agify

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint true and destructiveHint false. The description adds beyond that: clarifies free tier for Workers AI, payment responsibility for Anthropic probes, and return structure (per-model objects plus combined view). No contradictions with annotations.

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

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

Three sentences with no wasted words. First sentence defines core function and output. Second explains model options and API key. Third covers return format and use cases. Front-loaded with purpose, then specifics.

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 adequately describes return values (per-model {score, confidence, signals, raw_response} plus combined view) and use cases. Missing details on combined view structure or pagination, but given tool simplicity and idempotent nature, it's 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%, so baseline is 3. The description adds meaningful context: explains that omitting models defaults to workers-ai, that _apiKey is passed directly to api.anthropic.com, and gives a concrete example for entity (e.g., 'Pipeworx'). This exceeds what the schema alone provides.

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

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

The description explicitly states the tool probes LLMs for knowledge about a business/brand/product/topic and scores visibility (0-100). It uses specific verbs ('probe', 'score') and resources ('LLMs', 'per-model {score, confidence, signals, raw_response}'), clearly distinguishing it from siblings like 'entity_profile' or 'scan_competitor_ai_presence' by focusing on multi-model visibility scoring.

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 default model behavior (Workers AI free) and optional Anthropic probing with BYO API key, giving clear context for when to include _apiKey. It lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) but does not explicitly contrast with sibling tools or state when not to use it.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's core behavior: it interprets natural language questions, selects appropriate data sources, executes queries, and returns results. However, it doesn't mention limitations like response time, accuracy guarantees, or data source constraints.

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 efficiently structured with a clear purpose statement followed by behavioral explanation and concrete examples. Every sentence adds value: the first explains the tool's function, the second describes its automation benefits, and the third provides illustrative use cases without redundancy.

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

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

For a single-parameter tool with no annotations and no output schema, the description provides good contextual coverage of purpose, usage, and behavior. However, it lacks information about output format, error conditions, or limitations that would be helpful given the absence of structured output documentation.

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 'question' parameter adequately. The description adds minimal additional semantic context beyond what's in the schema ('Your question or request in natural language'), providing only the baseline value expected when schema coverage is high.

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 ('ask a question', 'get an answer') and resources ('best available data source'), distinguishing it from siblings by emphasizing natural language processing rather than specific data operations. It explicitly contrasts with sibling tools by stating 'No need to browse tools or learn schemas'.

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 ('just describe what you need in plain English') versus alternatives (implicitly suggesting not to use sibling tools like discover_tools when you don't want to 'browse tools or learn schemas'). The examples further clarify appropriate use cases.

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, non-destructive. Description adds that it resolves the market, classifies the bet, fans out to packs, and returns evidence. This contextualizes the behavior beyond the annotations.

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

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

Description is detailed yet efficient, front-loading the core purpose. About 3-4 sentences, all adding 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?

Despite no output schema, the description fully explains what is returned (evidence packet + market-vs-model comparison) and the internal logic (resolution, classification, fan-out). Covers all aspects needed for agent invocation.

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

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

Schema covers all parameters with descriptions. Description adds meaning for 'depth' (explains enum values 'quick' vs 'thorough') and reiterates 'market' input types, slightly enhancing 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's purpose: researching Polymarket bets by pulling Pipeworx data. It specifies input types (slug, URL, question text) and output (evidence packet + market-vs-model comparison). This distinguishes it from sibling tools like ask_pipeworx or validate_claim.

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 examples of when to use: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. No exclusions or alternatives mentioned, but the context is clear.

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

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

With no annotations, the description discloses return data and efficiency gains, but does not cover potential limitations, authentication needs, or side effects beyond the returned data.

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 redundancy, front-loaded with core purpose, and every sentence adds critical 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 no output schema, it adequately explains output for both types and mentions URIs, but could detail the 'paired data' format and URI structure more clearly.

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. The description adds value by specifying exactly which fields are returned per entity type, going beyond the schema's enum and format.

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

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

The description clearly states it compares 2–5 entities side by side, specifying data fields for company and drug types, and distinguishes from generic sibling tools by its specialized 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?

It provides clear context on when to use (comparing specific entities) and reduces sequential calls, but lacks explicit 'when not to use' or direct 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 search functionality and return format ('most relevant tools with names and descriptions'), but doesn't mention potential limitations like rate limits, authentication requirements, or error conditions. It adequately covers the basic operation but lacks deeper 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 perfectly concise with two sentences that each serve a clear purpose: the first explains what the tool does, and the second provides crucial usage guidance. There's zero wasted language, and the most important information (when to use it) 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 search tool with no annotations and no output schema, the description provides good context about when to use it and what it returns. However, it doesn't describe the format of returned results beyond 'names and descriptions' or mention any limitations. Given the 100% schema coverage and clear purpose, it's mostly complete but could benefit from more detail about output structure.

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

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

The schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions the tool's purpose but doesn't elaborate on parameter usage or constraints beyond the schema's existing documentation.

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

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

The description clearly states the tool's purpose with specific verbs ('search the Pipeworx tool catalog') and resource ('returns the most relevant tools with names and descriptions'). It distinguishes this tool from its siblings (predict_age, predict_age_country) by focusing on tool discovery rather than prediction tasks.

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: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This clearly indicates when to use this tool versus alternatives, including the specific condition (500+ tools) and priority (first).

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 reveals that the tool returns citation URIs, lists the data categories included, and notes that only 'company' type is currently supported. However, it does not mention error handling, authentication requirements, or rate limits, which would be expected for a tool that aggregates from multiple sources.

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 main purpose. Every sentence serves a clear function: stating the core capability, listing returned data, and providing usage caveats and alternatives. No unnecessary words.

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

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

Given no output schema, the description adequately explains what is returned (categories of data and URIs). It covers the major data types and provides a caution about federal contracts. However, it could briefly mention the response structure (e.g., a JSON object with keys) to fully compensate for the missing output schema in a tool that collates multiple sources.

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 descriptions for both parameters. The description adds value beyond schema by clarifying that 'type' only supports 'company' (with future support hinted) and that 'value' accepts ticker or CIK, not names. It also directs users to resolve_entity for name resolution, which is helpful context.

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

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

The description explicitly states the tool returns a 'full profile of an entity across every relevant Pipeworx pack in one call.' It lists specific data types for company entities (SEC filings, financials, patents, news, LEI) and mentions the return format (pipeworx:// URIs). It also differentiates from sibling tools by noting it replaces 10-15 sequential calls and directs federal contract queries to 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?

The description provides clear when-to-use guidance ('replaces 10–15 sequential agent calls') and when-not-to-use ('For federal contracts call usa_recipient_profile directly'). It also implies prerequisite steps by stating 'Names not supported — use resolve_entity first if you only have a name.' This helps the agent decide between 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?

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Delete' implies a destructive operation, it doesn't specify whether deletion is permanent, reversible, requires specific permissions, or what happens on success/failure. The description lacks crucial behavioral context for a destructive operation.

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

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

The description is extremely concise at just 5 words, front-loading the essential action ('Delete') and resource. 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 destructive operation with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'stored memory', what happens after deletion, error scenarios, or return values. Given the tool's destructive nature and lack of structured metadata, more context is needed.

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 'key' parameter adequately. The description adds no additional semantic context beyond what's in the schema - it simply repeats that deletion is 'by key' without explaining key format, constraints, or 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 action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'recall' or 'remember', but it's specific enough to understand what the tool does.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'recall' (which likely retrieves memories) or 'remember' (which likely stores memories). There's no mention of prerequisites, error conditions, or appropriate contexts for deletion.

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 (readOnly, idempotent, not destructive) align with description's claims of fetching and extracting data without side effects. Description adds that it 'fetches the page, extracts title/description/key links' and outputs a single text blob, clarifying behavior beyond annotations.

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

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

Description is two sentences, front-loaded with the main purpose, followed by use cases. No unnecessary words, though the list of AI crawlers could be slightly trimmed without losing clarity.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description fully covers purpose, input requirements, output format, and use cases. No gaps remain.

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

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

Schema coverage is 100% with clear descriptions for both parameters (url and max_links). Description does not add additional semantic meaning beyond the schema, so baseline score of 3 applies.

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 generates an llms.txt file for any URL, specifying the output format and use cases. It distinguishes from siblings as no other tool appears to serve this 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?

Explicitly lists three use cases (client indexing, own project drafting, competitor auditing), providing clear context for when to use. Does not explicitly state when not to use, but the guidance is sufficient.

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

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

No annotations are provided, so the description must disclose behavior. It does so by stating the rate limit (5 per day) and that it is free. It does not mention any response or error behavior if the rate limit is exceeded, but for a feedback tool this is acceptable transparency.

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

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

The description is extremely concise with only two sentences, covering the core action, use cases, instructions, and rate limit. It is front-loaded with the verb 'Send feedback', making the purpose immediately clear.

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

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

Given no output schema and no annotations, the description adequately covers what the tool does, when to use it, and content restrictions. It does not explain what happens after submission (e.g., confirmation), but for a 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?

The input schema has 100% coverage for all parameters, so the description adds limited value. However, the description provides crucial guidance on the content of the 'message' field (describe what was tried in terms of Pipeworx tools/data, exclude end-user prompt), which supplements the schema's basic 'feedback in plain text' 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 action ('Send feedback') and resource ('Pipeworx team'), enumerates specific use cases (bug reports, feature requests, etc.), and gives a unique instruction about not including end-user prompts. It distinguishes itself from sibling tools like ask_pipeworx and discover_tools by focusing on feedback.

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 appropriate uses (bug reports, feature requests, data gaps, praise) and provides content guidelines (describe in terms of Pipeworx tools/data, avoid end-user prompt). It also mentions a rate limit. It does not explicitly contrast with sibling tools but the intended use is clear.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds caching behavior, data source (CF analytics-engine), no PII, and result contents, going beyond annotations.

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

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

The description is a well-structured paragraph that front-loads the purpose, lists use cases, and explains the data source. Slightly verbose but each sentence adds value.

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

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

Given no output schema, the description adequately explains what is returned (top tools, packs, total call volume) and cache behavior. Lacks explicit format details but sufficient for this simple tool.

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

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

The input schema already describes the window parameter with enum and description. The tool description adds contextual meaning about shorter vs longer windows surfacing different trends, enhancing 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 it returns top tools, packs, and call volumes over a window, derived from analytics. It is a specific verb-resource combination and distinguishes itself from siblings like discover_tools.

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

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

Three explicit use cases are provided (discovering hot data, confirming canonical choice, aligning use case). No explicit when-not-to-use or alternatives, but the guidance is strong.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, implying a safe read operation. The description adds behavioral details: it checks monotonicity violations, returns ranked opportunities with suggested trade direction and reasoning. It does not mention rate limits or authentication, but the read-only nature and openWorldHint are consistent with the description.

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 yet comprehensive. It starts with the overall purpose, then details the two modes in a structured manner. No extraneous information; every sentence adds useful context. The length is appropriate given the tool's complexity.

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

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

Given the tool's complexity (two modes, monotonicity checking), the description is complete. It covers both modes, their use cases, the output (ranked opportunities with trade direction and reasoning), and provides examples. No output schema exists, but the description sufficiently describes the return value.

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, but the description adds significant value by explaining the meaning of each parameter in context. For 'event', it gives an example slug and indicates it can be a full URL. For 'topic', it explains the cross-event mode and provides a concrete example. This goes beyond the schema's simple parameter descriptions.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets.' It distinguishes itself from sibling tools like polymarket_edges by focusing on arbitrage opportunities and introduces two distinct modes (event and topic), making its purpose specific 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 provides explicit guidance on when to use each mode: event mode for a single event slug, and topic mode for cross-event arbitrage. It explains the limitation of event mode ('single-event mode misses the May≤June rule') and why topic mode is needed. It does not explicitly state when not to use the tool, but the context is clear enough for an AI agent.

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

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

Beyond the annotations (readOnlyHint, openWorldHint, destructiveHint), the description details the algorithm: scans top markets, groups by asset, fetches price history once, computes model probability, and ranks by |edge|. It also notes the V1 model covers crypto-price bets using FRED and coinpaprika. This adds significant behavioral context beyond what annotations provide.

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

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

The description is three sentences, each serving a distinct purpose: main action, technical detail, and use case. It is front-loaded with the core purpose and contains no redundant or unnecessary words.

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

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

Given the tool has three parameters, no output schema, and annotations present, the description explains the algorithm and output (returns top N ranked by edge magnitude with suggested trade direction) adequately. It is complete for a discovery tool, though an example of output format could further 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% with descriptions for all three parameters (limit, window, min_edge_pp). The description does not add new meaning beyond the schema; it merely restates the parameters in context. Baseline is 3, and no additional 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 scans high-volume Polymarket markets to find those with the largest disagreement between Pipeworx data and market price. It uses specific verbs ('scan', 'return') and identifies the resource ('Polymarket markets'). It distinguishes itself from siblings like 'polymarket_arbitrage' by focusing on edge magnitude and 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 explicitly states the tool is built for the 'what should I bet on today' question, helping users discover opportunities without manual paging. This provides clear context on when to use it, though it does not mention when not to use it or explicitly compare to alternatives like polymarket_arbitrage.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds behavioral context beyond annotations: it explains the source of the spread (different participant pools), the typical delta range (2-25pp), and the output (prices in 0-1, spread in percentage points). There is no contradiction with annotations.

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

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

The description is front-loaded with the core purpose and includes all necessary information in a logical flow: purpose, significance, modes, output. While it is slightly verbose (e.g., 'pre-mapped macro shortcuts'), every sentence earns its place. No wasted words.

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

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

Given no output schema, the description thoroughly explains the output format (leg-by-leg prices and spread) and the two modes of operation. It covers the tool's complexity well for a read-only tool. It lacks mention of error handling or fallback behavior when events don't match, but overall it is complete enough for correct invocation.

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 each parameter is already described in the schema. The description adds semantic value by explaining the relationship between parameters (explicit overrides topic), providing example values (e.g., 'KXFED-26OCT'), and clarifying that topic is a macro shortcut. This helps the agent understand how to combine parameters correctly.

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 computes a cross-venue spread between Kalshi and Polymarket for the same resolving question, explaining the spread's significance as an arbitrage signal. However, it does not differentiate from the sibling tool 'polymarket_arbitrage', so purpose differentiation is lacking.

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 covers two usage modes (topic shortcuts and explicit tickers) with examples, providing clear context for when to use each. However, it offers no guidance on when not to use this tool versus alternatives like polymarket_arbitrage, nor does it mention any prerequisites or limitations.

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 the prediction behavior and data source, but doesn't mention accuracy limitations, rate limits, or what happens with uncommon names. It adds some context but lacks comprehensive behavioral details.

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 with zero waste. It's appropriately sized for a simple tool and front-loads the core purpose 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 simple prediction tool with one parameter and no output schema, the description is adequate but has gaps. It doesn't explain the return format (e.g., age value, confidence score) or handle edge cases. With no annotations, it could benefit from more behavioral context.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter. The description adds marginal value by reinforcing that it's a 'first name' for age prediction, but doesn't provide additional syntax or format details beyond what the schema provides.

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

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

The description clearly states the specific action ('predict the most likely age'), resource ('a person based on their first name'), and data source ('global data from agify.io'). It distinguishes from the sibling tool predict_age_country by specifying 'global data' without country filtering.

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 when to use this tool (for global age prediction based on first name) and when not to use it (when country-specific prediction is needed, as suggested by the sibling tool name predict_age_country). However, it doesn't explicitly name the alternative or state exclusions.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the prediction action and calibration, but lacks details on accuracy, limitations, data sources, or response format. For a prediction tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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 unnecessary words. It's front-loaded with the core purpose and includes the key constraint, making it easy to parse and 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 moderate complexity (prediction with calibration), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and calibration aspect, but lacks details on behavioral traits, output format, or sibling differentiation, leaving room for improvement 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?

Schema description coverage is 100%, so the schema already documents both parameters ('name' and 'country_code') with clear descriptions. The description adds minimal value beyond the schema by implying country calibration, but doesn't provide additional syntax or format details. 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: predicting age based on first name with country calibration. It specifies the verb ('predict'), resource ('age'), and key constraint ('calibrated to a specific country'). However, it doesn't explicitly differentiate from the sibling tool 'predict_age', which likely lacks country calibration, so it misses full sibling distinction.

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 context by mentioning country calibration, suggesting this tool should be used when geographic localization is needed. However, it doesn't explicitly state when to use this tool versus the sibling 'predict_age' or provide any exclusions or alternatives, leaving the guidance incomplete.

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 the tool retrieves or lists memories, including persistence across sessions ('previous sessions'), which is useful behavioral context. However, it doesn't mention potential errors (e.g., if a key doesn't exist), performance aspects, or data format of retrieved memories, leaving gaps in behavioral understanding.

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

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

The description is concise and well-structured in two sentences. The first sentence clearly states the tool's functionality and parameter usage, while the second provides usage context. There is no wasted language, and key information is front-loaded, making it efficient for an agent 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 (retrieve/list operations), lack of annotations, and no output schema, the description is adequate but incomplete. It covers the basic purpose and usage but omits details like error handling, memory format, or session persistence mechanics. For a tool with no structured behavioral hints, more context would be beneficial to ensure reliable agent operation.

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 parameter 'key' documented as 'Memory key to retrieve (omit to list all keys).' The description adds minimal value beyond this, only reinforcing the same conditional behavior. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't significantly enhance 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 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'/'list') and resource ('memory'), making the functionality explicit. However, it doesn't distinguish from sibling tools like 'remember' (which presumably stores memories) or 'forget' (which likely removes them), so it doesn't fully differentiate from alternatives.

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

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

The description provides clear context on when to use the tool: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also explains the conditional usage based on the key parameter ('omit key' to list all). However, it doesn't explicitly state when not to use it or name alternatives (e.g., 'remember' for storing), so it lacks full exclusion 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?

Despite no annotations, the description explains the parallel fan-out behavior across multiple sources and the return format (structured changes + count + URIs). It lacks details on potential limits or errors but provides sufficient transparency for a read-only 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 concise and well-structured, packing a lot of information into a single paragraph without redundancy. It front-loads the core purpose and then details parameters and return value efficiently.

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 purpose, parameters, behavior, and return value. It could mention any pagination or result limits, but overall it is complete for the tool's complexity and given that 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?

With 100% schema coverage, the description still adds significant value: it explains the 'type' parameter is limited to 'company', provides concrete examples for 'since' (ISO dates and relative formats), and clarifies 'value' accepts ticker or CIK. This goes beyond the schema's brief descriptions.

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

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

The description clearly states it finds what's new about an entity since a point in time, specifying supported types and data sources (SEC EDGAR, GDELT, USPTO). This distinguishes it from siblings like entity_profile and compare_entities, which serve different purposes.

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

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

The description explicitly says 'Use for brief me on what happened with X or change-monitoring workflows,' providing a clear use case. It does not explicitly mention when not to use, but the context and examples effectively guide usage.

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

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence model (authenticated users get persistent memory, anonymous sessions last 24 hours) and the cross-tool context capability. It doesn't mention rate limits, error conditions, or memory size limits, but covers the essential operational behavior well.

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 states the core function, the second adds crucial behavioral context about persistence. No wasted words, and the most important information (what the tool does) is 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 simple 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 key behavioral aspects (persistence model). It doesn't describe return values or error cases, but for this complexity level, it's nearly complete. A perfect score would require mentioning what happens on success/failure.

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). The description doesn't add any meaningful parameter semantics beyond what's in the schema descriptions, which provide examples for 'key' and clarify 'value' accepts 'any text'. 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 siblings by specifying its unique function of storing data, unlike 'recall' (retrieving), 'forget' (deleting), or prediction tools. The description goes beyond the name 'remember' to explain what kind of data is 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 clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), giving practical examples. However, it doesn't explicitly state when NOT to use it or mention alternatives like using 'recall' for retrieval or 'forget' for deletion, which would be needed for a perfect score.

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

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

With no annotations, the description carries full burden. It discloses the output fields (ticker, CIK, company name, URIs) and notes it's a single call replacing multiple lookups. It does not cover error cases or edge conditions, but overall provides adequate transparency.

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

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

Two sentences, concise and well-structured. First sentence states core purpose, second adds specifics and benefits. No unnecessary words.

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

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

Despite no output schema, the description explains the return values sufficiently. It covers input requirements, output structure, and context (v1, replaces multiple calls). Complete for a lookup tool.

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

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

Schema coverage is 100% with descriptions. The description adds value beyond schema by providing input examples and clarifying that type=company for v1. This enriches 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 clearly states the tool resolves entities to canonical IDs, specifies supported type (company), and gives concrete input examples (ticker, CIK, name). It also mentions it replaces multiple calls, distinguishing it from other tools on the server.

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 (entity resolution in one call) and specifies current limitations (v1 only supports company). It does not explicitly state when not to use or compare to alternatives, but no sibling tool performs similar resolution.

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, openWorldHint true, idempotentHint true, destructiveHint false. Description adds behavior: probes each entity, ranks by score, surfaces results, and specifies return fields (score, confidence, signal density). No contradictions.

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

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

Two sentences plus parenthetical, front-loaded with main purpose, every sentence adds value. No superfluous information.

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

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

No output schema, but description covers return format (ranked list with score, confidence, signal density). Explains workflow and input implications. Self-contained for an auditing 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%. Description adds semantics beyond schema: clarifies first entity is treated as subject for narrative, explains models parameter defaults and key requirement. Baseline 3, incremented for added 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?

Clearly states it compares AI visibility across multiple entities side-by-side, uses ai_visibility_check for probing, ranks by score, and surfaces most/least recognized. Distinguishes from sibling 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 usage for competitive AI-marketing audits with an example. Does not explicitly say when not to use, but sibling tools imply alternative use cases (e.g., ai_visibility_check for single entity).

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, but description fully discloses data sources (SEC EDGAR + XBRL), return values (verdict, structured form, actual value with citation, percent delta), and scope (company-financial). No contradictions.

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

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

Two sentences, no wasted words. Front-loaded with purpose, followed by scope, output, and benefit. Efficient and informative.

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

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

Given no annotations and no output schema, the description covers all necessary context: what it does, how it works, what it returns, and when to use. Fully prepares an agent for selection and invocation.

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 description adds value by giving examples of valid claims and specifying the acceptable domains and metrics, going beyond the schema's parameter description.

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

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

Specific verb+resource (fact-check a natural-language claim against authoritative sources) with clear domain (company-financial for public US companies) and distinct from siblings like 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 when to use: fact-checking claims, especially company-financial. States limitations (v1 supports only revenue/net income/cash for US public companies). Replaces sequential agent calls, implying efficiency.

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