gamedeals

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds valuable behavioral details: it calls external APIs (Workers AI, Anthropic), requires a BYO key for Anthropic, and returns per-model scores with confidence and signals. 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 concise (4 sentences), front-loaded with purpose, and each sentence adds unique information without redundancy. 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?

Despite no output schema, the description clearly outlines return structure (per-model object with score, confidence, signals, raw_response + combined view). With 4 params fully described and no nested objects, 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?

Schema coverage is 100% with all parameters documented. The description adds meaning by explaining default model behavior, how _apiKey is passed, and providing entity examples. 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 the tool probes LLMs for knowledge and scores visibility, with specific verb 'probe', resource 'LLMs', and outcome 'visibility score (0-100)'. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on probing and scoring specific 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 explicitly mentions use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains default model and optional Anthropic usage. It lacks explicit when-not-to-use instructions but provides sufficient context.

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

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

No annotations are provided, so the description carries the full burden. It discloses that Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which explains the automation behavior. However, it lacks details on limitations (e.g., data source availability, error handling, or rate limits), leaving gaps in behavioral context for a tool with no annotation coverage.

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 functionality, followed by practical guidance and examples. Every sentence earns its place: the first explains the purpose, the second details the mechanism, the third provides usage guidance, and the examples clarify scope. It is appropriately sized with zero wasted text.

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 query processing) and lack of annotations or output schema, the description does well by explaining the automation behavior and providing examples. However, it could be more complete by mentioning potential limitations or the types of data sources covered, as the output format and error cases are unspecified.

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 parameter 'question' well-documented in the schema. The description adds value by emphasizing 'plain English' and 'natural language,' and provides concrete examples (e.g., 'What is the US trade deficit with China?') that illustrate the expected input format beyond the schema's basic 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'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'), distinguishing it from sibling tools like search_games or list_stores which are more specific. The examples further clarify the scope of questions it handles.

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: 'No need to browse tools or learn schemas — just describe what you need.' This implies it should be used for natural language queries instead of manually selecting specialized tools, providing clear guidance on its intended context versus alternatives like discover_tools or search_deals.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, indicating a safe read operation. The description adds substantial behavioral context: it resolves the market, classifies the bet type, fans out to relevant data packs (e.g., crypto+fred+gdelt), and returns an evidence packet plus a market-vs-model comparison. This goes beyond annotations to describe the internal workflow.

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 of 4-5 sentences, front-loaded with the main action. It is efficient but could benefit from slight restructuring (e.g., bullet points for inputs/outputs) to improve scannability. However, every sentence adds value, making it appropriately 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?

Given the tool's complexity (multi-step fan-out, classification) and the absence of an output schema, the description provides a solid high-level overview. It explains the return format (evidence packet + comparison) and the process (resolving, classifying, fanning out). It does not detail the exact structure of the evidence packet, but the claim about conversion rates indicates it is sufficiently specified for agent use.

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

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

Schema coverage is 100% with both parameters described. The description adds significant meaning beyond the schema: it explains that 'market' can be a slug, URL, or question text, provides an example slug format, and clarifies that 'depth' defaults to 'thorough' and controls the number of evidence sources ('quick = 2-3, thorough = full fan-out'). This fully enriches the parameter understanding.

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

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

The description uses the specific verb 'Research' with the resource 'Polymarket bet' and explains the action: pulling Pipeworx data in one call. It clearly distinguishes itself from sibling tools like ask_pipeworx or validate_claim by focusing solely on Polymarket bets and providing a comprehensive pre-packaged analysis, including classification and evidence packet generation.

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 with example queries: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It also implies it's better than alternatives by stating it's the 'core demo product' and that agents using it 'convert better'. However, it does not explicitly mention when NOT to use it or compare directly to alternatives like ask_pipeworx.

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 the two entity types and the data fields returned for each, as well as output including paired data and resource URIs. It could improve by explicitly stating the tool is read-only, but it is fairly transparent.

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

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

The description is concise with three sentences, each adding value. The first sentence states the core purpose, the second details the types and data, and the third mentions output format and efficiency. No extraneous 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?

Given the complexity of comparing multiple entity types with different data fields, the description covers the main aspects: purpose, parameters, data returned, and efficiency gains. However, it lacks details on the exact return structure (e.g., JSON format) since no output schema exists.

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 the schema covers all parameters with descriptions, the tool description adds meaningful context: it explains the two type values and provides examples for the values parameter (tickers/CIKs for company, names for drug). This enhances the schema's minimal 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 compares 2-5 entities side by side, with specific data for 'company' and 'drug' types. It distinguishes itself from sibling tools by emphasizing a unique multi-entity comparison capability, replacing many sequential calls.

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

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

The description explicitly specifies when to use the tool (comparing 2-5 entities of a given type) and provides details on what data each type returns. It does not list exclusions or alternatives, but the context is clear enough for an agent to decide.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a search operation that returns relevant tools, and it should be called first in certain contexts. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions that would be important for a discovery tool.

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

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

The description is perfectly concise and well-structured in two sentences. The first sentence explains what the tool does, and the second provides critical usage guidance. Every word earns its place with zero waste 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 tool's moderate complexity (search operation with 2 parameters) and lack of annotations/output schema, the description provides good contextual coverage. It explains the purpose, when to use it, and the general behavior. However, it doesn't describe the return format or potential search limitations that would be helpful for a discovery 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%, so the schema already documents both parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema (e.g., it doesn't explain query formatting nuances or limit implications). Baseline 3 is appropriate when 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 with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes itself from sibling tools by focusing on tool discovery rather than game/store/deal operations.

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 guidelines: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about when to use this tool versus alternatives, including the threshold condition (500+ tools) and the primary use case (finding tools for a task).

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

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

No annotations provided, so the description carries full burden. It discloses data included and that it returns citation URIs, replacing many sequential calls. Lacks details on side effects, auth, or rate limits, but still informative.

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 succinct sentences, each providing key information. No redundant words. Efficiently communicates purpose, content, and usage tips.

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 what is returned (details and citation URIs). Covers limitations and alternative tools. Feels complete for the tool's scope.

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% description coverage. The description adds context: only 'company' supported, value can be ticker or CIK, names not supported, and recommends resolve_entity for names. Enhances schema understanding.

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

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

The description clearly states the tool returns a full profile of an entity across multiple data sources (SEC, XBRL, patents, news, LEI) in one call. It explicitly distinguishes from sibling tools like resolve_entity and usa_recipient_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?

Provides explicit guidance: use this for comprehensive profiles; use usa_recipient_profile for federal contracts; hint to call resolve_entity if only a name is available. Clearly tells when and when not to use.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Delete' implies a destructive mutation, but doesn't specify whether this action is reversible, requires permissions, has side effects, or what happens on success/failure. For a destructive tool with zero annotation coverage, this is a significant gap in 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 a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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

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

Given the tool's destructive nature (implied by 'Delete'), no annotations, and no output schema, the description is incomplete. It lacks critical information about behavioral traits, error handling, and output expectations, which are essential for safe and effective tool invocation in this 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 schema description coverage is 100%, with the parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as 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 action ('Delete') and the resource ('a stored memory by key'), which is specific and unambiguous. However, it doesn't explicitly differentiate this tool from sibling tools like 'recall' or 'remember', which appear related to memory operations, so it doesn't fully achieve sibling differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or refer to sibling tools like 'recall' (which likely retrieves memories) or 'remember' (which likely stores them), leaving the agent to infer usage context.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context: it fetches the page, extracts title/description/links, and outputs text. This goes beyond annotations by explaining the process, though it omits rate limits or size implications. 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 concise (four sentences) and front-loaded with the main purpose. Every sentence adds value without redundancy or 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 tool is simple with no output schema. The description explains the output as 'a single text blob ready to drop at site-root/llms.txt', which is sufficient. All aspects (input, process, output) are covered.

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

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

Input schema has 2 parameters with 100% description coverage. The description does not add new meaning beyond the schema; it restates the URL as 'full URL' and repeats the max_links default/max. Given high schema coverage, a baseline of 3 is appropriate with no enhancement.

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 any URL, listing specific actions (fetch, extract, emit) and the output format. It distinguishes itself from sibling tools, none of which perform this function.

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 three use cases: indexing a client's site, drafting for own project, or auditing competitor visibility. It provides clear context for when to use, though it does not mention alternative tools (none are directly comparable).

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 what data is returned (price details, history, deals) but does not cover important traits such as rate limits, authentication needs, error handling, or whether this is a read-only operation. The description adds value by specifying the scope of data but misses key behavioral aspects.

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 efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the main action ('Get full price details') and lists specific data points clearly, making it easy to understand quickly.

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

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but has gaps. It explains what data is returned but does not address behavioral aspects like rate limits or error handling. Without annotations or output schema, the description should provide more context on how the tool behaves, but it partially compensates by detailing the data scope.

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 'id' parameter documented as 'CheapShark game ID (obtained from search_games)'. The description does not add any additional meaning beyond what the schema provides, as it does not mention parameters at all. Baseline 3 is appropriate since the schema handles parameter documentation effectively.

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 ('Get full price details') and resources ('for a game'), distinguishing it from siblings like 'search_games' (which finds games) and 'search_deals' (which finds deals). It explicitly lists the types of details returned: price history, cheapest price ever, and current deals across stores.

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 that the ID should be 'obtained from search_games' in the schema, but it does not explicitly state when to use this tool versus alternatives like 'search_deals' for deals or 'list_stores' for store information. It provides some context but lacks explicit guidance on exclusions or 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a list operation that returns data, implying it's read-only and non-destructive. However, it doesn't mention potential limitations like rate limits, authentication requirements, or whether the list is cached/real-time.

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 perfectly concise with two sentences that each earn their place: the first states the action and resource, the second explains the return value and its purpose. No wasted words, front-loaded with the core 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?

For a zero-parameter list tool with no annotations and no output schema, the description provides adequate context about what it does and why. It could be more complete by mentioning the format of the returned data or any behavioral constraints, but it covers the essential purpose and usage linkage well.

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 zero parameters with 100% schema description coverage, so the schema already fully documents the absence of parameters. The description appropriately doesn't add parameter information beyond what the schema provides, maintaining focus on the tool's purpose and output.

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 ('List all game stores') and resource ('tracked by CheapShark'), distinguishing it from siblings like search_deals or search_games. It explicitly identifies what gets returned ('store names and IDs') and their purpose ('for use with search_deals').

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 about when to use this tool ('for use with search_deals'), establishing its role as a prerequisite for another sibling tool. However, it doesn't explicitly state when NOT to use it or mention alternatives like get_game_details for different purposes.

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

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

No annotations provided, so description must disclose behavior. It does state the rate limit (5 per identifier per day) and cost ('Free'). However, it does not mention post-submission behavior (e.g., no confirmation, manual review) or authentication requirements. Adequate but could be richer.

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 total, each adding distinct value: purpose, usage guidelines, rate limit/cost. Front-loaded with purpose, no redundant words.

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

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

Covers purpose, usage, constraints, and rate limit. For a feedback submission tool with moderate complexity (3 params, nested object), this is nearly complete. Missing output/confirmation details (e.g., what happens after sending), but that's minor given tool simplicity.

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

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

Schema description coverage is 100% and already detailed. Description adds practical guidance: 'Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max.' This helps the agent craft suitable messages beyond what schema provides.

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

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

Clearly states 'Send feedback to the Pipeworx team' and enumerates specific use cases (bug, feature, data gap, praise). This immediately distinguishes it from sibling tools like discover_tools or get_game_details, which are for data retrieval.

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 to use for feedback types and provides instructions to describe Pipeworx-specific context and avoid including end-user prompt. Does not explicitly mention when not to use, but given sibling tool purposes, exclusion is implied. Slight gap in not naming 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 indicate read-only, idempotent, non-destructive. Description adds valuable context: derived from CF analytics-engine, no PII, aggregated counts, cache duration (5min-1h). No contradiction with annotations.

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

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

Description is well-structured with a brief overview, bulleted use cases, and additional details. Every sentence adds value, no redundant or extraneous text.

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 optional parameter, no output schema), the description adequately explains output, caching, data sources, and privacy. It is complete for effective use.

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

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

Schema coverage is 100%, and the schema's parameter description is already detailed. The main description mentions the window options but doesn't add significant new meaning beyond what the schema provides, so a score of 3 is appropriate.

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

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

The description clearly states it returns top tools, packs, and call volume over a time window. It uses specific verbs and resources, and distinguishes itself from siblings by focusing on trending data from AI agent usage.

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 data sources, confirming canonical tools, aligning use case with trends). Though it doesn't explicitly state when not to use, the use cases provide clear guidance. Could mention alternatives for more specific trend analysis.

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 readOnlyHint and non-destructive annotations, the description details behavioral traits: it walks child markets, groups related markets, checks monotonicity, and returns ranked opportunities with trade direction. Adds significant 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 a single paragraph but well-structured: purpose statement, then mode explanations. It is front-loaded and avoids redundancy, 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?

Despite no output schema, the description explains return values (ranked opportunities with reasoning). Covers inputs, modes, and behavior completely for the tool's complexity.

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

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

Schema coverage is 100% with descriptions, but the tool description adds substantial meaning by explaining the two modes and providing examples, exceeding 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 it finds arbitrage opportunities by checking monotonicity violations. It distinguishes two modes (event and topic) and explains their differences, making it distinct from siblings like 'polymarket_edges'.

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

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

Explicitly describes when to use each mode: event mode for a single event, topic mode for cross-event searches. Gives an example where topic mode catches cases event mode misses, providing clear guidance 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?

Annotations declare readOnlyHint=true and destructiveHint=false. The description adds significant behavioral detail: it scans top markets, groups by asset, fetches price history once, computes model probability, and ranks by |edge|. This transparency complements the 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 concise at four sentences, with the main purpose stated upfront. Each sentence adds value: purpose, version scope, algorithm, and usage context. 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 explains the output (top N with edge magnitude and trade direction) and algorithm. It mentions V1 covers crypto-price bets, which is a limitation. No output schema exists, so this is reasonably complete. Could add more about caveats or future versions.

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 parameters are well-documented in the schema. The description mentions 'top N' and 'scans top markets' which relate to limit and window parameters, but adds no novel semantic value beyond that. Baseline 3 is appropriate.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees with market price. It specifies the verb (scan/return), resource (markets), and unique value (edge detection). This distinguishes it from siblings like polymarket_arbitrage and bet_research.

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

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

The description says it's built for the 'what should I bet on today' question, providing clear context for when to use. However, it lacks explicit statements about when not to use or alternatives, though the usage scenario is well-defined.

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 discloses output format (leg-by-leg prices in probability 0-1 and spread in percentage points) and the behavioral trait that the delta is a real arbitrage signal, adding context beyond the annotations which already indicate read-only, idempotent, 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?

Three sentences, no wasted words. Information is front-loaded with purpose, context, and usage modes clearly separated. Each 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?

Tool has no output schema, but description fully explains the return values (leg-by-leg prices and spread). Context about arbitrage signal and two modes is provided, making the description complete for a simple parameter set.

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

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

Schema description coverage is 100%, and the description adds value by explaining that explicit tickers override the topic-mapped side, and provides example values for the topic parameter, enhancing the meaning 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?

Description clearly states the tool calculates cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies the verb (spread) and resource (cross-venue), and distinguishes from sibling tools like polymarket_arbitrage by focusing on the Kalshi-Polymarket pair.

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 explains two modes (topic shortcuts and explicit tickers) with examples, and provides context that the spread is an arbitrage signal due to different participant pools. It indicates when to use (real arb signal) but does not explicitly state when not to use or compare to alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the dual functionality (retrieve by key or list all) and persistence across sessions, which is valuable. However, it doesn't mention error handling (what happens if key doesn't exist), format of returned memories, or any rate limits/constraints. The description adds some behavioral context but leaves gaps for a tool with no annotation coverage.

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 perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality with conditional logic, and the second provides usage context. No wasted words, and information is front-loaded appropriately.

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 (dual functionality, session persistence), no annotations, and no output schema, the description does a good job covering the essentials. It explains what the tool does, when to use it, and parameter semantics. The main gap is lack of information about return format/output structure, which would be helpful since there's no output schema.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter. The description adds meaningful context by explaining the semantic effect of omitting the key ('omit to list all keys') and connecting the parameter to the tool's dual functionality. This goes beyond what the schema provides about the parameter's purpose.

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 ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by mentioning 'context you saved earlier' which differentiates it from tools like 'search_games' or 'discover_tools' that don't involve stored memories.

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 ('to retrieve context you saved earlier in the session or in previous sessions') and includes conditional usage instructions ('omit key to list all keys'). It also implicitly distinguishes from alternatives like 'remember' (for saving) and 'forget' (for deleting).

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 must fully disclose behavioral traits. It explains that for 'company' type, the tool fans out to multiple sources in parallel, which is a key behavior. It also describes the return format: structured changes, total_changes count, and pipeworx:// URIs. It does not mention rate limits, authentication needs, or error behavior, but for a read-only tool with straightforward inputs, the provided details are sufficient. The description adds value beyond what the schema conveys.

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 (3 sentences) and front-loaded with the core purpose. Every sentence adds value: the first states the overall function, the second details behavior for the only supported type, and the third covers parameters and return format. There is no redundancy or unnecessary information.

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

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

Given the tool has 3 required parameters, no output schema, and no annotations, the description provides a complete picture: input formats, internal behavior, and return value structure. It lacks details on pagination, error handling, or limits, but for a tool that returns recent changes (likely a bounded result set), this is acceptable. The description is rich enough for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description enhances parameter meaning: it explains the fan-out behavior for 'type', provides format examples for 'since', and clarifies that 'value' accepts either a ticker or CIK. This additional context helps an agent understand the parameters better than the schema alone. However, it doesn't specify constraints like maximum window length, so not a 5.

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 retrieves recent changes for an entity since a given time. It specifies the verb ('what's new'), resource ('entity'), and temporal scope ('since a given point in time'). While it does not explicitly differentiate from sibling tools, the unique fan-out behavior and mention of specific source types (SEC EDGAR, GDELT, USPTO) provide strong differentiation. The instruction to use for 'brief me on what happened with X' further clarifies its purpose.

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 guidance: it states the supported entity type ('Only 'company' supported today'), gives examples for the 'since' parameter (ISO date or relative), and recommends typical values ('Use '30d' or '1m' for typical monitoring'). It also explicitly states when to use the tool ('brief me on what happened with X' or change-monitoring workflows). Although it doesn't explicitly state when not to use it, the context is clear enough for an agent to make appropriate decisions.

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

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation (storage), specifies persistence characteristics (authenticated users get persistent memory, anonymous sessions last 24 hours), and implies session-scoped functionality. It doesn't mention rate limits, error conditions, or specific permission requirements, but covers the essential behavioral aspects for a memory storage 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 perfectly concise and well-structured in just two sentences. The first sentence states the core purpose, the second provides usage guidelines and behavioral context. Every word earns its place with no redundancy or unnecessary elaboration. The information is front-loaded with the primary function stated immediately.

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 2-parameter tool with no annotations and no output schema, the description provides good contextual completeness. It explains what the tool does, when to use it, and important behavioral characteristics (persistence differences). The main gap is the lack of information about return values or confirmation of successful storage, but given the tool's relative simplicity and the clear behavioral transparency, it's reasonably 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?

With 100% schema description coverage, the input schema already fully documents both parameters (key and value) with clear descriptions. The tool description doesn't add any additional parameter semantics beyond what's in the schema - it doesn't explain parameter relationships, constraints, or usage patterns. The baseline of 3 is appropriate when the schema does all the parameter documentation work.

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 ('store a key-value pair') and resource ('in your session memory'). It distinguishes from sibling tools like 'recall' (likely for retrieval) and 'forget' (likely for deletion) by focusing on storage functionality. The description goes beyond the name/title by specifying what kind of data can be stored.

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: 'to save intermediate findings, user preferences, or context across tool calls.' It also distinguishes usage scenarios based on authentication status (authenticated vs. anonymous sessions), giving clear context for application. No explicit alternatives are named, but the context implies when this tool is appropriate versus retrieval or deletion 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?

No annotations provided, so description carries full burden. It describes input types and returns (ticker, CIK, name, resource URIs) but does not disclose potential rate limits, authentication needs, or error behavior. Adequate for a straightforward lookup.

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 efficiently cover purpose, input, output, and version info without waste. Front-loaded with key action and scope.

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

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

Given the tool's simplicity (2 params, no output schema), the description adequately covers purpose, input format, and return values. It adds context about replacing multiple calls, which aids in tool selection.

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 both parameters are well-described. The description adds context with examples of valid values and notes that v1 only supports 'company', which adds meaning beyond the schema's enum.

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, with specific examples for 'company' type (ticker, CIK, name). It distinguishes from siblings by noting it replaces 2-3 lookup calls.

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

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

The description implies usage by stating 'in a single call' and 'replaces 2-3 lookup calls', but lacks explicit when-to-use, when-not, or alternative conditions. It provides version info and supported types.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the tool's safety and idempotency are clear. The description adds value by explaining the probing mechanism and the output structure (ranked list with 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?

The description is concise (4 sentences) and well-structured: purpose first, then mechanism, use case, and output. Every sentence adds value 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?

Despite no output schema, the description sufficiently describes the output as ranked list with metrics. It also explains the role of the first entity. It could mention the entity count limit from schema, but schema already provides that.

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

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

With 100% schema description coverage, baseline is 3. The description adds extra semantics: first entity is treated as subject, models parameter default and Anthropic key requirement, and context purpose for disambiguation.

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 specific verb 'compare' and resource 'AI visibility across multiple entities', explicitly contrasting with single-entity tool ai_visibility_check by stating it probes multiple entities and ranks them. It also provides a concrete use case (competitive audits).

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

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

The description provides a clear context for use ('competitive AI-marketing audits') and implies when to use it (multiple entities) vs. alternatives (single entity would use ai_visibility_check). However, it does not explicitly state when not to use it or name other 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return fields (deal title, store, prices, etc.) which is helpful, but doesn't describe pagination behavior (though page_size parameter hints at it), rate limits, authentication requirements, or error conditions. For a search tool with 6 parameters, this leaves significant behavioral aspects undocumented.

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 perfectly concise - two sentences that efficiently convey the core functionality and return format. The first sentence states the purpose, the second lists return fields. Every word earns its place with zero redundancy or unnecessary elaboration.

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 search tool with 6 well-documented parameters but no annotations and no output schema, the description provides adequate but incomplete context. It covers what the tool does and what it returns, but lacks behavioral details (pagination, errors, limits) and sibling tool differentiation. The absence of an output schema means the description's return field listing is valuable, but overall completeness is just 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 schema description coverage is 100%, so all parameters are well-documented in the schema itself. The description adds minimal value beyond the schema - it mentions 'optional filters' which aligns with the schema's 0 required parameters, but doesn't provide additional context about parameter interactions or usage patterns. Baseline 3 is appropriate when 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: 'Search for game deals with optional filters.' It specifies the resource (game deals) and action (search). However, it doesn't explicitly differentiate from sibling tools like 'search_games' - both involve searching, but one is for deals and the other for games. The distinction is implied but not 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 provides no guidance on when to use this tool versus alternatives. There are three sibling tools (get_game_details, list_stores, search_games), but the description doesn't mention any of them or explain when this search_deals tool is appropriate versus searching for games directly. No context about prerequisites or exclusions 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return data (price and deal ID) but lacks details on error handling, rate limits, authentication needs, pagination, or whether the search is case-sensitive. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its operational 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 two sentences, front-loaded with the core purpose and followed by return details. Every word earns its place with no redundancy or fluff, making it highly efficient and easy to parse for an AI agent.

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

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

Given the tool's moderate complexity (search function with two parameters), no annotations, and no output schema, the description is adequate but incomplete. It covers the purpose and return data but lacks behavioral context (e.g., error cases, performance limits) and detailed output structure, which would be needed for full agent understanding.

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 input schema fully documents both parameters ('query' for game title and 'limit' for result count). The description adds no additional parameter semantics beyond what the schema provides, such as search syntax or format examples, meeting the baseline for high schema coverage.

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

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

The description clearly states the specific action ('Search for games by title') and resource ('games'), distinguishing it from sibling tools like 'get_game_details' (detailed view), 'list_stores' (store listing), and 'search_deals' (deal-focused search). It explicitly mentions the return data (cheapest price and deal ID), making the purpose unambiguous.

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

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

The description implies usage for finding games by title, but it does not explicitly state when to use this tool versus alternatives like 'search_deals' or 'get_game_details'. There is no guidance on prerequisites, exclusions, or comparative contexts, leaving the agent to infer usage from the purpose 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?

With no annotations provided, the description fully bears the burden of behavioral disclosure. It explains the output (verdict, structured form, actual value with citation, percent delta) and the source (SEC EDGAR + XBRL). It also notes the version limitation (v1). However, it does not mention authentication requirements or error handling.

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

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

The description is concise, using a single paragraph that front-loads the purpose and key details. It covers all necessary information without verbosity. However, it could be slightly more structured (e.g., bullet points for return fields).

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

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

Given the tool has one parameter and no output schema, the description provides complete context. It explains the input, output fields, source, and limitations. The description is sufficient for an agent to understand and 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 has 100% coverage with a description for the 'claim' parameter. The tool description provides example values and format guidance, but these add marginal value beyond the schema. Baseline is 3, and no significant additional semantics are 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: fact-check a natural-language claim against authoritative sources, specifically company-financial claims via SEC EDGAR and XBRL. It lists the verdict types and return values, distinguishing itself from sibling tools by noting it replaces sequential agent calls.

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

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

The description specifies that v1 supports only company-financial claims for public US companies, providing clear context when to use the tool. While it does not explicitly state when not to use, the domain restriction implies exclusions, and the note about replacing sequential calls guides usage versus alternatives.

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