for factual questions and provides concrete examples. However, it doesn't differentiate from sibling tools like ask_pipeworx_grounded or deep_research, which could receive similar 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 readOnly, idempotent, not destructive. Description adds critical behavioral details: returns evidence/confidence/refusal reasons, extracts from tool result only, and outlines specific refusal categories. 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?

Four sentences, each purposeful: defines mode, explains mechanism, specifies use cases, and notes cost trade-off. Front-loaded with key phrase. Slightly verbose on refusal reasons; could be trimmed.

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, behavior, return format, refusal modes, and trade-off relative to sibling. Lacks detail on routing mechanism and example inputs, but sufficient for an agent.

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

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

Schema coverage is 100% with well-documented aliases. Description merely restates the aliases without adding deeper semantics or usage nuance. 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 identifies a specific verb-resource pair ('hallucination-resistant answer mode') and contrasts with sibling ask_pipeworx by noting extra LLM cost and suitability for high-stakes reads. It distinguishes itself effectively.

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 (quoted/cited/acted-on answers) and when to prefer ask_pipeworx (casual lookups), including a cost trade-off. Provides clear decision 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?

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds rich behavioral details: decomposes into sub-questions, parallel execution, verbatim evidence with confidence/source/fetched_at/pipeworx citation, explicit gaps[] for unanswered facets, never invented facts, and timing estimate.

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 five sentences, front-loaded with the core purpose. While it includes many details (tool count, account requirement, timing), each sentence serves a purpose. Slightly verbose but not excessive for a complex tool.

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

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

For a tool with no output schema, the description fully explains the return format (structured findings packet with evidence, gaps). It also covers prerequisites (account, paid plan) and expected latency. Given the complexity, this is very complete.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining the depth parameter values (quick=3, standard=5, thorough=8) and clarifies that question can be broad/multi-part. This extra context justifies a 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 does multi-source research in one call by decomposing questions, routing sub-questions to 3,745 tools in parallel, and returning grounded answers with evidence. It distinguishes itself from sibling tool ask_pipeworx by contrasting multi-part vs. single 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?

Explicitly states when to use: broad or multi-part questions like compare X and Y. Also gives an alternative: use ask_pipeworx for single lookups. It mentions prerequisites: Pipeworx account, paid plan for 'thorough' depth, and expected time (15-60s).

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, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is clear. The description adds only 'by ID', which is redundant with the schema. No additional behavioral traits like rate limits, response format hints, or data scope are disclosed.

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 at four words, but it under-specifies. It does not earn its place because it repeats information evident from the tool name and schema. A slightly longer description with context would be more balanced.

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 low complexity (one required parameter) and existing annotations and output schema, the description could still be more complete. It fails to explain what 'profile' includes or how this tool compares to siblings like 'search_players'. The description is insufficient for an agent to fully understand when and how to invoke it.

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

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

Schema description coverage is 0%, so the burden falls on the description. It only says 'by ID', offering no explanation of what 'player_id' represents, its format, or valid values. The example in the schema helps slightly, but the description itself 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 'Player profile by ID.' clearly states the tool retrieves a player profile given an ID. It implies the verb 'get' and the resource 'player', and the name reinforces this. However, it does not explicitly differentiate from sibling tools like 'search_players' or 'entity_profile', which may overlap.

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

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or context where 'search_players' or 'entity_profile' would be more appropriate.

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, idempotentHint, and non-destructive. The description adds minimal behavioral context beyond stating it returns a profile. 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?

Extremely concise: a single, clear sentence with no unnecessary words. All information is front-loaded and earned.

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 tool (1 param, output schema present, rich annotations), the description is nearly complete. It does not detail the output but output schema covers that. Slightly missing is an explicit note that it returns a single object, but schema implies it.

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

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

Schema provides full description for the single parameter (team_id). Description only reiterates 'by ID' without adding new meaning or usage details beyond what schema already provides.

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

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

The description 'Team profile by ID' clearly states the verb (get) and resource (team) with a specific method (by ID). It distinguishes from sibling tools like search_teams which are for searching, but does not explicitly contrast 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?

No guidance on when to use this tool versus alternatives such as search_teams or team_events_* . The description lacks any usage context or prerequisites.

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, openWorldHint=true. The description adds minimal behavioral context beyond stating the scope ('in a league'). It does not disclose whether results are paginated, sorted, or limited. Given annotations cover safety, this is acceptable but adds little 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?

The description is extremely concise at six words, which is efficient but borderline under-specified. It lacks any structural elements like usage context or parameter detail, but for a simple list tool, brevity is acceptable.

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 one parameter and an output schema (not shown), the description is minimally adequate. However, it lacks usage guidelines and parameter semantics, which are necessary for complete understanding, especially with multiple sibling tools.

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

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

With schema description coverage at 0%, the description does not elaborate on the required parameter 'league_id'. The parameter name is self-explanatory, but the description should have specified the expected format or source to aid correct invocation.

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 'All teams in a league.' clearly indicates that the tool retrieves a list of teams belonging to a specified league. While it distinguishes from sibling tools like get_team (single team) and search_teams (search-based), it does not explicitly state the action (list/retrieve), relying on the title and name for clarity.

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

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

The description provides no guidance on when to use this tool versus alternatives such as get_team, search_teams, or league_table. There are no explicit conditions, exclusions, or comparisons to siblings, leaving agents to infer 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, 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, destructiveHint false, covering safety. Description adds that it lists sports from TheSportsDB, providing source context. 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?

A single, complete sentence that conveys the purpose succinctly. No unnecessary words.

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

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

Given no parameters and an output schema (presumably documenting return format), the description is complete. It specifies the scope (all sports) and source (TheSportsDB).

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?

No parameters exist; schema coverage is 100% (none). With 0 parameters, the baseline is 4. The description doesn't need to explain 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 action (list) and resource (all sports tracked by TheSportsDB). It is specific and distinguishes from sibling tools like list_leagues.

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 need a list of all sports. No alternatives are needed as there is no other tool for this purpose. It could explicitly state when not to use, but not required.

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 are consistent (readOnly, openWorld, idempotent). Description adds behavioral details: requires one of event/topic, checks monotonicity and partition sum, Jaccard similarity filter, placeholder filtering, fill check, and response structure. 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?

Very detailed but lengthy; lacks structured formatting like bullet points. Could be more concise without losing essential 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?

No output schema, but description fully explains response fields (opportunities, partition_check, fill check) and covers edge cases (no args, low similarity, placeholder filtering). Complete for a complex arbitrage 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%, so baseline is 3. Description adds significant context: examples of event slugs and topic questions, mode behavior, and detailed checks triggered by 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: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes between event and topic modes and differentiates from sibling tools like polymarket_edges and 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?

Explicitly says when to use event vs topic mode, that no args fails, and mentions using polymarket_fill_risk for custom sizing. Does not fully compare with all siblings but provides clear 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?

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 true, and destructiveHint false, covering safety and idempotency. The description adds the search-by-name behavior but lacks details like matching algorithm, pagination, or result limits. It does not contradict 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 sentence with no superfluous words. The key information is front-loaded, making it easy to parse quickly.

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 and comprehensive annotations, the description is minimally sufficient. However, it omits details like return format (list of players), matching behavior (exact/partial), and does not leverage the examples to add context. For a simple search tool, it is adequate but not complete.

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

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

Schema coverage is 0%, so the description must compensate. It adds 'by name', which clarifies that the query parameter expects a player name. The examples reinforce this. While concise, it provides enough meaning for the single required 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 action (search), the resource (players), and the method (by name). It is specific and distinct from sibling tools like search_teams and get_player.

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 implicitly indicates usage for finding players by name, but it does not provide explicit guidance on when or when not to use this tool versus alternatives like get_player or search_teams. No exclusion criteria or context is 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?

Annotations already provide readOnlyHint, idempotentHint, non-destructive. Description adds that search is by name with partial matching, which is useful but does not disclose other behavioral traits like result ordering or pagination.

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 with no superfluous words. The key information is front-loaded and immediately actionable.

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 annotations covering safety, the description is mostly complete. The output schema exists to describe return values. Minor omission: no mention of case sensitivity or whitespace handling, but these are not critical.

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

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

Schema has 0% description coverage and only one required parameter 'query' with type string. The description adds crucial meaning: 'by name (full or partial)' clarifies what the query represents and that partial matching is supported.

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 'search' and resource 'teams', specifying matching by name (full or partial). This distinguishes it from sibling tools like get_team (exact lookup) and search_players.

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

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

No guidance on when to use this tool versus alternatives like get_team. The description implies usage for partial name searches but does not explicitly exclude use cases or provide when-not-to-use 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?

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 indicate read-only, idempotent, and non-destructive behavior. The description adds no additional behavioral context, such as ordering or potential time range. It does not contradict 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 very concise (5 words) and front-loaded, but it sacrifices necessary information. It earns a 3 for brevity but loses points for missing critical details.

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 tool (1 parameter, output schema exists, annotations provide safety), the description is barely adequate. It lacks usage guidelines and parameter semantics, making it incomplete for effective agent use.

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

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

With schema description coverage at 0%, the description should elaborate on the 'team_id' parameter. It only mentions 'team', lacking details on format or source. The minimal text does not compensate for the lack of schema documentation.

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 the last 5 events for a team, which distinguishes it from the sibling tool 'team_events_next'. However, it could be more explicit about the action (e.g., 'Retrieves').

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

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

No guidance is provided on when to use this tool vs. alternatives like 'team_events_next'. The description does not mention any prerequisites or context for use.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds the '5 event limit' which provides some 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?

Very concise single phrase, no wasted words. Could be considered slightly under-specified but efficient for a simple tool.

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

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

Given the tool has one required parameter, output schema, and rich annotations, the minimal description is adequate but doesn't explain event ordering, time frame, or return format.

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 only parameter 'team_id' has 0% schema description coverage, and the description does not clarify its meaning or format beyond the schema's example.

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 'Next 5 events' and the resource 'for a team', distinguishing it from sibling tool team_events_last.

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 explicit guidance on when to use this tool versus alternatives, though the purpose implies using it for upcoming team events.

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.