Pkg Go Dev

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

Annotations already declare read-only, open-world, idempotent, non-destructive. The description adds critical behavioral details: default model (Workers AI Llama-3.3-70b free), cost implication for Anthropic (BYO key), and return structure. 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?

Four sentences with no wasted words: first sentence states core function, second clarifies defaults and cost, third details output, fourth gives use cases. Front-loaded and efficient.

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

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

All 4 parameters are explained, return shape is described (per-model fields and combined view), and use cases are provided. No output schema exists, but description compensates fully. 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%, but the description adds value by clarifying default model, optional API key usage, and context disambiguation. This exceeds the baseline of 3 for high coverage.

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

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

The description uses specific verbs ('Probe', 'score') and clearly defines the resource (LLMs) and output (visibility scores 0-100). It distinguishes from siblings like 'scan_competitor_ai_presence' by being more general and focused on 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?

States explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'). Does not explicitly exclude alternatives, but context is sufficient for an agent to decide when to invoke.

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 mentions routing across many sources and that Pipeworx 'picks the right tool, fills arguments, returns the result.' However, it does not disclose potential failure modes, rate limits, or authentication requirements, leaving some behavioral gaps.

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

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

The description is a single paragraph that gets to the point quickly. It includes examples that aid understanding without excessive verbosity. Slightly longer than necessary but still efficient.

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

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

For a single-parameter tool with no output schema or annotations, the description is reasonably complete. It explains the tool's function, routing behavior, and gives concrete examples. It does not describe the output format, but that is often inferred from the natural language response.

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

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

Schema coverage is 100% with a clear parameter description. The description adds examples and context (e.g., 'What is the US trade deficit with China?') but does not significantly extend beyond the schema's 'Your question or request in natural language'.

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: 'Answer a natural-language question by automatically picking the right data source.' It provides specific examples and distinguishes from sibling tools that are more specialized (e.g., compare_entities, entity_profile).

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

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

The description explicitly instructs when to use: 'Use when a user asks... and you don't want to figure out which Pipeworx pack/tool to call.' This provides clear context and implies alternatives for more specific queries.

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, safe operation. Description elaborates on process: resolves market, classifies bet, fans out to packs, and returns evidence + 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?

Four-sentence paragraph that is front-loaded with purpose, then process, then use cases. No wasted words; each sentence adds 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 no output schema, description adequately explains return type (evidence packet + comparison). Covers input variations, classification, and purpose. Sufficient for the tool's complexity.

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

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

Schema covers both parameters with descriptions. Description adds value by explaining market parameter accepts slug, URL, or question text, and mentions depth defaults to thorough. This provides extra context beyond schema.

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

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

Clearly defines verb 'Research' and resource 'Polymarket bet'. Explains it pulls Pipeworx data, resolves market, classifies bet, and returns evidence. Distinguishes from siblings by being the core demo product 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 states use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. Implicitly contrasts with discovering packs manually, but lacks explicit when-not or 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?

No annotations provided. Description discloses data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs) and return format (paired data + citation URIs), and mentions it consolidates many calls. Sufficiently 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?

Description is fairly long but well-structured with clear sections for each type. Efficiently communicates key information, though slightly verbose.

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

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

No output schema, but description explains return format (paired data + URIs). Covers purpose, data sources, and types completely for a comparison tool.

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

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

Schema coverage is 100% but description adds significant meaning: explains what each 'type' pulls (revenue, net income, etc. for company; adverse events, approvals, trials for drug) and details 'values' format (tickers/CIKs vs drug names).

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 companies or drugs side by side. It specifies the verb 'compare' and the resources, and distinguishes from siblings by noting it replaces 8-15 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?

Explicitly lists trigger phrases like 'compare X and Y' and 'X vs Y', and explains when to use each type (company vs drug). It lacks explicit when-not guidance but effectively covers usage context.

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

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

No annotations are provided, so the description carries the full burden. It states the tool returns 'the top-N most relevant tools with names + descriptions', which is sufficient for a read-only discovery tool. However, it lacks details about default behavior or any potential side effects, but for this purpose it is adequate.

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

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

The description is a single sentence that fronts the purpose with a list of domains. While it is somewhat long, it is well-structured and informative without being verbose. Every part 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 the tool's simplicity and the richness of the schema, the description is complete: it explains the purpose, usage context, and return value. No output schema exists, but the description covers what the tool returns.

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 minimal value beyond the schema; it mentions 'top-N' but does not elaborate on parameter semantics beyond what the schema already 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 that the tool finds tools by describing the data or task, using specific verbs like 'browse', 'search', 'look up', and 'discover'. It distinguishes itself from sibling tools by focusing on tool discovery, which is unique among the listed siblings.

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 when you need to browse, search, look up, or discover what tools exist for...' and advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear context and 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?

With no annotations, description carries full burden. It discloses return data types (filings, fundamentals, patents, news, LEI) and input constraints. Does not mention side effects or rate limits, but remains sufficiently transparent for a read 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?

Description is informative but slightly long. Front-loads the main purpose and provides examples. Every sentence adds value, though could be trimmed slightly.

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

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

Despite no output schema, description lists all return categories and mentions pipeworx:// citation URIs. Covers input constraints and hints at future types. Complete for a complex aggregator 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%, but description adds key context: type is limited to 'company', value must be ticker or CIK, and names are not supported. This goes 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?

Description clearly states 'Get everything about a company in one call' and provides specific user query examples. It distinguishes itself from sibling tools like resolve_entity and compare_entities by listing the aggregated data types.

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

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

Explicitly provides when-to-use criteria with example queries. Advises against using with names and directs to resolve_entity instead. References alternative approach of calling multiple 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?

Clearly states deletion behavior, which aligns with the implied destructive nature. No annotations provided, but description is straightforward; could add note on irreversibility but sufficient for a simple delete.

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 concise sentences front-loading the verb and resource, plus a third sentence for pairing guidance. No unnecessary information.

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

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

For a simple tool with one parameter and no output schema, description covers purpose, usage context, and sibling pairing completely.

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

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

Only one parameter 'key' with schema coverage 100%. Description in input schema already defines it as 'Memory key to delete', so the tool description adds no new semantic value.

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

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

The description uses a specific verb ('Delete') and resource ('memory by key'), clearly distinguishing it from sibling tools 'remember' and 'recall'.

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

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

Explicitly states when to use ('context is stale, the task is done, or want to clear sensitive data') and names sibling tools to pair with ('Pair with remember and recall').

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

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false. The description reinforces this by stating it fetches and emits output without side effects. It adds context about the output format and standard location, but could mention size limits or behavior for large sites beyond the max_links default.

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: two sentences covering functionality, output, and use cases. Every sentence adds value with no repetition or filler. Front-loads key action and purpose.

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

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

Given the simple tool with 2 parameters and no output schema, the description explains output format, use cases, and how to use the result. It is sufficient for an agent to understand when and how 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 covers 100% of parameters with descriptions. The tool description does not add significant new meaning beyond the schema descriptions for 'url' and 'max_links'. The baseline of 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the action ('generate'), the resource ('llms.txt file'), and the specific function ('Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format'). It is distinct from sibling tools like 'ai_visibility_check' or 'scan_competitor_ai_presence' 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 provides explicit use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' It does not specify when not to use or name alternatives, but the contexts are clear and helpful.

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 to compensate; description only hints at read operation (raw contents) but lacks details like error behavior or format.

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 concise sentences with purpose upfront; 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?

Adequate for a simple retrieval but no output schema or behavioral details; could specify return 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?

Schema covers both parameters with basic descriptions; description adds no extra meaning beyond schema.

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

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

Clearly states it retrieves raw go.mod contents for a version, distinguishing from siblings like get_module_info that might provide structured info.

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?

Suggests use for dependency analysis but does not mention alternatives or exclusion conditions among siblings like get_module_info.

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 are empty, so the description carries the full burden. It adds context that the tool returns 'resolved version, commit time, origin' beyond the schema. However, it does not explicitly state that this is a read-only operation or any 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?

Single concise sentence that is front-loaded and contains no redundant information. Every word earns its place.

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

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

For a simple retrieval tool with 2 paramters, the description is somewhat complete, but it lacks description of the output structure (since no output schema is provided). The mention of three fields helps but could be more thorough.

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

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

Schema coverage is 100% with both parameters having descriptions. The description does not add additional meaning beyond the schema for the parameters. Baseline 3 is appropriate.

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

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

The description clearly states it returns metadata for a specific version including resolved version, commit time, and origin. It implicitly distinguishes from siblings like list_versions (which lists versions without metadata) and get_go_mod (which gets a file), but could be more explicit with the verb 'retrieve'.

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

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

No guidance on when to use this tool versus siblings such as latest_version or list_versions. The description does not mention prerequisites or 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, the description bears full responsibility for behavioral disclosure. It only states the output (most recent version + resolved time) but does not mention caching, network calls, error handling (e.g., module not found), rate limits, or whether the tool is read-only. The lack of any such detail reduces 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 a single, clear sentence that conveys the essential purpose and output. Every word is meaningful, no redundancy. It is well front-loaded with the 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?

Given the tool's simplicity (one parameter, no output schema), the description covers the basic function. However, it lacks details on the output format, potential errors, and behavior in edge cases (e.g., no releases). For a tool with no annotations, slightly more completeness could improve usability.

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 the single parameter 'module_path' with description 'Module import path'. The tool description does not add additional context, such as format examples or validation requirements. Baseline 3 is appropriate as the schema already provides clear parameter meaning.

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

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

The description clearly states that the tool returns 'the most recent released version of a Go module' including the resolved time. This effectively distinguishes it from siblings like list_versions (likely returns all versions) and get_module_info (likely returns detailed info for a given version). The verb and resource are specific, avoiding tautology.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention scenarios, prerequisites, or exclusions (e.g., when the module has no releases). Compared to siblings, there's no clarification that list_versions should be used for non-latest versions.

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 full burden. It states the action but does not disclose any behavioral traits such as read-only nature, caching, rate limits, or error handling. The agent learns nothing beyond the basic action.

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

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

The description is a single concise sentence with no wasted words. However, it could be expanded slightly to include more context without becoming verbose.

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 covers the core purpose but omits details about the return format, potential errors, or pagination. It is minimally adequate but not complete.

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

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

The input schema has 100% description coverage for the single parameter. The description adds no extra meaning beyond the schema, so a baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'List' and the resource 'all published versions of a Go module'. It is specific and distinguishes from siblings like 'latest_version' and 'get_module_info', but could be improved by specifying the return format.

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

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

No guidance is given on when to use this tool versus alternatives, nor are there prerequisites or exclusions mentioned. The description is a single sentence that gives no usage context.

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

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

With no annotations provided, the description carries full responsibility. It discloses rate limits (5 per identifier per day), that it's free and doesn't count against quota, and how feedback is processed (digests read daily, affecting roadmap). No contradictions with any 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, then guidelines, then constraints. It's fairly concise but could be slightly tighter; however, every sentence earns its place.

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

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

Given the tool's simplicity (3 parameters, no output schema), the description covers all necessary aspects: purpose, usage, constraints, and message content guidance. It is complete for an agent to select 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%, but the description adds value by explaining the 'context' object (e.g., pack slug, tool name, vertical) and guiding the 'message' content (be specific, 1-2 sentences, 2000 chars max). This enriches the schema definitions.

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 is for informing the Pipeworx team about bugs, missing features, data gaps, or praise, using specific verbs and resources. It distinguishes itself from sibling tools like 'ask_pipeworx' or 'discover_tools' by focusing on feedback rather than inquiries or exploration.

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

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

The description explicitly states when to use the tool (e.g., for bug reports, feature requests, praise) and what not to do (avoid pasting end-user prompts). It also provides context on rate limits and that it's free, guiding the agent on appropriate usage and alternatives.

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

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

Annotations declare readOnlyHint, idempotentHint, etc. The description adds value by disclosing caching behavior (5min-1h), data source (CF analytics-engine), and that no PII is collected. 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 concise (3 sentences plus a parenthetical note) yet packs in purpose, use cases, and technical details. No filler; every sentence adds value.

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

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

Given no output schema, the description mentions return format (top tools, top packs, count) and aggregation nature. It covers caching and data source, providing adequate context for an agent to understand the output. Slightly lacking on exact structure but sufficient.

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

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

Schema coverage is 100% with parameter description. The description enhances it by explaining the trade-off between shorter and longer windows (what's hot vs. steady-state), which adds practical context beyond the enum values.

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, top packs, and total call volume' for a recent window, distinguishing it from sibling tools like 'ask_pipeworx' and 'discover_tools'. The verb 'Returns' and specific resources make purpose unambiguous.

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

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

Three explicit use cases are provided (discovering hot data sources, confirming canonical choice, aligning use case). It also explains caching and aggregation. However, it doesn't explicitly state when not to use it or compare to alternatives, though the context 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 indicate read-only and non-destructive behavior. The description adds detailed behavioral context: it walks child markets, extracts dates/thresholds, and reports violations. 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 concise yet comprehensive, covering the purpose, logic, input, and output in a single well-structured paragraph without 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 one parameter, no output schema, and rich annotations, the description fully explains the tool's functionality, including the return format, making it 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?

The single parameter 'event' is described with clarity: it accepts a slug or URL, with an example. This adds value beyond the schema description, which already covers 100% of parameters.

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 within a Polymarket event by checking monotonicity violations, with a specific rule and example. It distinguishes itself from sibling tools by focusing on arbitrage detection.

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 how to use it (pass an event slug or URL) and what it does, but does not explicitly state when not to use it or mention alternative tools. The usage context is clear.

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

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

Annotations declare readOnlyHint=true and destructiveHint=false. The description adds rich behavioral details: the V1 model uses lognormal model from FRED and live coinpaprika price, groups by asset, fetches price history once, and returns top N with trade direction. This fully discloses the tool's operation without contradicting annotations.

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

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

The description is a single focused paragraph that front-loads the core action. It is concise but dense with valuable information. Minor improvement could be achieved with more structural elements like bullets, but it remains efficient and readable.

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

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

Given the tool's complexity (multi-step data gathering, model computation), the description covers inputs, algorithm, and outputs well. However, the absence of an output schema means the description must stand alone; it explains the ranked list with edge magnitude and trade direction, which is sufficient for an agent to understand the output, though the exact format is vague.

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%; descriptions for all three parameters (limit, window, min_edge_pp) are present and clear. The tool description does not add new information beyond the schema definitions, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees with market price. It uses specific verbs ('scan', 'return') and a distinct resource ('Polymarket markets'), and differentiates from siblings like polymarket_arbitrage by specifying the algorithm and data sources.

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 it is built for the 'what should I bet on today' question, providing clear usage context. However, it does not explicitly mention when not to use this tool or compare it to sibling tools (e.g., polymarket_arbitrage), which would improve guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description's burden is low. It adds context about the two modes and the return structure (leg-by-leg prices and spread). However, it does not disclose data freshness, potential errors, or how the tool handles mismatches, which would be valuable 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?

The description is front-loaded with the core purpose and modes, then details return format. It is comprehensive without being verbose, though it could be slightly more concise by removing the restatement of the modes. Every sentence adds useful 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 no output schema, the description adequately explains the return values (prices and spread) and units. It covers the main use cases (topic and custom pairs). However, it lacks mention of error handling, rate limits, or behavior when no matching events exist, leaving some gaps for complete 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?

Input schema has 100% description coverage, so baseline is 3. The description adds value by explaining the interaction between topic and explicit parameters (e.g., overriding the topic-mapped side). It also provides concrete examples of topic values and ticker/slug formats, enriching the schema descriptions.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two modes and the return format. However, it does not explicitly differentiate itself from the sibling tool 'polymarket_arbitrage', which also deals with arbitrage, leaving some ambiguity about when to use which.

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

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

The description explains two operational modes (topic vs. explicit ticker/slug) and provides examples. It implies the tool is for detecting arbitrage signals due to venue price differences. However, it lacks explicit guidance on when not to use this tool (e.g., if no matching events exist) and does not mention alternatives or prerequisites like API keys.

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 burden. It explains scoping (by identifier) and implies read-only behavior, but lacks details on errors, rate limits, or destructive hints.

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

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

The description is 4 sentences, front-loaded with the main action, and every sentence provides essential context without redundancy.

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

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

Given the tool's simplicity (one optional param, no output schema), the description is complete: it covers purpose, usage, scoping, and pairing with related tools.

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 optional parameter. The description adds value by explaining the behavior when key is omitted and providing examples of typical keys, going beyond the schema description.

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

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

The description clearly states it retrieves values saved via 'remember' or lists all keys, using specific verbs and resources. It distinguishes from siblings 'remember' and 'forget' by explicitly mentioning them.

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 concrete examples of when to use (target ticker, address, notes) and mentions scoping, but does not explicitly state when not to use or 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?

No annotations are provided, so the description carries the full burden. It discloses the parallel fan-out to three sources, parameter formats, and return structure, but does not mention latency, error handling, or rate limits, which would be valuable for a tool querying external APIs.

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 purpose and usage. It is reasonably concise but contains slight redundancy in listing multiple example queries. Overall 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 no output schema, the description covers return structure. Parameters are well-documented. For a tool of moderate complexity (3 params, no nested objects), it provides sufficient context for typical use, though missing details on error handling or latency.

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 covers 100% of parameters with descriptions, and the description adds value by explaining the `since` parameter formats (ISO date or relative shorthand) and providing typical usage ('30d', '1m'). This enriches understanding beyond the schema alone.

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

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

The description clearly states the tool retrieves recent changes for a company from multiple sources (SEC, GDELT, USPTO). It provides example user queries and distinguishes itself from sibling tools by its temporal focus and multi-source fan-out.

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

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

The description includes explicit 'use when' examples and covers typical monitoring scenarios. However, it does not explicitly state when not to use it or mention alternatives, but the context is sufficient for an 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?

Despite no annotations, the description fully discloses behavior: memory is scoped by identifier, authenticated users get persistent memory, anonymous sessions retain for 24 hours, and stores key-value pairs. 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?

Description is concise and well-structured: front-loads purpose, then usage, then details. Every sentence adds value with no 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?

Tool complexity is low, schema covers parameters, no output schema needed. Description covers purpose, usage, persistence, and pairing with siblings, making it complete for an AI 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 has 100% coverage, but description adds value by providing key naming conventions and value types, going beyond the schema's basic descriptions. Baseline 3, plus extra context justifies 4.

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

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

The description clearly states the tool's purpose: save data for reuse later across conversations or sessions. It uses specific verb 'Save' and resource 'data', and distinguishes itself from sibling tools like recall and forget.

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

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

Explicitly tells when to use the tool: when discovering something worth carrying forward, with examples like resolved ticker, target address, etc. Also mentions scoping by identifier and persistence duration.

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 that the tool returns IDs plus citation URIs, lists specific ID systems (CIK, ticker, RxCUI, LEI), and gives examples. It implicitly indicates a read-only operation but doesn't discuss error conditions or rate limits, though that's acceptable for a simple lookup 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 yet comprehensive: front-loaded purpose, usage guidance, examples, and benefit—all in a few sentences with no unnecessary words. It is well-structured and easy to parse.

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

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

The description fully covers what the tool does, why to use it, what inputs are expected (with examples), and what outputs are returned (IDs and citation URIs). Given no output schema, it provides sufficient context for an agent to invoke it correctly.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by providing example values and explaining how to use the 'value' parameter (e.g., 'For company: ticker (AAPL), CIK, or name'), which clarifies acceptable inputs beyond the schema's basic 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: 'Look up the canonical/official identifier for a company or drug.' It specifies the verb 'look up' and the resource 'canonical identifier,' and gives concrete examples (Apple, Ozempic) that differentiate 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 says 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' It also notes it 'Replaces 2–3 lookup calls,' providing clear when-to-use and benefit.

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 (readOnlyHint, idempotentHint, destructiveHint=false) already declare safety. The description adds behavioral context: it reveals the tool calls ai_visibility_check internally, mentions API key requirement for certain models, and clarifies the first entity is treated as the subject. 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 tightly packed sentences: first states purpose, second explains mechanism, third gives a use-case example. No filler words; 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?

Despite no output schema, the description specifies return fields ('ranked list with score, confidence, signal density per entity'). It covers the required and optional parameters adequately. Could mention error handling or limits, but overall sufficient for this tool's complexity.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining that the first entity in 'entities' is the subject for narrative, and that 'models' defaults to workers-ai. This clarifies semantics beyond the raw 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 uses specific action verbs ('Compare', 'Probes', 'ranks', 'surfaces') and clearly identifies the resource ('AI visibility across multiple entities'). It distinguishes from sibling tools like ai_visibility_check by emphasizing side-by-side comparison and competitive audits.

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

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

The description explicitly states the tool is 'useful for competitive AI-marketing audits' and gives a concrete question ('does Claude know about us as well as our competitors?'). It implies alternatives by mentioning ai_visibility_check, but lacks explicit when-not-to-use guidance.

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

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

With no annotations provided, the description carries the full burden. It details return structure (verdict, structured form, actual value with citation, percent delta) and notes it replaces 4-6 sequential calls. It does not disclose potential latency, rate limits, or authentication needs, but for a single-parameter tool, this is acceptable.

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

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

The description is concise, with no unnecessary words. It is front-loaded with the core action ('Fact-check, verify...') and efficiently covers purpose, usage, scope, and return format in a few sentences.

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 only one parameter, no output schema, and no annotations, the description is remarkably complete. It explains the tool's purpose, when to use it, its limitations (v1 supports only company-financial claims), and what the response contains. No critical information is missing.

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

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

The only parameter 'claim' has a schema description with examples. The description adds context beyond the schema by providing natural-language examples of valid claims (e.g., 'Apple's FY2024 revenue...'). Since schema coverage is 100%, a baseline of 3 applies, but the examples boost it to 4.

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

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

The description clearly states the tool's purpose: fact-checking natural-language claims against authoritative sources. It provides specific verb+resource ('validate claim') and includes example queries, making it unambiguous. It also distinguishes itself by noting it replaces multiple sequential calls, providing unique value beyond siblings.

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 when an agent needs to check whether something a user said is true' and gives example phrasings. It also specifies scope (company-financial claims for v1). However, it does not explicitly state when not to use the tool or mention alternative tools for non-financial claims, but the scope limitation is clearly implied.

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