deckofcards

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

Annotations indicate read-only, idempotent, non-destructive. Description adds valuable context: it's a probe that scores visibility, mentions default free model and BYO key for Anthropic, and outlines return structure (score, confidence, etc.). 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?

Approximately 80 words, front-loaded with core function, no redundant sentences. Every sentence provides essential information without 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?

No output schema, but description details return per-model fields (score, confidence, signals, raw_response) plus combined view. Also covers models, API key, and context parameter. Complete for a probing tool.

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

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

Schema has 100% coverage with descriptions for all 4 parameters. Description adds extra value: default model, _apiKey only needed for anthropic, context as disambiguator. This goes beyond basic schema info.

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 entity visibility with a score per model, specifies default model and optional Anthropic, and provides use cases like AI-marketing audits, pre-launch checks. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on individual entity probing.

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

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

The description explicitly lists when to use (AI-marketing audits, pre-launch brand checks, competitive monitoring) and clarifies model choice and API key usage. It does not directly compare to sibling tools but the context is clear enough.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool automatically selects data sources and fills arguments, and it handles natural language queries. However, it lacks details on limitations (e.g., supported topics, error handling, rate limits) or response format, which are important for a tool with no output schema.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by explanatory details and concrete examples. Every sentence earns its place by clarifying functionality or providing usage context without redundancy.

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

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

Given the tool's complexity (natural language querying with automatic tool selection) and lack of annotations/output schema, the description is moderately complete. It explains the high-level behavior and provides examples, but does not cover potential constraints, error cases, or result formatting, leaving gaps for an agent to infer 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%, with the 'question' parameter well-documented in the schema as 'Your question or request in natural language'. The description adds minimal value beyond this, only reinforcing that questions should be in 'plain English' with examples. Baseline 3 is appropriate since the schema does the heavy lifting.

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: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes from siblings by emphasizing natural language input versus structured tool selection.

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 context on when to use this tool: for asking questions in plain English without needing to browse tools or learn schemas. It includes examples like 'What is the US trade deficit with China?' to illustrate appropriate use cases. However, it does not explicitly state when not to use it or name alternatives among sibling tools.

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

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

The description explains the internal fan-out logic with examples, the classification of bets, and the output (evidence packet + comparison). It adds value beyond annotations, which already indicate read-only, open-world, and non-destructive 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 fairly concise for the amount of detail, with front-loading of the core action. It includes examples and explains the benefit. A minor improvement could be removing 'the caller can see where the implied probability disagrees with the data' for brevity.

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

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

Given no output schema, the description thoroughly explains what is returned: an evidence packet and a market-vs-model comparison. It covers inputs, classification, and fan-out logic, leaving little ambiguity for a two-parameter tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context: for depth, it explains 'quick = 2-3 evidence sources, thorough = full fan-out' and notes default thorough, which is not in the schema. For market, it reinforces the accepted formats.

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's a tool to research Polymarket bets by pulling Pipeworx data. It uses specific verbs like 'Research', 'pull', 'resolves', 'classifies', 'fans out', and distinguishes from sibling tools by noting it's the core demo product.

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

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

The description explicitly lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It implies when to use over other tools (e.g., agents that use this convert better). It does not explicitly state when not to use.

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

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

With no annotations, description fully describes behavior: returns paired data with specific fields per type (revenue, net income for company; adverse-event report count for drug). It also mentions output includes resource URIs. No destructive actions indicated.

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. First sentence states main purpose and scope. Second adds key details and value proposition. 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?

Covers entity types, data returned, efficiency benefit. No output schema but description explains return type. For a comparison tool with two parameter types, it is adequately complete.

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

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

Schema coverage is 100%, baseline 3. Description adds meaning by explaining the 'values' parameter context: for company, tickers/CIKs; for drug, names. Examples are given, enhancing schema.

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

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

Description clearly states the tool compares 2-5 entities side by side, with specific verb 'Compare' and resource 'entities'. It distinguishes from siblings by mentioning it replaces 8-15 sequential agent calls, which is a unique value.

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 says when to use: to compare multiple entities efficiently across company or drug types. It does not mention when not to use, but the context is clear. Alternatives are implicitly implied by efficiency gain.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a search operation (implying read-only), returns relevant tools with names/descriptions, and has a default/max limit context (implied by the input schema but not explicitly stated in the description). However, it doesn't mention error handling, rate limits, or authentication needs, leaving some gaps.

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 appropriately sized and front-loaded: the first sentence states the core purpose, the second adds critical usage guidance. Every sentence earns its place with no wasted words, making it highly efficient and easy to parse.

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

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

Given the tool's moderate complexity (search function with 2 parameters), no annotations, and no output schema, the description is mostly complete. It covers purpose and usage well but lacks details on output format (beyond 'names and descriptions'), error cases, or performance expectations. It compensates somewhat with strong guidance.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters (query and limit). The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting, but no extra value is provided.

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 with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and distinguishes it from siblings by emphasizing its role in discovery when many tools are available. It explicitly mentions returning 'the most relevant tools with names and descriptions', making the outcome transparent.

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

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

The description provides explicit guidance on when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This includes a specific condition (500+ tools) and timing (first), and it implicitly distinguishes from siblings by focusing on discovery rather than card/deck operations.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the prerequisite (deck_id from new_deck), it doesn't describe what happens during the draw operation (e.g., cards are removed from deck, potential for empty deck, return format, error conditions). This leaves significant behavioral gaps.

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 zero waste. The first sentence states the purpose, the second provides essential usage guidance. Every word earns its place, and information is appropriately front-loaded.

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

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

For a mutation tool (drawing cards modifies deck state) with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, what happens to the deck after drawing, or potential edge cases (drawing more cards than available). The prerequisite mention helps but doesn't compensate for these 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 description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples beyond the deck_id example already in schema). Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('Draw one or more cards') and the resource ('from an existing deck'), distinguishing it from siblings like new_deck (creates deck) and shuffle_deck (reorders deck). It provides a complete verb+resource+scope statement.

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 this tool ('Requires the deck_id returned by new_deck'), providing clear context about prerequisites. However, it doesn't specify when NOT to use it or mention alternatives like shuffle_deck for different operations.

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

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

With no annotations, the description carries full burden. It explains the tool is a read-like operation (returns data) and mentions output format (pipeworx:// URIs) and efficiency (replaces many calls). It lacks explicit mention of side effects or error cases, but provides sufficient transparency for safe use.

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

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

The description is extremely concise: four sentences front-load the purpose, then list contents, output format, and alternatives. No wasted words.

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

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

For a tool with no output schema and medium complexity, the description covers key aspects: what it does, what data it returns for companies, and how to use parameters. Missing details on output structure or error handling, but sufficient for an agent to invoke correctly.

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

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

Schema description coverage is 100% with detailed descriptions for both parameters (type and value). The description adds no new parameter meaning beyond the schema; it reinforces examples already in schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool returns a full profile of an entity across relevant Pipeworx packs in one call, listing contents for companies (SEC filings, revenue, patents, news, LEI) and noting it replaces 10–15 sequential calls. It distinguishes itself from siblings like resolve_entity and compare_entities.

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

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

Explicit guidance is provided on when to use (for a comprehensive profile) and when not to (for federal contracts, use usa_recipient_profile). It also instructs to use resolve_entity first if only a name is available, covering prerequisites.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Delete' implies a destructive mutation, the description doesn't specify whether this operation is reversible, what permissions are required, or what happens on success/failure (e.g., error if key doesn't exist). It lacks critical behavioral details for a mutation tool.

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

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

The description is a single, efficient sentence that directly states the tool's function without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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

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

For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like reversibility, error conditions, or response format, leaving significant gaps in understanding how to use the tool effectively.

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

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

The schema description coverage is 100%, with the single parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional meaning beyond this, such as format examples or constraints, but the schema provides adequate baseline coverage.

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

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

The description clearly states the action ('Delete') and the resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'recall' or 'remember' beyond the destructive nature implied by 'Delete'.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'recall' or 'remember', nor does it mention prerequisites such as needing an existing memory key to delete. It simply states what the tool does without contextual usage information.

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

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

Annotations indicate read-only, idempotent, and non-destructive behavior, which the description complements by detailing the action: fetches the page, extracts title/description/key links, and emits markdown. This adds behavioral context beyond annotations. No contradictions found.

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

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

The description is three sentences: purpose, process, and use cases. It is front-loaded with the main action and audience, and every sentence adds value without redundancy. No waste.

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

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

Despite no output schema, the description explains the return value as a 'single text blob ready to drop at site-root/llms.txt'. For a simple read-only tool, this is sufficient. It could clarify error handling but is mostly complete.

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

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

Schema coverage is 100%, so baseline is 3. The description mentions parameters 'url' and 'max_links' but does not add significant semantic meaning beyond schema descriptions. The process description ('extracts title/description/key links') implies the output's content but not parameter usage details.

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

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

The description clearly states the tool generates a production-ready llms.txt file for AI crawlers, specifying the verb 'generate' and the resource 'llms.txt'. It distinguishes from sibling tools by focusing on a unique output format and use cases like auditing 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 explicit usage scenarios ('getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor'), offering clear context on when to use. It does not mention when not to use or alternatives, but the examples are sufficiently directive.

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it creates and shuffles decks, returns a deck_id, and supports multiple decks via the count parameter. However, it lacks details on error conditions, rate limits, or whether the operation is idempotent, which would be helpful for a mutation tool.

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

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

The description is appropriately sized and front-loaded, with two concise sentences that directly state the tool's action and outcome. Every sentence earns its place by covering creation, shuffling, deck count, and the return value without any waste.

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

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

Given the tool's moderate complexity (a mutation with one parameter) and no annotations or output schema, the description is mostly complete. It covers the purpose, usage, and key behavior, but could improve by addressing potential errors or the format of the returned deck_id to fully compensate for the lack of structured data.

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 description coverage is 100%, so the input schema already documents the count parameter fully. The description adds minimal value by mentioning 'multiple decks' which aligns with the schema, but does not provide additional semantics beyond what the schema specifies, such as constraints or usage examples.

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 with specific verbs ('create and shuffle') and resource ('new deck of playing cards'), and distinguishes it from sibling tools by mentioning it returns a deck_id for subsequent draws, which implies it's the initial deck creation tool versus draw_cards and shuffle_deck.

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 context for when to use this tool (to create and shuffle a new deck) and implicitly suggests alternatives by mentioning deck_id for subsequent draws, but does not explicitly state when not to use it or name specific alternatives like shuffle_deck for existing decks.

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

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

No annotations are provided, so the description carries full burden. It discloses rate limits (5 per day), cost (free), and guidelines on what to include. It does not specify response behavior, but for a feedback tool this is 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?

The description is two sentences that front-load purpose, then concrete use cases and restrictions, then rate limit. Every sentence adds value without redundancy.

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

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

Given no output schema and no annotations, the description covers purpose, usage, limits, and parameter guidance adequately. It does not describe the response format, but for a simple feedback tool this is not critical.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds value by providing usage context (e.g., 'Be specific... 1-2 sentences typical, 2000 chars max') and explaining enum values in the schema, going beyond the schema alone.

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

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

The description states 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, etc.), clearly differentiating it from sibling tools that query data or manage decks.

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 when to use ('Use for bug reports, feature requests, missing data, or praise') and gives a negative instruction ('do not include the end-user's prompt verbatim'). It also mentions rate limits, but does not explicitly exclude other uses; the context makes it clear enough.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds beyond this by stating it's a 'self-aggregating signal' from CF analytics, no PII, and cached 5min-1h, which is valuable 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?

Three well-structured sentences with bullet-pointed use cases. Every sentence is informative and front-loaded. No redundancy or clutter.

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 what is returned, the aggregation source, privacy (no PII), and caching behavior. Given no output schema, it provides sufficient context for a simple read-only tool with one parameter.

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 'window' has 100% schema description coverage with enum values explained. The description adds value by associating window length with different use cases (hot vs. steady-state demand).

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 'top tools, top packs, and total call volume' over configurable windows, distinguishing it from sibling tools like discover_tools by focusing on real-time agent usage trends.

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?

Three explicit use cases are listed (discovering hot sources, confirming canonical tools, checking alignment), but no explicit when-not-to-use or alternative tools mentioned. However, the use cases provide strong 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 and openWorld hints. The description elaborates by detailing the search behavior across Polymarket, return of ranked opportunities with reasoning, and the distinction between event and topic modes. Does not contradict annotations; adds meaningful 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?

Very concise: two sentences plus a two-mode breakdown. Front-loaded with purpose; no fluff. Every sentence contributes to understanding functionality.

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?

Parameters are well-described and an optional constraint (exactly one of event or topic) is implied but not explicit. No output schema, but the description states what is returned. Could be more complete by noting that event and topic are mutually exclusive.

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 enhances both parameters with examples and clarifies their roles in each mode. Adds value by explaining how the event parameter expects a slug/URL and the topic parameter expects a seed question.

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

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

Clearly states the tool finds arbitrage opportunities via monotonicity violations on Polymarket. Describes two specific modes (event and topic) with distinct use cases, making the purpose highly specific and 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 explains two modes and when each is appropriate, including a concrete example where topic mode is necessary. Provides clear guidance on mode selection, adding significant value beyond a simple list of parameters.

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

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

Annotations indicate read-only and open-world behavior, which the description reinforces by explaining the underlying model (FRED + coinpaprika), single fetch per asset, and deterministic ranking. It adds context about the computation process beyond what annotations provide, though it omits details like rate limits or output structure.

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

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

The description is a single paragraph but front-loads the core purpose in the first sentence. It efficiently conveys the key behavioral details without unnecessary fluff, though it could be slightly more structured for 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?

For a read-only tool with no output schema, the description adequately covers inputs, processing steps, and return type (top N ranked by edge). It provides enough context for an agent to understand what the tool does, though a brief note on output format would enhance completeness.

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 each parameter has a clear description. The description adds minimal extra context beyond the schema (e.g., defaults repeated). The baseline of 3 is appropriate as the schema already documents parameters well.

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

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

Description clearly states the tool scans high-volume Polymarket markets and returns those with greatest disagreement between Pipeworx data and market price. It specifies the unique methodology (lognormal model, grouping by asset, ranking by edge) and distinguishes it from sibling tools like polymarket_arbitrage and bet_research by targeting opportunity discovery.

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 is explicit about the intended use case: answering 'what should I bet on today' and avoiding manual paging through markets. While it doesn't explicitly list when not to use this tool or name alternatives, the context of sibling tools and the clear purpose provide sufficient guidance for an AI agent.

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

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds valuable contextual behavior: it returns leg-by-leg prices in raw probability (0-1) and spread in percentage points. 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 slightly long but well-structured: purpose first, then modes, then return format. Every sentence adds value, though some redundancy could be trimmed.

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

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

Given the complexity of two venues, two modes, and multiple outputs, the description covers the essential aspects without an output schema. It explains the return format but could mention limitations (e.g., binary events only).

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 each parameter described. The description adds meaning by explaining how topic and explicit tickers interact (overrides) and providing examples.

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 calculates cross-venue spreads between Kalshi and Polymarket for the same resolving question, using the verb 'cross-venue spread' and specifying it as an arb signal. It distinguishes itself from siblings like polymarket_arbitrage by focusing on intra-event spreads.

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 two modes (topic shortcuts and explicit tickers) with clear guidance on when to use each. However, it does not explicitly state when not to use the tool or mention alternatives, leaving a minor gap.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool's behavior: retrieving or listing memories, with persistence across sessions. However, it lacks details on error handling (e.g., what happens if a key doesn't exist), performance traits (e.g., speed or limits), or response format. This leaves gaps in understanding how the tool behaves in edge 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?

The description is concise and well-structured in two sentences. The first sentence clearly states the purpose and usage conditions, and the second provides context on when to use it. Every sentence earns its place without redundancy, making it 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 moderate complexity (retrieval/listing with session persistence), no annotations, and no output schema, the description is somewhat complete but has gaps. It covers the basic purpose and usage but lacks details on return values, error cases, or behavioral constraints. This makes it adequate but not fully comprehensive for an agent to use confidently without additional context.

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

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

The input schema has 100% description coverage, with the 'key' parameter documented as 'Memory key to retrieve (omit to list all keys).' The description adds minimal value beyond this, only reiterating the same semantics. Since schema coverage is high, the baseline is 3, and the description doesn't provide additional context like key format or examples, so it doesn't exceed 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?

The description clearly states the tool's purpose: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It specifies the verb 'retrieve' and the resource 'memory,' and distinguishes between retrieval by key and listing all memories. However, it doesn't explicitly differentiate from sibling tools like 'remember' or 'forget,' which likely handle memory storage and deletion, so it's not a perfect 5.

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 context on when to use this tool: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It explains the condition for listing all memories ('omit key') and ties usage to retrieving saved context. However, it doesn't explicitly state when not to use it or name alternatives among siblings, such as 'remember' for storing memories, so it falls short of a 5.

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

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

Without annotations, the description covers key behaviors: parallel fan-out to three sources, accepted since formats (ISO date and relative), and return structure (structured changes, count, URIs). It omits details on error handling, rate limits, or empty results, but is sufficient for typical usage.

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 remarkably concise, packing purpose, behavior, parameter guidance, and use cases into a single sentence stream. Every phrase adds unique information with no filler.

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

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

Given the tool has no output schema, the description effectively explains the return format (structured changes, total_changes, pipeworx:// URIs). It covers all three data sources, parameter details, and provides usage context, making it complete for a read tool with moderate 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 already provides descriptions for all parameters (100% coverage). The description adds significant value by explaining the fan-out behavior for type='company', giving examples and recommendations for since (e.g., 'Use 30d or 1m for typical monitoring'), and clarifying that value can be ticker or CIK.

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: 'What's new about an entity since a given point in time.' It details the parallel fan-out to SEC EDGAR, GDELT, and USPTO for company entities, and provides explicit use cases ('brief me on what happened with X' or change-monitoring), effectively distinguishing it from siblings like entity_profile or compare_entities.

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

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

The description gives clear usage context ('Use for brief me on what happened with X or change-monitoring workflows') and implies when not to use it (e.g., static profiles). However, it does not explicitly list alternatives among siblings, though the sibling names suggest appropriate distinction.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes the storage action, persistence behavior (authenticated vs. anonymous), and session context, which are crucial for understanding how the tool behaves. However, it doesn't mention potential limitations like storage size, key uniqueness, or error conditions, leaving some behavioral aspects uncovered.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by usage guidance and behavioral details in the second. Every sentence adds value without redundancy, and it's appropriately sized for a tool with two parameters and no annotations, making it efficient and well-structured.

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 (simple storage with two parameters), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, and key behavioral traits like persistence. However, it doesn't explain return values or error handling, which could be useful given the lack of output schema, leaving a minor gap in completeness.

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 description coverage is 100%, so the schema already fully documents both parameters (key and value). The description doesn't add any parameter-specific semantics beyond what's in the schema, such as format constraints or usage examples for the parameters. It meets the baseline for high schema coverage but doesn't provide extra value.

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

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

The description clearly states the specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'recall' (retrieve) and 'forget' (delete). It provides concrete examples of what to store ('intermediate findings, user preferences, or context across tool calls'), making the purpose explicit and differentiated.

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 this tool ('to save intermediate findings, user preferences, or context across tool calls') and provides context on persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), which helps guide usage decisions. It implicitly distinguishes from 'recall' (for retrieval) and 'forget' (for deletion) by focusing on storage.

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

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

No annotations are provided, so the description must fully disclose behavior. It describes the input types and outputs, and notes it's a single call replacement. However, it does not state whether the operation is read-only, mention error conditions (e.g., unresolved entity), or discuss data freshness or authorization requirements. Given the lack of annotations, more behavioral context would be beneficial.

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

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

The description is three sentences long, with clear front-loading of purpose. Each sentence adds essential information: what it does, what inputs/outputs to expect, and the benefit. No redundant words or details.

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

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

The description covers inputs, outputs, and the tool's value proposition. While there is no output schema, the description explicitly lists returned fields. It does not mention potential errors or edge cases (e.g., ambiguous names), but for a simple resolution tool with two parameters, this is generally sufficient.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by explaining that 'value' accepts ticker, CIK, or name, and provides concrete examples ('AAPL', '0000320193', 'Apple'). It also clarifies that 'type' is limited to 'company' in v1, supplementing the enum definition.

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

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

The description clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources in a single call. It specifies the supported entity type (company) and input formats (ticker, CIK, name), and lists returned data (ticker, CIK, company name, pipeworx URIs). This distinguishes it from siblings like 'ask_pipeworx' by being a focused entity resolution tool.

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

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

The description emphasizes efficiency ('single call', 'replaces 2-3 lookup calls'), implying it should be used when canonical entity IDs are needed. It does not explicitly exclude use cases or mention alternatives among siblings, but the context makes the purpose clear. A slight improvement would be to state when not to use it, but it's still effective.

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 safety (readOnly, idempotent, non-destructive). Description adds value by detailing the probing mechanism with ai_visibility_check, ranking behavior, and returned metrics (score, confidence, signal density). No contradiction.

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

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

Three sentences, front-loaded with purpose, mechanism, and use case. No filler or redundancy. 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?

No output schema exists, but description clearly states returns 'ranked list with score, confidence, signal density per entity'. For a 4-parameter tool with good annotations, this is sufficient to guide an agent.

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

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

Schema coverage is 100%, so baseline is 3. Description adds meaningful context: first entity is 'subject' for narrative, models default to workers-ai, and context disambiguates common names. This 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?

The description clearly states it compares AI visibility across multiple entities side-by-side, ranks by score, and surfaces most/least recognized. It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic).

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

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

Explicitly states usefulness for competitive AI-marketing audits with an example question. Implies when to use, but does not provide explicit when-not-to-use or alternatives beyond hinting at ai_visibility_check for single entities.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool shuffles a deck and returns drawn cards, which implies a mutation (reshuffling) and a resetting effect. However, it doesn't disclose key behavioral traits such as whether this requires specific permissions, if the shuffle is random or deterministic, what happens to the deck state (e.g., order changes), or any rate limits. For a mutation tool with zero annotation coverage, this is a significant gap.

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

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

The description is appropriately sized and front-loaded, consisting of a single, efficient sentence: 'Shuffle (or re-shuffle) an existing deck, returning all drawn cards back into it.' Every word earns its place by conveying the action, target, and effect without redundancy or unnecessary detail.

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

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

Given the tool's complexity (a mutation operation with no annotations and no output schema), the description is partially complete. It covers the basic purpose and effect but lacks details on behavioral aspects (e.g., permissions, shuffle randomness) and output (since no output schema exists). For a tool that modifies state, this leaves gaps in understanding how it behaves and what it returns, making it adequate but with clear room for improvement.

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

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

The input schema has 100% description coverage, with the 'deck_id' parameter fully documented in the schema. The description does not add any meaning beyond what the schema provides (e.g., it doesn't explain the format of 'deck_id' further or provide usage examples). Since schema coverage is high, the baseline score is 3, as the description doesn't compensate but also doesn't detract.

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: 'Shuffle (or re-shuffle) an existing deck, returning all drawn cards back into it.' It specifies the verb ('shuffle') and resource ('an existing deck'), and distinguishes it from siblings by mentioning 'returning all drawn cards back into it,' which implies a resetting action not present in 'draw_cards' or 'new_deck.' However, it doesn't explicitly differentiate from 'new_deck' in terms of creating vs. modifying, which prevents a perfect score.

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 specifying 'an existing deck,' suggesting it should be used on decks already created (likely via 'new_deck'), and 'returning all drawn cards back into it' hints it's useful after drawing cards (via 'draw_cards'). However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'new_deck' for a fresh shuffle or 'draw_cards' for drawing without shuffling), and no exclusions or prerequisites are 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?

No annotations are provided, so the description carries full burden. It thoroughly explains the tool's behavior: it returns a verdict (with five possible values), extracted structured form, actual value with citation, and percent delta. It also mentions replacing sequential agent calls, giving insight into efficiency gains.

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

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

The description is three sentences, front-loaded with the main purpose. It includes examples and a bullet-like list of return components embedded in prose. It is efficient but could be slightly more concise by omitting the list of verdicts, though it adds completeness.

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

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

Given no output schema, the description adequately explains the return format, including the verdict, structured form, actual value with citation, and delta. It covers supported claim types, data source, and even contrasts with sequential agent calls. For a single-parameter tool, this 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?

The single parameter 'claim' has a schema description with examples. The tool description adds meaning by specifying the types of claims supported (company-financial) and the source (SEC EDGAR + XBRL), which helps the agent understand valid inputs beyond the schema description.

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

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

The description clearly states the tool fact-checks natural-language claims against authoritative sources, specifically company-financial claims, using SEC EDGAR + XBRL. It provides examples and distinguishes itself from sibling tools by stating it replaces 4-6 sequential agent calls. The verb 'fact-check' and resource 'authoritative sources' are specific and informative.

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 the domain of supported claims (company-financial for public US companies) and gives examples, providing clear context for when to use. However, it does not explicitly state when not to use or suggest alternative sibling tools, though it implies limitation to financial claims.

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