Thesportsdb

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

Annotations already mark the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds critical behavioral details beyond annotations: it returns per-model {score, confidence, signals, raw_response} plus a combined view, and explicitly states that probing Anthropic requires a BYO key and that the user pays Anthropic directly. This cost and key-handling information is valuable for the agent.

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

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

The description is a single, well-structured paragraph with two sentences. The first sentence front-loads the core action and output. Every sentence adds value: purpose, default behavior, optional key handling, return format, and use cases. 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?

Given the tool has 4 parameters (1 required), no output schema, and annotations covering safety, the description is complete. It explains the return format, default model, optional Anthropic integration with cost implications, and multiple use cases. The agent can understand the tool's full capability and constraints without ambiguity.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds concrete examples for each parameter (e.g., 'Pipeworx' for entity, 'workers-ai' for models, 'sk-ant-...' for _apiKey, 'Boston restaurant' for context), which helps the agent form correct invocations. This extra detail raises the score to 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 probes LLMs for knowledge about an entity and returns a visibility score per model. It specifies the verb 'probe', the resource 'one or more LLMs', and the output format. The description also distinguishes from sibling tools by mentioning specific use cases like 'AI-marketing audits, pre-launch brand checks, competitive monitoring', which are not covered by siblings such as 'scan_competitor_ai_presence'.

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 contexts for use: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains optional probing with Anthropic via BYO key. However, it does not explicitly state when not to use the tool or suggest alternatives among siblings, missing an opportunity for full 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 cover readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond those: it returns stable pipeworx:// citation URIs, is a fast single call, works on every tier, and can handle live news. This is value beyond the structured 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 long but front-loaded with the most critical routing guidance and includes illustrative examples that earn their place. It is more verbose than strictly necessary, but every section supports selection and invocation decisions.

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 router tool with no output schema, the description covers input format, example queries, output characteristics, when to use alternatives, and even warns about breaking-news coverage. Nothing essential for a correct invocation is missing.

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

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

Schema description coverage is 100%, so all six parameters are already documented. The description adds example questions and clarifies that the tool accepts natural-language requests, but it does not add parameter-level semantics beyond the schema. Baseline 3 is appropriate.

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

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

The description names a specific verb (routes), a resource (5,724 tools across 1,497 verified sources), and a clear output (structured answer with citation URIs). It differentiates itself from siblings by framing ask_pipeworx as the default entry point while naming ask_pipeworx_grounded and deep_research as alternatives.

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

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

It explicitly says 'PREFER OVER WEB SEARCH' and states when to step up to ask_pipeworx_grounded or deep_research. It also gives concrete trigger phrases and examples, making the when-to-use decision nearly unambiguous.

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 read-only, idempotent, and non-destructive. The description adds meaningful behavioral detail beyond those annotations: it is a full working router, not a fallback, candidate routing improvements may be live, and as of 2026-07-26 no candidate is active, so it currently matches ask_pipeworx exactly. It also highlights that results are compared against the stable router to decide merges.

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 compact yet information-dense. Every sentence earns its place: the first establishes identity and scope, the second clarifies the current no-candidate state, the third gives direct usage guidance, and the fourth removes ambiguity about fallback behavior. Highly front-loaded and free of 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?

The description is sufficient for an agent to know this is a usable, fully working router, how to call it, and what its relationship to ask_pipeworx is. It leaves the exact response shape unspecified, relying on the promise that it matches ask_pipeworx; since there is no output schema and the description is not fully self-contained on return format, this is a minor but real completeness 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%, with all six parameters documented, including the five aliases of question. The description adds only 'same arguments as ask_pipeworx,' which does not deepen or clarify parameter semantics beyond what the schema already provides. This is the expected baseline when the schema fully carries parameter 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 states a clear purpose: this is the beta version of the ask_pipeworx universal router, with the same 5,724 tools, arguments, and response shape. It explicitly differentiates itself from the stable sibling ask_pipeworx by framing itself as the experimental edge with candidate routing improvements.

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

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

It gives an explicit usage instruction: 'Use it exactly like ask_pipeworx when you want the newest routing.' It also names the stable router as the comparison point for routing decisions. However, it stops short of explicitly stating when not to use it (e.g., when production stability is required), leaving that exclusion implied rather than stated.

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, so the baseline is lowered, but the description adds substantial behavioral context beyond that: it discloses an extra LLM call cost, the full set of refusal_reason values, and that evidence is a verbatim quote. There is 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?

Despite being dense, the description is well-structured and front-loaded: the core purpose, routing behavior, return contract, refusal reasons, and usage guidance each earn their place. The extra statistics about source count and the cost note are directly relevant to an agent deciding whether to invoke this 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 documents the success return shape, the failure/refusal shape, possible refusal_reason values, routing behavior, and the cost trade-off versus the sibling. An agent has enough information to call it correctly and interpret its result.

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

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

Schema coverage is 100% and the schema already documents that question is the natural-language input and that q, text, input, query, and prompt are aliases. The description does not add parameter-level detail, so the baseline 3 is appropriate.

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

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

The description names a specific verb and resource: 'Hallucination-resistant answer mode for high-stakes reads,' and clearly differentiates it from ask_pipeworx by emphasizing extraction limited to the tool result. It also specifies the exact return shape and refusal semantics, leaving no ambiguity about what the tool does.

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

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

It explicitly states when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when to prefer the sibling ask_pipeworx ('prefer ask_pipeworx for casual lookups'). It also notes the extra LLM call cost, giving the agent a concrete trade-off for decision-making.

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 discloses many behavioral traits: low-confidence short-circuits, closed market handling, wide-spread warnings, resolution-rule risk, fallback mechanisms for news, and more. Annotations indicate read-only, idempotent, non-destructive, which match the description. 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?

The description is long but well-structured: starts with a one-sentence summary, then details classifiers, fan-out examples, response shapes, resolver contract, etc. Every section adds necessary detail for correct usage. Slightly verbose but justified by 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 the tool's complexity (resolver, classifiers, fan-out, multiple response fields, safety mechanisms) and lack of output schema, the description is remarkably complete. It covers all response shapes, fallback behaviors, and edge cases, enabling an agent to use the tool correctly and interpret results.

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 good descriptions. The description adds value by explaining market input flexibility (slug/URL/text), depth behavior (quick vs thorough), and include_raw effect (summary vs full payload). This enriches understanding beyond the schema.

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

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

The description clearly states that the tool researches Polymarket bets by pulling Pipeworx data. It specifies the input (slug, URL, or question text) and output (evidence packet + market-vs-model comparison). It distinguishes itself from siblings like polymarket_edges by focusing on a single bet with deep evidence.

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

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

Explicit use cases are given: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It also advises to inspect market_match fields before trusting analysis. However, it does not explicitly mention when not to use it or alternatives for multi-bet comparisons.

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

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

Discloses data sources (SEC EDGAR/XBRL for companies, FAERS etc. for drugs), fiscal year handling, sorting by primary metric, and citation URIs. All beyond annotations which only indicate read-only open world.

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 clear; every sentence adds value. Could trim some example triggers but still effective.

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

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

Covers output (paired data, citation URIs), behavior (sorted by primary metric), and edge cases (off-calendar fiscal years). Without output schema, this is sufficient for a comparison 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 meaning beyond schema: explains 'values' format (tickers/CIKs vs drug names), constraints (2–5 items), and sorting behavior. Schema coverage is 100% but description adds valuable context.

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

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

Description specifies verb 'compare' with resource 'entities' (companies/drugs) and distinguishes from sequential lookups. It lists natural language triggers and clearly states it handles 2–5 items in parallel.

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

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

Explicit preference over sequential lookups when comparing. Provides trigger phrases. Does not explicitly exclude single-entity use (handled by sibling entity_profile), but context is clear.

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

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

Annotations convey readOnly/openWorld/idempotent/non-destructive, but the description adds substantial behavioral context beyond those flags: account and paid-plan requirements, structured-data-only grounding with gaps[] never invented, parallel routing across 5,724 tools, hop/recovery behavior by depth, contradictions[] for standard/thorough, citation_uri fetchability guarantees, semantic excerpting, and latency expectations. No contradiction with the annotations exists.

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 long but every sentence carries decision-relevant information, and the most actionable constraints (account required, alternative tool, not open-web) are front-loaded. It is somewhat dense and stream-of-consciousness in places, but it earns its length given the tool's complexity and the lack of an output schema.

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 no output schema, the description fully documents the return semantics: findings packet fields, gaps[], contradictions[], hop field, citation_uri guarantees, and latency. It also covers auth prerequisites, tier differences, and sibling routing. For a complex research tool this is as complete as an agent needs to select and invoke it 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 the baseline is 3, but the description adds meaningful depth semantics: it explains what depth:'standard' does (gap recovery), what depth:'thorough' additionally does (chases leads + contradictions scan), and that depth:'thorough' is paid. It also clarifies that 'question' can be broad/multi-part because decomposition is the point. This goes beyond the schema's enum 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 states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1497 STRUCTURED data sources' that 'decomposes your question into focused facets' and routes them in parallel. It explicitly differentiates itself from open-web search and from ask_pipeworx, and clarifies it is for broad/multi-part structured-data questions. This is far beyond a tautology and gives an agent a precise mental model.

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

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

The description gives explicit when-to-use and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data', 'For a single lookup use ask_pipeworx', and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. It also names the exact alternative tool (ask_pipeworx) and adds an account/tier precondition with a fallback if not signed in. This leaves little to inference.

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, destructiveHint. Description adds return format: '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?

Description is somewhat long but front-loaded with key info. Well-structured, though could be slightly more concise.

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

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

No output schema, but description thoroughly explains return value: top-N tools with full details ready to call. Covers aliases, limits, and purpose.

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 clarifies that query, q, task, search, description are aliases, and provides example inputs. 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?

Clearly states the verb 'find tools' and the resource is tools. Distinguishes from siblings by being the discovery/browse tool, and lists many domains.

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 when-to-use and 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 read-only, idempotent, open-world. The description adds valuable behavioral context: fans out across multiple sources, returns specific data fields, mentions USPTO sunset soft-fail, and notes names not supported. This goes beyond annotations without contradicting them.

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

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

The description is dense but well-organized, front-loading examples and purpose. It could benefit from slight structural formatting (e.g., bullet list of return fields), but remains concise and informative.

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 comprehensively lists return fields (cik, filings, fundamentals, patents, news, LEI) and notes failure modes (USPTO sunset, name not supported). This fully equips an AI agent to understand and invoke 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% with clear descriptions. The description adds concrete examples (ticker 'AAPL', CIK '0000320193') and reinforces the unsupported name constraint, enhancing usability.

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 provides a full cross-source profile of US public companies, with concrete examples ('Tell me about X', 'company profile for Microsoft') and explicitly distinguishes from chaining individual lookups. It also references sibling tool resolve_entity for name resolution.

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 when to use ('ALWAYS PREFER over chaining...when the user asks for a holistic view') and when not to ('names not supported – use resolve_entity first'). This is excellent 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?

Description is consistent with annotations (readOnlyHint, idempotentHint). While it doesn't add new safety or behavioral details beyond the annotations, it enriches context by specifying the sports domain and use cases.

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?

Exceptionally concise: two sentences plus a list of examples. Front-loaded with the main purpose, no fluff. Every sentence earns its place.

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

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

Given the tool's low complexity, full schema coverage, and presence of output schema, the description is complete. It covers purpose, usage guidelines, and parameter hints without needing to explain return values.

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

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

Schema coverage is 100%, baseline 3. The description adds value by providing common sport filter values (e.g., 'Ice Hockey' for NHL) and clarifying that sport and league are optional, which helps agents form correct inputs.

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 returns sports schedules/fixtures for a given date, optionally filtered by sport or league. It distinguishes itself from sibling tools like team_events_last or get_team by focusing on date-based listings.

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 preferring over web search for common queries like 'what games are on today' or 'NBA games tonight'. Gives practical tips like passing two dates for 'next 24h' and provides sport/league examples.

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 destructive and idempotent behavior. Description adds context about clearing sensitive data and pairing, but omits details on return values or error handling.

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

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

Two sentences that are direct and efficient. No filler, every sentence adds value.

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

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

For a tool with one parameter and no output schema, the description is complete: it explains purpose, usage context, and related tools. No missing information for typical use.

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

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

Schema covers the single parameter 'key' fully (100% coverage). Description adds no additional detail beyond what the schema provides, meeting the baseline.

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

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

Clear verb ('Delete'), specific resource ('previously stored memory'), and key-based deletion. Differentiates from siblings by mentioning pairing with 'remember' and 'recall'.

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 conditions for use: stale context, task completion, or clearing sensitive data. Does not explicitly state when not to use, but the positive cases are sufficient for this simple tool.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context: it fetches the page, extracts title/description/key links, and outputs standard llms.txt format. This goes 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 concise: two sentences plus a bullet list of use cases. It front-loads the core action and benefit, with no wasted words.

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

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

The description covers the main purpose, process, output format, and use cases. It lacks details on error handling or failure scenarios, but for a straightforward read-only tool with no output schema, it is largely 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 the description doesn't need to add much. It mentions the output format and use, but no additional parameter-specific details beyond the 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?

The description clearly states it generates a production-ready llms.txt file for any URL, with a specific verb and resource. It distinguishes itself from siblings like ai_visibility_check by focusing on file generation rather than visibility checking.

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

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

Explicitly provides use cases (getting a client's site indexed, drafting your own, auditing competitors). However, it does not explicitly state when not to use it or contrast with sibling tools, so it's slightly less than a perfect score.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds that the profile is 'full' and lists return fields but does not provide additional behavioral context (e.g., rate limits, caching, error behavior). It conforms to annotations 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 a single, well-structured sentence that front-loads the tool's purpose and key identifier. Every word is necessary, and no information is repeated from the schema or annotations.

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 (1 parameter, no nested objects, output schema exists), the description covers the essential return fields and the input format. While it omits potential error cases or alternative usage, it is sufficiently complete for a straightforward lookup 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?

The single parameter player_id is described as 'numeric' despite the schema typing it as a string. This adds meaningful constraint beyond the schema, which has 0% description coverage. The description helps agents understand the required format.

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

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

The description clearly states it retrieves a 'Full player profile' using a 'numeric player_id' and lists the returned fields (name, team, nationality, position, birth date, description, thumbnail). This specific verb+resource combination distinguishes it from sibling tools like search_players (search by name) or get_team.

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 by providing the player_id, but does not explicitly state when to use this tool versus alternatives (e.g., search_players, entity_profile). No exclusions or prerequisites are mentioned, leaving the agent to infer the context from the description alone.

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

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

Annotations already declare readOnlyHint and destructiveHint false, ensuring safe read. Description adds value by listing the specific return fields (name, sport, country, etc.), providing 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?

Single sentence that is front-loaded and clear. No wasted words; every element earns its place.

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

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

For a simple tool with one parameter and an existing output schema, the description is complete. It lists return fields, and the output schema handles the structure.

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

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

Schema covers 100% of parameters with description 'TheSportsDB team id'. Description repeats this and adds 'numeric', but adds minimal new meaning. Baseline 3 is appropriate given high schema coverage.

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

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

Description clearly states it retrieves a full team profile by numeric team_id and lists specific return fields. Distinguishes from sibling tools like search_teams (which searches by name) and league_teams (gets teams in a league).

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 implies usage when you have a numeric team_id. While it does not explicitly state when not to use or name alternatives, the context of sibling tools provides clarity. A brief note about alternatives would improve it.

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, so the safe, nondestructive behavior is clear. The description adds minimal context ('standings table') but does not disclose pagination, ordering, or handling of missing data. With strong annotations, the additional value is limited but acceptable.

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, front-loaded sentence that directly states what the tool does. No unnecessary words or redundancy.

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

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

Given the simple purpose, an output schema (not shown) likely describes the return structure, so the description does not need to cover that. However, it misses default behavior (current season) and edge cases. It is adequate but not thorough.

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 50% (only 'season' has a description). The tool description does not elaborate on either parameter. It does not explain what 'league_id' expects or how 'season' should be formatted beyond the example. This fails to compensate for the missing 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?

The description 'Standings table for a league/season' clearly states the tool returns a standings table for a given league and season. The verb is implicit but understandable. It distinguishes from sibling tools like 'league_teams' or 'get_team' by specifying 'standings' vs. team-level data.

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

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

No guidance is provided on when to use this tool versus alternatives. For example, it does not mention when to prefer this over 'league_teams' or 'search_teams'. The agent is left to infer the 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, openWorldHint, idempotentHint, and destructiveHint=false, covering safety and mutation. The description adds no behavioral details beyond what's in the schema (e.g., return fields). With rich annotations, the description adds minimal 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 a single, concise sentence that immediately states the core functionality and output. No extraneous words or repetition.

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

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

For a simple list tool with one parameter, rich annotations, and an output schema, the description covers the essential purpose and output fields. It does not mention pagination or limits, but the open world hint and tool nature imply all teams are returned.

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

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

Schema description coverage is 0%, and the description does not explain the 'league_id' parameter's meaning, format, or source. The schema only shows a type and example. The description fails to add semantic context for the single 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 verb 'list', the resource 'teams', the scope 'by league_id', and the return fields (id, name, badge, country). It distinguishes the tool from siblings like 'get_team' (single team) and 'search_teams' (search).

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

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

Usage is implied for listing teams by league, but no explicit when-to-use, when-not-to-use, or alternatives are given. While straightforward, it lacks guidance on edge cases or when to prefer this over 'search_teams' or team-specific 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, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds context about the data source, return fields, and that the ID is used by other tools, which goes beyond what the annotations provide.

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, front-loaded with the action and parameters, then return values and downstream use. No redundant information; every sentence earns its place.

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

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

Given the tool's simplicity, the description covers purpose, filtering, output, and usage context. The output schema exists, so return structure is handled elsewhere, and the description is complete for an agent to select and 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?

The input schema already has descriptions for sport and country, with 100% coverage. The description reinforces that both are optional and provides example sport values, clarifying the narrowing behavior and adding 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 uses a specific verb ('List') with a clear resource ('sports leagues catalogued by TheSportsDB') and explains filtering options. It also distinguishes itself by noting the league id feeds league_teams and league_table, setting it apart from sibling tools like list_sports.

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

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

It states the tool answers which leagues exist for a sport or country and provides the league id for downstream tools, giving clear context on when to use it. However, it does not explicitly mention when not to use it or compare with alternatives such as list_sports.

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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds the specific return fields (id, name, forum), which is useful but not extensive. No behavioral contradictions. Given annotation coverage, a score of 3 is appropriate.

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 concise sentence that front-loads the action and includes key details (examples, output fields). Every word earns its place; 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 simplicity (no parameters, full annotation coverage, output schema exists), the description is complete. It covers what the tool does and what it returns, leaving no significant gaps.

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

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

The tool has 0 parameters and schema description coverage is 100%, so the description does not need to explain parameters. Per guidelines, 0 parameters yields a baseline of 4. The description adds value by describing the output, but that is not parameter semantics.

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 'Return' and the resource 'all sports tracked by TheSportsDB', with examples (Soccer, Ice Hockey, Basketball) and specifies output fields (id, name, forum). This distinguishes it from siblings like list_leagues, which also lists entities but of a different type.

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 tool is simple and parameterless, so usage is obvious. The description gives clear context: it returns all sports. It does not explicitly state when not to use it or mention alternatives, but the sibling context is sufficient for an agent to differentiate.

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. The description adds value by detailing return fields (id, type, params, timestamps, count), enriching agent understanding.

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 fluff. First covers purpose and output, second gives usage guidance. 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?

Despite no output schema, the description lists all return fields. Usage and parameter are well-covered. Complete for a list 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?

Only one parameter with 100% schema coverage. Description doesn't add extra meaning beyond schema, meeting baseline.

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

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

The description clearly states the tool lists subscriptions and enumerates return fields. It distinguishes from siblings like subscribe/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 says when to use: 'review what you're monitoring before adding more or to find an id to cancel.' 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?

Beyond the annotations, the description discloses the two-phase claim_token flow, rate-limiting ('5 per identifier per day'), quota impact ('doesn't count against your tool-call quota'), and human review cadence ('digests daily'). These are non-obvious behaviors that significantly aid an agent in predicting outcomes.

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 longer than average but every sentence serves a purpose: it defines the tool's scope, gives examples, warns about exclusions, explains the claim_token flow, and states limits. The structure progresses from 'what' to 'when' to 'how', and it is fully front-loaded with the core purpose.

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 a nested context object, no output schema, and a multi-step claim_token workflow, the description is exceptionally complete. It covers all major aspects: intended use, what not to use it for, the return token, rate limits, and roadmap impact. The agent is well-equipped to decide when and how to invoke this tool.

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

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

Although schema coverage is 100% and each parameter already has a description, the tool description adds valuable semantics: it explains how claim_token is used to retrieve resolution status ('pass it back later... to read whether it was fixed'), and instructs users to describe issues in terms of Pipeworx tools/packs rather than pasting prompts. This goes beyond what the 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?

The description opens with a specific verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly differentiates itself from sibling tools like ask_pipeworx or discover_tools by framing itself as the channel for reporting issues, gaps, or praise about Pipeworx tools.

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

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

The description provides explicit when-to-use scenarios: bug, feature/data_gap, praise. It also gives an exclusion rule: only for tools served by this Pipeworx connection, and directs users to file issues with other MCP servers. The line 'Not sure? Pipeworx tool names are the ones this connection lists' offers a practical disambiguation.

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 non-destructive. Description adds valuable context: data source (CF analytics-engine), no PII, and caching duration (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?

Five sentences, front-loaded with main purpose, use cases, and behavioral notes. No redundant information; each sentence adds value.

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

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

For a simple read-only tool with 1 optional parameter and no output schema, the description fully explains what it returns, why to use it, data source, and caching. Complete for its 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 covers the single parameter fully (100% coverage) with clear enum and description. Tool description mentions window options but adds no new semantics beyond schema. Baseline 3 is appropriate.

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

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

Clear verb 'Returns' with specific resource: top tools, packs, and call volume. Distinct from sibling tools like discover_tools by focusing on trending data from other AI 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 sources, confirming canonical tools, aligning use case). Caching behavior noted. Could be improved by stating when not to use, but the use cases effectively guide appropriate usage.

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

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

Annotations indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds substantial behavioral context: internal checks (semantic anchor, partition filter, fill check), constraints on modes, and edge cases like placeholder slugs. No contradictions with annotations.

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

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

The description is well-structured and front-loaded with the core purpose. While somewhat lengthy, every section adds value and clarifies complex behavior. Minor redundancy (e.g., repeating modes) but overall efficient for a complex tool.

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

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

Given the tool's complexity (multiple modes, internal checks, fill validation), the description is thoroughly complete. It explains response structure without an output schema, covers edge cases (placeholder slugs, similarity thresholds), and provides actionable guidance. No gaps identified.

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema. It explains each parameter's purpose (event slug, topic seed question), provides examples, and describes the internal logic (partition_check, Jaccard similarity, fill check). This exceeds mere 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 the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes between three modes (no args, event, topic) with specific explanations for each, making the purpose unambiguous and differentiating it from 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 provides explicit guidance on when to use each mode: no args for trending scan, event for a specific market, topic for cross-event scanning. It also includes warnings about not trading when realizable_edge_pp ≤ 0 and directs users to polymarket_fill_risk for custom sizing, offering clear usage boundaries.

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 declare read-only, idempotent, and non-destructive, and the description adds extensive context: 1h cache keyed on knobs, model families, filtering logic, diagnostics for empty segments. 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 quite lengthy with many technical specifics (e.g., model families, Kelly formulas). While front-loaded with purpose, it could be more concise for an AI agent. Structure is somewhat rambling.

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 having 9 parameters and no output schema, the description fully explains the response structure (by_segment, diagnostics), caching behavior, and edge conditions. Callers can understand what to expect.

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 detailed descriptions. The description adds value by explaining how knobs interact (e.g., 'min_partition_leg_kelly applies to per-leg Kelly inside top_legs') and the overall ranking/filtering purpose, going 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 uses specific verb 'Scan top Polymarket markets' and clearly states the resource and outcome. Differentiates from siblings by focusing on Pipeworx data disagreement and providing detailed segmentation.

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 explicitly states 'Built for 'what should I bet on today'' and explains how knobs like min_liquidity, max_spread_pp filter unrealizable edges. While it lacks explicit when-not-to-use, the context is clear from sibling tool names.

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 behavior. The description adds valuable behavioral details: return structure, trend computation, decay_pp_per_day, snapshot gaps on cache-miss, and TTL bounds. 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 detailed and front-loaded with purpose, but slightly long. Each sentence provides value, though some minor restructuring could improve readability.

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

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

Despite no output schema, the description fully explains the return structure (tracked, expired, snapshot_dates) and covers edge cases (gaps, TTL, decay calculation). With simple parameters and strong annotations, it is 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%, yet the description adds meaning beyond defaults and clamping: explains the effect of 'days' (lookback) and 'window' (snapshot family), including the possible values and their impact.

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: edge persistence and decay telemetry from daily snapshots. It distinguishes from sibling tools (e.g., polymarket_edges) by focusing on time-series analysis of edge persistence.

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

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

Provides context on interpreting edges (fresh vs. old) and mentions limits (60-day TTL, not intraday). However, it does not explicitly compare to alternatives like polymarket_edges for when to use each.

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. Description adds significant behavioral context: it walks the ladder, returns specific fields (top_of_book, vwap_fill_price, slippage_pp, etc.), explains basket mechanics, and cautions about forced directional risk. 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 with information but front-loaded with the core purpose and mode breakdown. While slightly verbose, every sentence earns its place. Capitalization and bullet-like formatting aid readability. Minor room for tighter phrasing.

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 4 parameters, no output schema, and complex dual-mode behavior, the description thoroughly covers both modes, required parameters, defaults, output fields, and risk scenarios. It leaves no critical ambiguity for correct usage.

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

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

Schema description coverage is 100%, but the description adds essential meaning: clarifies size_usd interpretation differs per mode, side auto-detection in basket mode, and that one of market or event is required. Also provides defaults and clamp ranges, going beyond schema.

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

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

The description clearly states the tool performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth', distinguishing between single-market and basket modes. It explicitly relates to sibling tools like polymarket_arbitrage and polymarket_edges, making its unique purpose evident.

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 instructions: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Also warns about theoretical overround not being capturable and partial basket fill risks, guiding against misuse.

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 readOnly/openWorld/idempotent context, and the description adds extensive behavioral detail without contradiction: gross spreads because Kalshi fees are not modeled, pairing_unverified on all returned pairs, compatibility codes, skipped/unclassified leg handling, and the meaning of null temporal_alignment. This goes far beyond what annotations convey and prevents misinterpretation of results.

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 long, but it is deliberately structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS) and front-loaded purpose. Each sentence carries operational value given the tool's complexity, and there is little repetition of schema 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 no output schema, the description must explain return semantics, and it does thoroughly: leg prices, spread.top_spreads_pp, compatibility_warning, compatibility_codes[], fees_note, skipped counters, and per-entry flags. It also covers tricky cases like unclassified legs never being paired and temporal_alignment null, giving an agent everything needed 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?

The input schema already documents all three parameters with 100% coverage, so the baseline is 3. The description adds meaningful parameter semantics: topic values auto-fetch matching events, explicit parameters override the mapped side, and both modes run the identical token-overlap matcher. This interplay is not implied by 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 opens with a precise verb phrase: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' identifying exactly what is computed and on which resources. The two-mode explanation further clarifies scope without ambiguity, and the explicit naming of both venues distinguishes this from sibling tools like polymarket_arbitrage.

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

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

The description clearly explains when to use each mode: pre-mapped topic shortcuts for known macro events, or explicit kalshi_event_ticker + polymarket_event_slug for custom pairings. It also warns that most pre-mapped topics return compatibility_warning today, telling an agent not to assume a shortcut is tradeable. It does not mention alternative sibling tools, so it stops short of full 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 indicate readOnly, idempotent, no destruction. Description adds behavioral details: listing all keys when omitted, scoping to identifier, and pairing with remember/forget.

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 front-loads core purpose. Each 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?

For a simple tool with full annotations, the description covers purpose, usage, behavior, parameter semantics, and pairing with sibling tools. 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%, description adds meaning by explaining omission behavior (list all keys) and scoping, going beyond the schema's single parameter 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?

Clearly states the verb 'retrieve' and resource 'saved values', distinguishes from siblings by mentioning pairing with remember and forget. Specific usage scenarios are described.

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

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

Explicitly tells when to use: 'look up context the agent stored earlier without re-deriving it from scratch'. Does not explicitly list when not to use, but context is clear.

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

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

Description contradicts annotations. Annotations declare readOnlyHint=true, but the description states that setting mark_read:true flags events as read, which is a mutating side effect. This creates confusion about whether the tool is read-only.

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 informative and well-structured, with a clear first sentence stating purpose followed by details on returned fields, filtering, and behavior. Slightly verbose but each 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?

While the description covers parameters, return fields, and usage tips, the annotation contradiction undermines completeness by providing inconsistent information about mutability. Without output schema, the description should be more reliable.

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 5 parameters are described in the schema (100% coverage). The description adds value by providing examples ('sec_8k'), explaining the mark_read effect, and detailing the return fields, enhancing understanding beyond the schema.

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

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

The description clearly states the tool pulls fired events from a subscription feed and returns the most recent alerts, specifying the verb, resource, and scope. It distinguishes from sibling tools that are unrelated to alert feeds.

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 mentions filtering by type and since, and notes that polling works fine, but does not explicitly state when to use this tool versus alternatives among siblings (e.g., events_by_day). No exclusions or when-not-to-use guidance is provided.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable detail about multi-source fan-out, fallback logic (GDELT→GNews), and soft-failure for USPTO, which goes 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 comprehensive but slightly verbose with multiple example queries. It is well-structured and front-loaded with purpose, but could be trimmed without losing 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?

The tool is complex with multiple sources and fallback logic. The description fully explains behavior, parameters, and return structure (grouped changes, total count, citation URIs). No output schema, but description provides sufficient info.

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. The description adds meaning by explaining 'since' accepts ISO or relative formats and suggests '30d'/'1m', and that 'value' can be ticker or CIK. This enhances 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 provides a 'change feed for a company in the last N days/weeks/months'. It gives numerous example queries and explicitly distinguishes itself 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 includes explicit usage context ('What's new with X', 'latest on Y') and an alternative ('Use entity_profile instead when you want the static profile'). It also advises on typical 'since' values.

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

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

Annotations already indicate readOnlyHint=false, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: data is scoped by identifier, persistent for authenticated users, and 24-hour retention for anonymous sessions. 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?

Three sentences, front-loaded with the primary purpose. No fluff; every sentence adds critical information. Highly 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 no output schema, the description could mention the return value (e.g., success indication). However, it adequately covers persistence, scope, and pairing with other tools. It might also clarify behavior on overwrite, but idempotentHint suggests idempotent.

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 key and value. The description adds semantic value by providing example key patterns (e.g., 'subject_property', 'target_ticker') and value types (findings, addresses, preferences, notes), enhancing understanding beyond the schema.

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

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

The description uses clear verbs ('Save data the agent will need to reuse later') and specifies the resource (key-value pair). It distinguishes from sibling tools recall and forget by mentioning them, making the tool's purpose distinct.

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 when you discover something worth carrying forward' and provides concrete examples (resolved ticker, target address, etc.). It also mentions pairing with recall and forget, guiding the agent on 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?

Beyond the read-only/idempotent/non-destructive annotations, the description discloses important runtime behaviors: graceful degradation when GLEIF/OpenFIGI is unavailable, ambiguity handling via figi_candidates, explicit unresolved identifiers rather than silent omission, source labelling for every identifier, and internal cascading through multiple lookup endpoints. This is unusually 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 front-loaded with a practical purpose statement and example phrasings, and the type-specific details are organized under clear labels. It is long and dense enough that some parenthetical explanations could be trimmed, but the complexity of the tool justifies most of the length.

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 no output schema, the description still explains what callers can expect: source-labelled identifiers, an unresolved list, figi_candidates on ambiguity, and degraded enrichment behavior. Combined with parameter-level instructions and annotations, there is no critical missing information an agent would need to call and interpret this 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%, but the description adds substantial meaning beyond the schema: accepted input forms (ticker, CIK, ISIN, name), the ISIN-to-LEI mapping behavior, the distinction between brand and generic drug names, and the crucial warning to pass only the entity name and not trailing security-class words. This materially improves 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 names a specific action—resolve a user-spoken name to canonical/official identifiers—and clearly delimits what it covers (company: CIK/ticker/LEI/FIGI; drug: RxCUI). It positions the tool as the prerequisite for other tools and differentiates it from siblings like entity_profile by focusing on name-to-ID resolution rather than profile lookup.

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

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

It explicitly says 'Use FIRST whenever you have a name but need an ID,' giving clear selection guidance and even explaining that one call replaces 2-3 manual lookups. It does not, however, name specific alternatives or state when not to use the tool beyond the implicit name-to-ID condition, so it falls just short of a perfect exclusionary usage guide.

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 behavior. The description adds context that the tool internally calls ai_visibility_check for each entity, returns scores, confidence, and signal density, and requires an Anthropic API key if probing with that model. No contradictions with annotations, and the additional behavioral detail is valuable beyond structured hints.

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 two sentences totaling ~50 words, with no filler. It front-loads the primary action, then details the process and output. Every sentence adds value: purpose, mechanism, use case, and return format.

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

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

The tool has 4 parameters and no output schema; the description explains the output format (ranked list with score, confidence, signal density). It does not mention the 2-8 entity constraint (already in schema) or error handling. However, given the annotations and schema richness, the description is sufficiently complete for an AI agent to understand how to 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 coverage is 100% with descriptions for all 4 parameters. The description adds extra meaning beyond the schema: it explains that the first entry in 'entities' is treated as the 'subject' for narrative purposes, and clarifies that 'models' can be omitted for just workers-ai. This enhances understanding without repeating schema content.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a concrete use case example ('does Claude know about us as well as our competitors?') and distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic 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 explicitly says the tool is useful for competitive AI-marketing audits and gives a sample question. It implies when to use (comparing multiple entities) but does not explicitly state when not to use it or list alternative tools for single-entity checks. The context is clear but lacks explicit exclusions.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds behavioral details: fans out to two services, returns summary and per-advisory detail, and notes potential 5-30s delay on bundlephobia first measurement. 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 relatively long but all sentences add value (use case, return fields, partial failure behavior). It is front-loaded with the main purpose.

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

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

No output schema exists, but the description fully explains the return format (summary block fields, per-advisory detail, alternative versions) and error behavior (sources_failed list). This is sufficient for a read-only composite 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 100% – both parameters are described in the schema. The description only reiterates schema info (scoped packages, version default) without adding new semantic meaning.

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 is a composite check for evaluating npm packages using deps.dev and bundlephobia. It distinguishes itself from sibling tools (e.g., scan_competitor_ai_presence) by focusing on package quality assessment.

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 an agent asks about safety, popularity, size, or cost of adding a package. Notes NPM-only scope and mentions partial failures, but does not exclude specific 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 readOnly, idempotent, and non-destructive behavior. The description adds that it returns specific fields (id, current team, etc.), which is useful but not critical given the 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 waste. First sentence states purpose and return, second gives usage guidance. 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 the tool's simplicity and the presence of annotations and an output schema, the description covers the essential: what it searches, what returns, and when to use a sibling. Could mention pagination or match behavior, but overall adequate.

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

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

Schema coverage is 0%, so description must compensate. It does so by indicating the 'query' parameter is a player name, adding the semantic connection that the schema alone lacks.

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

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

The description clearly states it searches for players by name, using a specific verb and resource. It distinguishes from sibling tools like search_teams and get_player by specifying it's a search by name.

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

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

Provides explicit guidance to use search_teams first if team IDs are needed, implying context for usage. Lacks explicit alternative comparisons but gives reasonable usage context for a simple search tool.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false. The description adds value by explaining the return payload (team id, sport, league, country, stadium, description) and the scope (worldwide leagues, lower divisions). It also provides usage context that the openWorldHint alone does not, such as the specific sports covered. This is more than 3 but not quite 5 because it doesn't mention rate limits or behavior for no results.

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

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

The description is a single, front-loaded sentence that immediately states the core action 'Search TheSportsDB for sports teams by name', then efficiently adds scope, examples, and return values in a compact clause. Every phrase 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 simple parameter (single string query), the existence of annotations (readOnlyHint, idempotentHint, openWorldHint) and an output schema, the description covers the key missing details: what the tool searches (sports teams by name), its scope (worldwide leagues, multiple sports), and what information is returned. It is complete for the tool's complexity but could briefly mention that results are limited or that further lookups are needed via team_events_next/team_events_last.

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

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

The schema provides 0% description coverage (no parameter descriptions) and just has an 'examples' array with two examples. The description compensates by explaining that 'query' is a team name and listing the type of data returned, which helps the agent understand what search term to provide. With only one parameter and no enums, the agent can infer most semantics from the examples and tool name, so the description adds meaningful context 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 verb 'search' and resource 'sports teams', with specific examples of team types (football/soccer clubs, NBA, NFL, MLB) and the scope (any league worldwide including lower divisions). It also lists what the tool returns, distinguishing it from similar tools like 'get_team' (which likely returns details for a specific team ID) 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?

The description explicitly names related tools for next steps (team_events_next, team_events_last) and clearly states the tool is for searching by name. It implies when to use this tool (when you need to find a team's ID, sport, league, country, etc.) but does not explicitly state when not to use it or mention alternatives among siblings like 'get_team' or 'league_teams'.

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 etc. Description adds significant behavioral details: character offsets, similarity scores, truncation at 200K chars with flag, embedding model, window size. 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?

Two well-structured paragraphs, front-loaded with main purpose. Every sentence adds value; no redundant or vague statements.

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 explains return format (top-N passages with offsets and scores) and truncation behavior. All relevant aspects covered 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 good descriptions. Description further clarifies 'text' max chars, 'query' examples, 'limit' default. Adds practical guidance 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 'Semantic search INSIDE a fetched record' with specific verb and resource. Examples (SEC 10-K, article) and differentiation from sibling ask_pipeworx_grounded make 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 says 'Use when the record is too big to cram into the prompt', describes pairing with ask_pipeworx_grounded, and explains trade-offs (saves context, returns only relevant passages).

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 substantial behavioral context beyond annotations: OAuth requirement, delivery channel details (SMS cap, phone verification, webhook signing), and that anonymous users cannot persist subscriptions. No contradictions with annotations.

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

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

The description is well-structured with the purpose first, then types, then delivery. It is somewhat lengthy but each sentence adds value. Slightly verbose 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?

The description covers the return value, subscription types, parameters, and delivery channels. It lacks explanation of what to do with the subscription ID (e.g., for unsubscribing) but sibling tools cover that. No output schema, so return value details are 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?

The input schema has 100% coverage, but the description greatly expands on each parameter with concrete examples and edge-case details (e.g., sponsor or condition required for clinical_trial, webhook signing secret).

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 creates a proactive monitoring subscription to a live-data event stream and returns a new subscription ID. It distinguishes 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 explains when to use the tool for live-data monitoring and provides type-specific examples. However, it does not explicitly contrast with alternatives like recent_alerts or when not to use it beyond noting the OAuth requirement.

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 readOnly, openWorld, idempotent, non-destructive. Description adds that the output includes categorized examples with exact tool invocation details, and that it's drawn from a live catalog. 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?

Single paragraph is efficient, uses slashes for variants, front-loads the purpose. Could be slightly more structured but remains concise and clear.

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

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

No output schema, but description explains return type (category-bucketed example questions with tool shapes). Covers purpose, parameter behavior, and use cases. Comprehensive for an exploration-oriented 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 one optional parameter. Description adds value by listing example topic values ('finance', 'pharma', etc.) and explaining effect of omission (full spread vs focused).

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 verbs ('returns category-bucketed example questions') and identifies the resource (example questions with tool+argument shapes). It distinguishes from sibling tools like ask_pipeworx by stating to use this first 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 tells when to use: 'when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. Mentions optional topic filtering. Could improve by noting when not to use, but context is clear.

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

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

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, and destructiveHint. The description adds specific behavioral information: '5 most recently completed games', 'returns event name, date, home/away teams, and final score', and 'works for any league worldwide including lower divisions'. No contradictions with annotations.

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

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

The description is a single sentence followed by a clause, no fluff. It is front-loaded with the core purpose: 'Recent results for a team: the 5 most recently completed games/matches/fixtures'. Each part adds value, and the structure is 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?

The tool has one parameter, an output schema exists (not shown but present), rich annotations, and a clear description that explains purpose, output, and parameter source. Sibling tools (team_events_next, search_teams) provide further context. The description is 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?

The input schema has 0% description coverage for the single parameter 'team_id'. The description provides context by stating it comes from search_teams and is used to identify the team. While it does not specify format constraints, the added context compensates for the bare schema, making it moderately helpful.

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

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

The description clearly states the tool returns the 5 most recently completed games for a team, specifying the input (team_id from search_teams) and output fields (event name, date, home/away teams, final score). The title 'Team Events Last' contrasts with the sibling 'team_events_next', though not explicitly stated.

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 mentions that team_id comes from 'search_teams', implying a prerequisite. It also states the tool works for any league worldwide. However, it does not explicitly compare with the sibling 'team_events_next' or provide guidance on when to use this tool vs alternatives for more than 5 results or different timeframes.

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 behavioral context beyond the annotations: it specifies a fixed result count (5 games), lists the exact fields returned (event name, kickoff date/time, teams, league, venue), and confirms global coverage including lower divisions. The annotations already declare the tool as read-only, idempotent, and non-destructive, so the description complements them well 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 only two sentences long, with no redundant words. The first sentence front-loads the core purpose and output, and the second sentence adds valuable scope information. Every sentence serves a clear function, making it highly 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 simplicity (one parameter, no enums, output schema present), the description covers the key aspects: input source, output fields, and global applicability. It does not address error handling or edge cases (e.g., unknown team_id, no upcoming events), but the presence of an output schema likely handles return structure. Overall, it is sufficiently complete for the tool's straightforward use case.

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

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

The schema has 0% description coverage for the single parameter 'team_id', so the description must compensate. It clarifies that team_id is obtained from the 'search_teams' tool and implies it is a string identifier. While this adds basic semantic context, it does not provide details on format, length, or validation rules, which would be needed for a higher score.

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

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

The description clearly states the tool's purpose: retrieving upcoming fixtures (next 5 games) for a team using a team_id. It specifies the resource (team events), the action (get upcoming fixtures), and the scope (5 scheduled games). The use of 'upcoming' differentiates it from the sibling tool 'team_events_last', which presumably returns past events.

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

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

The description implicitly guides usage by specifying that the team_id comes from 'search_teams' and that the tool works for any league worldwide, including lower divisions. It does not explicitly mention when not to use it (e.g., for past events, use 'team_events_last'), but the context strongly implies the appropriate scenario. A more explicit alternative mention would raise the score.

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 that the row is deactivated (not deleted) and historical events remain via 'recent_alerts'. The annotations indicate non-readonly and non-destructive; description adds value by clarifying the deactivation behavior and ownership enforcement.

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, front-loaded purpose, and critical details (ownership, deactivation) provided 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?

For a single-parameter tool with no output schema, the description fully covers the action, constraints, and side effects. No gaps remain for effective agent usage.

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

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

Schema coverage is 100% with a clear parameter description. The description adds no additional meaning beyond what the schema provides ('Subscription id returned by subscribe').

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 'Cancel a subscription by id' uses a specific verb ('Cancel') and resource ('subscription by id'), clearly distinguishing it from siblings like 'subscribe' and 'list_subscriptions'.

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?

Describes ownership enforcement ('you can only cancel your own subscriptions') and outcome ('row is deactivated, not deleted'), but does not explicitly contrast with alternatives like 'list_subscriptions' or 'subscribe'.

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?

Even though annotations already mark this as read-only, idempotent, and non-destructive, the description adds substantial behavioral context: it enumerates all six possible verdicts, explains the crucial distinction between could_not_verify and unsupported, warns that could_not_verify must not be treated as evidence, and discloses the verification_error structure. This goes far beyond the annotations and is critical for correct agent 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 well-structured and front-loaded with the trigger phrases and purpose. It includes an 'IMPORTANT for callers' section and an efficiency statement, all of which are valuable. It's longer than minimal descriptions, but the tool's complexity justifies the length; no sentence is wasted.

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 no output schema, the description carries the full burden of explaining return values. It clearly specifies the verdict values, the actual value with pipeworx:// citation, and reasoning. It also covers edge cases like could_not_verify and unsupported, making the description complete for the tool's complexity and leaving no ambiguity about how to interpret results.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds meaning by explaining how tolerance_pct interacts with grading (e.g., 'set 1–2 for hallucination detection') and how the claim parameter is used in examples like 'Apple's FY2024 revenue was $400 billion' (the latter also appears in the schema). This is a meaningful enhancement 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: natural-language claim verification against authoritative sources. It provides specific trigger phrases and example claims, and distinguishes itself from sibling tools like ask_pipeworx by describing the dedicated financial and grounded pipelines. This is a specific verb+resource+scope description, not a tautology.

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 whenever the agent needs to check whether something a user said is factually correct' and explains the routing logic for financial vs. other claims. It also notes it 'Replaces 4–6 sequential calls,' indicating when it's the consolidated choice. However, it does not name alternative tools or explicitly state when NOT to use it, so it falls short of a perfect 5.

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