qrcode

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

The description adds behavioral details beyond annotations, such as the cost implication for Anthropic probes and the return structure (score, confidence, signals, raw_response per model). Annotations already indicate a read-only, idempotent, non-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 concise and front-loaded with the core purpose. It includes essential details without redundancy, making it easy to parse.

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

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

Given the lack of an output schema, the description explains the return format and covers key behavioral aspects like costing and model selection. It is sufficiently complete for an agent to use correctly.

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

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

All 4 parameters have schema descriptions (100% coverage). The description adds examples and clarifies parameter roles, e.g., '_apiKey' is optional and passed through to Anthropic, 'context' disambiguates entities.

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 multiple LLMs to score visibility (0-100) for a given entity. It distinguishes from sibling tools like 'entity_profile' or 'ask_pipeworx' by focusing on multi-model scanning and 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 lists use cases (AI-marketing audits, pre-launch checks, competitive monitoring) and explains when to provide an API key and specify models. It lacks explicit 'when not to use' 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?

With no annotations provided, the description carries full burden. It discloses key behavioral traits: the tool picks the right data source and fills arguments automatically, handles natural language questions, and returns results. However, it doesn't mention limitations like rate limits, authentication needs, or error handling, leaving some gaps in 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 front-loaded with the core functionality, followed by supporting details and examples. Every sentence earns its place: the first defines the tool, the second explains the automation, the third provides usage guidance, and the examples illustrate practical applications. No wasted words.

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

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

Given the tool's complexity (natural language processing to select data sources) and lack of annotations/output schema, the description does well by explaining the automation process and providing examples. However, it doesn't detail return formats or potential limitations, which could be important for an agent to use it effectively.

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

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

The input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the parameter's purpose: 'Your question or request in natural language' and providing concrete examples ('What is the US trade deficit with China?', etc.). This enhances understanding 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 from data source'), and distinguishes from siblings by emphasizing natural language input without needing to browse tools or learn schemas. The examples further clarify the scope.

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 contrasts with sibling tools like 'discover_tools' or 'recall' by positioning it as a high-level query interface. It provides clear alternatives (implied: use other tools for specific structured operations).

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

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

Annotations already indicate read-only, open-world, and non-destructive behavior. The description adds significant behavioral context: it resolves markets, classifies bets, fans out to appropriate data packs, and returns an evidence packet with a comparison. No contradictions.

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

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

The description is a single paragraph that front-loads the purpose and provides necessary details. While it is relatively long, every sentence adds value without redundancy, making it efficient for its 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?

Covers inputs, process, and output (evidence packet plus comparison). Lacks explicit output schema, but the description adequately communicates expected returns. Given tool complexity, it is sufficiently complete for an agent to invoke correctly.

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

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

Schema coverage is 100% with descriptions for both parameters. The description enriches these by explaining the 'depth' enum (quick vs thorough) with source counts and clarifying the 'market' parameter can be a slug, URL, or question text, adding context beyond the schema.

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

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

The description clearly states it researches Polymarket bets by pulling Pipeworx data, specifying input formats (slug, URL, question text) and the process of resolving, classifying, and gathering evidence. It distinguishes itself from siblings like 'ask_pipeworx' by being specialized for 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?

Explicitly lists use cases ('should I bet on X?', 'what does data say?') and positions itself as the core demo product. While it doesn't explicitly state when not to use it, the comparison with 'ask_pipeworx' provides implicit guidance.

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

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

No annotations are provided, so the description carries full burden. It discloses data sources (SEC EDGAR, FDA, clinical trials) and return type (paired data + URIs), but does not mention potential errors, rate limits, or data freshness. Adequate but not rich.

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 three sentences, with no filler. The core purpose is front-loaded, and every sentence provides necessary information.

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

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

Given the tool's simplicity (2 params, no output schema), the description covers purpose, parameters, and return type fairly completely. It could specify output format more precisely, but is sufficient for an agent.

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

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

Schema description coverage is 100%, so parameters are already explained. The description adds value with concrete examples (e.g., 'AAPL','MSFT' for companies) and clarifies the meaning of 'values' based on type, which aids selection.

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

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

The description clearly states the tool's purpose: 'Compare 2–5 entities side by side in one call.' It enumerates two distinct entity types with specific data fields for each, and notes it replaces multiple sequential calls, distinguishing it from sibling tools.

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

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

The description provides clear context for when to use the tool (for side-by-side comparison) and highlights its efficiency benefit. It does not explicitly state when not to use it or name alternatives, but the sibling tools are all distinct, making usage 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 provided, the description carries full burden and does well by disclosing key behavioral traits: it generates QR codes, returns an image URL (not the image itself), and specifies how to use the URL. It doesn't mention rate limits, authentication needs, or error conditions, but covers the core operational behavior adequately for a simple 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?

Two sentences with zero waste: the first states the purpose and output, the second explains how to use the output. Every word earns its place, and the description is appropriately sized for this simple tool.

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

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

For a simple QR code generation tool with no annotations, no output schema, and good schema coverage, the description is nearly complete. It explains what the tool does, what it returns, and how to use the return value. The main gap is lack of explicit guidance versus the sibling tool 'read_qr', but otherwise it provides sufficient 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 both parameters thoroughly. The description adds no additional parameter information beyond what's in the schema (e.g., it doesn't explain 'data' or 'size' further). 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 specific action ('Generate a QR code') and resource ('any text or URL'), distinguishing it from the sibling tool 'read_qr' which presumably reads/decodes QR codes rather than creating them. The verb+resource combination is precise and unambiguous.

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

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

The description implies usage context by mentioning what the tool returns ('Returns the image URL') and how to use the output ('embedded directly in an <img> tag or downloaded'), but doesn't explicitly state when to use this tool versus alternatives or any prerequisites. The existence of 'read_qr' as a sibling suggests this is for creation while that is for reading, but this distinction isn't made explicit in the description.

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 behavior: it's a search operation that returns 'the most relevant tools with names and descriptions.' It doesn't mention rate limits, authentication needs, or error conditions, but for a read-only discovery tool, the description provides adequate 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 and well-structured: two sentences that each earn their place. The first sentence states the core functionality, the second provides crucial usage guidance. There's zero wasted text, and the most important information ('Call this FIRST') 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/discovery 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 or potential limitations (beyond the 500+ tools context). Given the tool's relative simplicity and good parameter coverage, this is mostly complete but could mention output format.

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

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

With 100% schema description coverage, the input schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.

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

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

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resources ('tool catalog'), and explicitly distinguishes it from siblings by emphasizing its role in discovery among '500+ tools available' - a context not relevant to the listed sibling tools (create_qr, read_qr).

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 gives clear conditions for when to use it (large catalog, discovery needed) and implicitly suggests alternatives (direct tool invocation) when those conditions aren't met.

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 carry behavioral info. It mentions returning URIs and that it is a data retrieval tool (presumably read-only), but does not explicitly state side effects, authentication needs, or rate limits. Could be more 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?

Four sentences, no fluff. First sentence front-loads main purpose, second lists contents, third mentions URIs, fourth gives efficiency claim and alternative. Every sentence earns its place.

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

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

No output schema, but description lists data sources and mentions URIs. Missing error handling or behavior on missing entity, but given simple schema and no output schema, it is fairly complete. Could add more about what to expect if entity not found.

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

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

Schema coverage is 100% (baseline 3). Description adds valuable context: explains type enum (only 'company'), value can be ticker or CIK, and explicitly states names are not supported, directing to resolve_entity. This improves understanding beyond schema.

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

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

Description clearly states 'Full profile of an entity across every relevant Pipeworx pack in one call' and lists specific data sources (SEC, XBRL, USPTO, GDELT, LEI). It distinguishes from siblings by noting it replaces 10-15 calls and recommends usa_recipient_profile for federal contracts.

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

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

Explicitly says when to use (full profile in one call) and when not (federal contracts -> usa_recipient_profile). Also advises to use resolve_entity if only a name is available.

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 has side effects. For a destructive tool with zero annotation coverage, this is insufficient 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 communicates the core functionality without any wasted words. It's appropriately sized for a simple deletion tool and is front-loaded with the essential information.

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

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

For a destructive operation tool with no annotations and no output schema, the description is incomplete. It doesn't address what happens after deletion, whether there are confirmation requirements, what errors might occur, or how this tool relates to sibling memory operations. The minimal description leaves significant gaps in 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?

The schema description coverage is 100%, with the single parameter 'key' clearly documented as 'Memory key to delete'. The description adds no additional parameter information beyond what's already in the schema, so it meets but doesn't exceed the baseline expectation for tools with complete schema documentation.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'recall' or 'remember' that might also interact with stored memories, preventing a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'recall' and 'remember' that likely interact with stored memories, there's no indication of when deletion is appropriate versus retrieval or creation, leaving the agent without contextual usage information.

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

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it fetches the page, extracts title/description/key links, and outputs a text blob ready for placement. No contradictions. Could mention error handling, but overall sufficient.

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 tight: two sentences for purpose, then a bullet-like 'Useful for' list. No unnecessary words. Could be slightly shorter, but well-structured.

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

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

Given the tool's simplicity (no output schema, straightforward params), the description covers the core behavior, output format, and use cases. No significant gaps for an AI agent to understand 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% with descriptions for both parameters. The description doesn't add significant new info about parameters beyond restating 'any URL' and implying link extraction. 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 starts with a clear verb+resource: 'Generate a production-ready llms.txt file for any URL.' It specifies the target for AI crawlers and the output format, leaving no ambiguity about the tool's 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 'Useful for:' section explicitly lists three distinct use cases (client indexing, personal project drafting, competitor auditing). While it doesn't state when not to use it, the context is clear enough for most scenarios.

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 the rate limit and marks the tool as 'free'. However, it does not describe any side effects (e.g., whether feedback is logged, response times, or confirmation). The description adds some behavioral context beyond the schema but lacks detail on outcomes or consequences.

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 front-loaded with the purpose, followed by usage guidelines and rate limit. Each sentence serves a purpose, and there is no redundant information. Could be slightly more structured, but it is clear and efficient.

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

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

The tool is simple and has no output schema, so the description does not need to explain return values. It adequately covers the necessary context: purpose, usage instructions, and rate limit. The completeness is high for a feedback tool, though it could mention expected response behavior (e.g., no confirmation).

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, so the baseline is 3. The description adds guidance on the 'message' parameter (do not include verbatim prompts), which provides some extra semantics. For other parameters (type, context), the schema already explains them well. The overall added value is marginal but non-zero.

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: sending feedback to the Pipeworx team, listing specific use cases (bug reports, feature requests, missing data, praise). It is a distinct function with no overlap among siblings, so it is easy for an agent to select this tool for feedback-related 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 clear guidance on when to use the tool (for feedback) and gives explicit instructions on how to format the message (describe what you tried in Pipeworx tools/data, avoid including the user's prompt verbatim). It also mentions the rate limit of 5 messages per day per identifier. While it doesn't explicitly state when not to use it, the unique purpose makes this clear enough.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context: data is self-aggregating from CF analytics engine, no PII included, just counts, and cached 5min-1h depending on window. 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 (three sentences) and well-structured: first states the main output, then lists use cases in a numbered list, then adds technical details. Every sentence contributes value without redundancy.

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

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

Given one simple parameter, no required fields, rich annotations, and no output schema, the description covers all necessary aspects: what data is returned, data origin, privacy, caching behavior. It is fully complete for an agent to decide when and how to invoke this tool.

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

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

The schema already provides a full description and enum for the window parameter. The description adds semantic guidance on how window length affects results ('shorter windows surface what's hot right now; longer windows show steady-state demand'), 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 explicitly states it returns top tools, top packs, and total call volume over selectable windows. It lists three concrete use cases, making the tool's purpose very clear and distinct from siblings like ask_pipeworx or 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?

The description provides three specific scenarios for when to use the tool (discovering hot data sources, confirming canonical choice, checking alignment). However, it does not explicitly mention when not to use it or compare to alternative tools on the server.

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

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

The description discloses its read-only nature (consistent with annotations) and adds behavioral details: it searches, groups, checks monotonicity, and returns ranked opportunities with trade suggestions. No contradictions with annotations.

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

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

The description is well-structured with a clear opening sentence, two paragraphs for modes, and a closing sentence on output. It is slightly verbose but each sentence adds value; 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 explains the return value (ranked opportunities with reasoning and direction). It covers both modes, prerequisites, and cross-event use cases. It is sufficiently complete for an agent to decide and invoke correctly.

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

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

Schema coverage is 100%. The description adds examples and clarifies the role of each parameter (event slug vs topic), enriching the schema's brief descriptions. It does not add syntax details but compensates with use-case 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 clearly states the tool finds arbitrage opportunities via monotonicity violations, with two explicit modes (event and topic). It is specific about the resource (Polymarket) and action, but does not differentiate from sibling tools 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?

The description provides clear guidance on when to use each mode, including an example of cross-event mode catching cases single-event mode misses. It lacks explicit 'when not to use' or alternatives, but the modes serve as a starting point.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, which the description reinforces by describing a non-mutating analysis. It adds behavioral context: uses a lognormal model from FRED and live coinpaprika price, scans top markets, groups by asset, and computes model probability. 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 about 100 words, front-loaded with purpose, then methodology, then use case. It is efficient but slightly verbose in explaining the model. Every sentence contributes, but could be 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?

Given no output schema, the description explains the return format: top N ranked by edge magnitude with suggested trade direction. It covers how the tool works, its inputs, and its intended use, making it complete for a tool of this complexity with safe annotations.

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

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

Schema coverage is 100% with descriptions for limit, window, and min_edge_pp. The description does not add significant meaning beyond the schema; for example, it restates defaults mentioned in the schema. Baseline 3 applies as schema already provides sufficient information.

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 highest-volume Polymarket markets and returns those with the largest disagreement between Pipeworx data and market price. It uses specific verbs (scan, return, rank) and resource (Polymarket markets), and distinguishes from sibling tools like polymarket_arbitrage by focusing on edge detection for a specific use case.

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 the tool is built for the 'what should I bet on today' question, helping agents discover opportunities without manual browsing. This provides clear when-to-use guidance, though it does not mention when to avoid or provide alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true and destructiveHint false. The description adds valuable behavioral context: it describes the two modes, explains that explicit parameters override topic mappings, and details the return structure (prices and spreads). This goes 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 compact and front-loaded with purpose, but it is a single dense paragraph. Breaking into sections (modes, returns) could improve scannability. Nevertheless, every sentence adds value without redundancy.

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

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

Given no output schema, the description fully details the return values: per-venue leg prices as 0-1 probabilities and the spread in percentage points. For a tool with three optional parameters and clear modes, all key context is provided for correct invocation and interpretation.

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 tool description adds significant meaning: it explains that 'topic' provides pre-mapped shortcuts for common events, and that explicit ticker/slug override the topic-mapped sides. This clarifies the interdependency and usage intent beyond the schema descriptions.

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

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

The description clearly defines the tool as a cross-venue spread calculator between Kalshi and Polymarket for the same event. It specifies two distinct modes (topic shortcuts and explicit pairing) and explains the arb signal, distinguishing it from sibling tools like polymarket_arbitrage which may operate on different venues or data sets.

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

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

The description explains when to use the tool: to exploit real arbitrage signals between Kalshi and Polymarket with typical spreads. It provides clear guidance on the two invocation modes and how to choose between them, but does not explicitly list scenarios where the tool should not be used or directly compare to sibling tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool decodes QR codes and returns text, which covers the basic operation, but lacks details on error handling (e.g., invalid URLs, non-QR images), rate limits, or authentication needs. It adds some value but not rich behavioral context.

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

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

The description is a single, efficient sentence that front-loads the purpose ('Decode a QR code') and includes key details (source and output) without any wasted words. Every part of the sentence earns its place, making it highly concise and well-structured.

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

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

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is mostly complete: it states the action, input requirement, and output. However, it could be more complete by addressing potential errors or constraints, slightly lowering the score from 5.

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 'url' parameter fully documented in the schema itself. The description mentions 'publicly accessible image URL,' which aligns with the schema but does not add significant meaning beyond it. 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 specific action ('Decode a QR code') and resource ('from a publicly accessible image URL'), with the verb 'Decode' distinguishing it from the sibling tool 'create_qr' which presumably creates QR codes. It precisely communicates what the tool does without being vague or tautological.

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

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

The description provides clear context for when to use this tool: when you have a publicly accessible image URL containing a QR code that needs decoding. However, it does not explicitly mention when not to use it or name alternatives (e.g., using 'create_qr' for generation instead), which prevents a score of 5.

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

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

With no annotations, the description carries full burden and does well by disclosing key behaviors: it retrieves stored memories, supports listing all keys when key is omitted, and works across sessions. It doesn't mention error handling or performance limits, but covers core functionality adequately.

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

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

Two sentences, front-loaded with core functionality and followed by usage context. Every word earns its place: no redundancy, clear structure, and efficient communication of key information.

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

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

For a simple tool with one parameter and no output schema, the description is nearly complete: it explains purpose, usage, and parameter semantics. It lacks details on return format or error cases, but given low complexity, this is sufficient 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?

The schema has 100% coverage for the single parameter, so baseline is 3. The description adds value by explaining the semantic effect of omitting the key ('omit to list all keys'), which clarifies the tool's dual behavior beyond the schema's technical 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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings by mentioning retrieval of saved context, unlike tools like 'remember' (store) or 'forget' (delete).

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

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

It provides explicit guidance on when to use this tool: 'to retrieve context you saved earlier in the session or in previous sessions'. It also specifies when to omit the key parameter ('omit key to list all keys'), offering clear usage rules without alternatives needed here.

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. It discloses parallel execution across three sources, the return structure (structured changes, count, URIs), and the acceptable formats for the 'since' parameter. However, it does not mention rate limits, authentication needs, or any potential side effects.

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

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

The description is remarkably concise at two sentences, yet it covers the core purpose, the multi-source fan-out, parameter details, and return format. Every sentence provides valuable information without redundancy.

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

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

Given that no output schema exists, the description adequately explains the return structure (structured changes, count, URIs). It covers all parameters and gives examples. It does not address potential edge cases or limitations (e.g., only company type), but overall it is sufficient for an AI agent to invoke the tool correctly.

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

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

The input schema has 100% coverage with descriptions for each parameter. The description adds extra value by explaining ISO vs. relative formats for 'since' and recommending '30d' for typical monitoring, and clarifying that 'value' can be a ticker or CIK. 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 clearly states the tool's purpose: retrieving recent changes for an entity since a given time. It specifies the supported entity type (company) and explains the parallel fan-out to multiple sources (SEC EDGAR, GDELT, USPTO), which distinguishes it from sibling tools like entity_profile or compare_entities.

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

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

The description explicitly states use cases: 'brief me on what happened with X' or change-monitoring workflows. It also explains parameter formats (ISO date or relative), but does not provide when-not-to-use guidance or mention alternative sibling tools, limiting 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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation ('Store'), specifies persistence differences ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), and implies it's for session-scoped data. However, it doesn't mention potential limitations like storage size, rate limits, or error conditions, which would be helpful for a mutation tool.

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

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

The description is appropriately sized and front-loaded, with two sentences that efficiently convey purpose, usage, and behavioral details without waste. Every sentence adds value: the first defines the core function, and the second adds critical context about persistence. No redundant or vague phrasing is present.

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

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

Given the tool's moderate complexity (a write operation with persistence nuances), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, and key behavioral aspects like authentication differences. However, it lacks details on return values or error handling, which would be beneficial since there's no output schema to compensate.

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

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

Schema description coverage is 100%, with both parameters ('key' and 'value') well-documented in the schema. The description adds minimal semantic value beyond the schema—it mentions what can be stored ('findings, addresses, preferences, notes') but doesn't provide additional syntax, constraints, or examples. This meets the baseline of 3 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 ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from siblings like 'recall' (likely for retrieval) and 'forget' (likely for deletion). It explicitly mentions what can be stored ('intermediate findings, user preferences, or context across tool calls'), 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 provides clear context for when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly state when not to use it or name alternatives. For example, it doesn't clarify if 'recall' is the complementary retrieval tool or how it differs from other storage mechanisms, leaving some usage decisions implicit.

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 the burden. It discloses the input format and output fields, but omits behavioral traits like idempotency, authentication needs, or error handling. It is adequate but not fully 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 no unnecessary words. Two front-loaded sentences efficiently convey purpose, input, output, and versioning.

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

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

For a simple tool with two parameters and no output schema, the description adequately explains the output. It lacks details on edge cases or limitations but is generally complete for its complexity.

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

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

Schema covers 100% of parameters with descriptions. The description adds value by providing concrete examples (e.g., 'AAPL', '0000320193') and clarifying that value can be a ticker, CIK, or name, enhancing meaning beyond the schema.

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

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

The description clearly states the tool's function: resolving an entity to canonical IDs. It specifies the entity type ('company' v1) and input formats (ticker, CIK, name), making it distinct from siblings like ask_pipeworx or create_qr.

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

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

The description mentions that the tool replaces 2–3 lookup calls, implying efficiency. However, it does not explicitly state when not to use it or compare to alternatives, leaving some guidance implicit.

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 safe read/idempotent behavior. The description adds that it probes each entity with ai_visibility_check and returns a ranked list with specific fields, which goes beyond annotations.

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

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

Every sentence is informative: purpose, process, use case, input handling, output format. 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?

No output schema, but description fully explains the return format (ranked list with score, confidence, signal density). Combined with rich annotations, the tool is completely described.

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

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

Schema coverage is 100%, but the description adds value by explaining that the first entity is treated as the 'subject' and the rest as competitors, which is not in the schema.

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

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

The description clearly states the tool compares AI visibility across multiple entities, using ai_visibility_check internally, and ranks results. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (likely other metrics).

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 use case ('competitive AI-marketing audits') and context ('does Claude know about us as well as our competitors?'). However, it does not explicitly state when not to use or name 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 fully carries the burden of behavioral disclosure. It discloses the sources used (SEC EDGAR + XBRL), the return format (verdict, structured form, actual value with citation, percent delta), and the domain limitations. It does not mention rate limits, authorization needs, or latency, but for a read-only fact-checking tool against public data, this is acceptable. 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 and front-loaded with the primary purpose. Every sentence adds value: defining the tool, specifying the supported domain, listing outputs, and highlighting its efficiency improvement. No redundant 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 a single simple parameter and no output schema, the description is complete. It explains what it does, its scope, the outputs (verdict, structured form, actual value, delta), and why it is useful. All necessary context for an agent to decide when and how to use it is present.

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 for the single parameter 'claim', providing clear examples. The tool description reinforces the domain but does not add significant new semantic information beyond what the schema already provides. The baseline 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 the tool's purpose: fact-checking natural-language claims against authoritative sources, specifically company-financial claims for public US companies. It names the verb 'Fact-check', the resource 'authoritative sources' (SEC EDGAR + XBRL), and distinguishes itself from siblings by specifying its domain and that it replaces multiple 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 explicitly states that the tool replaces 4-6 sequential agent calls for NL parsing, entity resolution, data lookup, and comparison, which provides a clear usage context. It also specifies the supported claim types (company-financial for public US companies), implicitly indicating when not to use it (for non-financial claims). However, it does not explicitly list alternative tools for other use cases.

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