Gsa Perdiem

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

Annotations already declare readOnlyHint=true and idempotentHint=true; the description adds value by explaining the response format (per-model fields + combined view) and cost implications (free vs. BYO key), enriching behavioral understanding beyond annotations.

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

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

The description is concise (4 sentences), front-loaded with the primary action, and every sentence adds necessary 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 no output schema, the description explains return values (per-model objects and combined view) adequately for a tool with 4 parameters and no nested objects.

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

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

Schema description coverage is 100%, and the description adds meaning beyond parameter descriptions by stating default model, free tier, and API key usage details.

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

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

The description uses a specific verb ('probe') and clearly states the resource ('LLMs for visibility'), resulting in a defined scope that distinguishes 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 explicitly lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains API key usage, but does not mention when not to use 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 cover safety and idempotency. The description adds that it returns structured answers with stable citation URIs and routes to many tools. No contradictions, but could mention failure modes or rate limits.

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?

Front-loaded with key guidance, but slightly lengthy. Every sentence adds value, including examples and alternative recommendations.

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 and lack of output schema, the description provides comprehensive context: use cases, alternatives, expected output, and examples. It is complete enough for effective use.

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

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

Schema coverage is 100%, with all aliases documented. The description adds minimal value beyond 'your question in natural language'. Baseline score 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: routes questions to a large set of verified sources for factual answers with citations. It distinguishes itself from siblings like ask_pipeworx_grounded and deep_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 says to prefer over web search for many domains, provides examples of when to use, and explains when to step up to alternative 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?

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds essential context: that the tool is a full working router with optional candidate improvements, currently inactive, so it behaves identically to the stable version. It does not contradict 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 moderately concise and front-loads key information (beta, identical, experimental). It could be slightly shorter, but every sentence adds context about the tool's nature and usage.

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

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

Given the complexity (beta version with potential experimental changes), the description covers the tool's identity, current state, usage guidance, and fallback behavior. No output schema exists, but the description notes identical response shape, which suffices.

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

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

Schema coverage is 100%, and the description notes 'same arguments' as the stable tool. It adds no parameter-specific details beyond what the schema already provides, so the description adds minimal value here.

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

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

The description clearly states it's a beta version of ask_pipeworx, identical but with experimental routing improvements. It specifies the verb 'ask' and the resource 'pipeworx beta', and distinguishes from the stable ask_pipeworx by noting that it uses candidate routing improvements for testing.

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 it exactly like ask_pipeworx when you want the newest routing' and explains that results are compared to decide merges. It implies that this version is for testing experimental features, but does not explicitly contrast with other similar siblings like ask_pipeworx_grounded.

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?

Discloses internal behavior: picks tool from 5245 across 1369 sources, fills args, fetches data, extracts answer only from result, and lists refusal reasons. Annotations already indicate readonly/idempotent; description adds rich context without contradiction.

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

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

Well-structured with key info front-loaded (purpose, refusal reasons). Slightly verbose but every sentence earns its place; could be trimmed slightly without losing meaning.

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?

Completes the picture for high-stakes usage: explains return structure (success/refusal), refusal reasons, and cost tradeoff. No output schema needed given detailed description.

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 description adds minimal value beyond listing aliases. Baseline 3 is appropriate; no additional syntactic or semantic details.

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

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

The description clearly states the tool is a 'Hallucination-resistant answer mode for high-stakes reads,' returns answer with evidence, and distinguishes from ask_pipeworx by noting the extra LLM call and evidence extraction.

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

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

Explicit usage guidance: use when answers will be quoted/cited/acted on and agent must not invent facts (e.g., financial, legal, medical). Prefer ask_pipeworx for casual lookups due to cost.

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

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive, so the tool is safe. The description goes far beyond, detailing fan-out behavior, response shapes, resolver contract (match confidence, alternatives, suggestions), parent event extraction, news fields with fallback, safety for low-confidence matches, dead market handling, illiquid spread warnings, and resolution-rule risk. 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 very long and dense, making it difficult to parse quickly. While it is structured with capitalized section headers, it contains excessive detail that could be external documentation. The core purpose and usage are front-loaded, but the verbosity hurts conciseness. Every sentence has value, but the overall size is too large for an efficient tool description.

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 (multiple classifiers, fan-out, resolver contract, safety), the description covers all critical aspects thoroughly. Without an output schema, the description fully explains return shapes. However, the sheer volume of information might overwhelm users; a more structured or layered approach could improve completeness. Still, it leaves no major gaps.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds significant value by explaining how to use the 'market' parameter (slug, URL, question text), the 'depth' parameter (quick vs thorough with defaults), and the 'include_raw' parameter (when to use true/false and size implications). It also elaborates on output behavior tied to these 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's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and output (evidence packet + comparison). It distinguishes from siblings by framing the use case as 'should I bet on X', aligning with a single-bet research focus, unlike general query tools like ask_pipeworx or edge detectors 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 explicitly states when to use: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It provides clear context and examples. However, it does not explicitly mention when not to use or compare to siblings, though the context implies differentiation. Still, it lacks direct exclusions 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?

Adds behavioral traits beyond annotations: explains handling of off-calendar fiscal years, sorting by primary metric, and return of paired data with citation URIs. Discloses that it replaces 8-15 sequential lookups.

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?

Highly concise but information-dense. Front-loaded with example phrases. 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, description adequately explains return values (sorted data, paired data, citation URIs). Covers sources and edge cases (off-calendar fiscal years).

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 rich context: for type='company' it details specific financial metrics from SEC filings, for type='drug' it lists FAERS counts. Examples given for 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?

Description clearly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call' with specific data sources (SEC EDGAR/XBRL, FAERS). Distinguishes from siblings by explicitly advising preference over sequential lookups.

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

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

Provides explicit example queries and states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Clearly defines when to use the tool.

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

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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description details decomposition into facets, parallel tool routing, depth levels with gap recovery and contradictions, timing expectations, citation format, and semantic excerpting. 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 lengthy but every sentence adds necessary detail. It is front-loaded with key constraints (account required) and expands logically. Could be slightly more concise, but structure is good.

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 two parameters, no output schema, and complex behavior, the description covers return format (findings packet with evidence, confidence, source, fetched_at, citation, gaps, contradictions), timing for each depth, and limitations. Very complete without output schema.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant context: for depth, it explains each enum value's behavior (quick=3 facets, standard=5+gap recovery, thorough=8+leads chase); for question, it clarifies that broad/multi-part is fine. Adds value 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?

Explicitly states the tool performs grounded multi-source research across structured data sources, distinguishing it from siblings like ask_pipeworx. The verb 'research' and resource 'Pipeworx's 1369 structured data sources' are specific.

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

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

Provides clear when-to-use (broad/multi-part questions), when-not-to-use (single lookup, breaking news), and alternatives (ask_pipeworx). Also mentions account requirements and fallback if not signed in.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, ensuring safety. The description adds behavioral detail: returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed.' This explains exactly what the agent gets and how to use results.

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 efficient: one paragraph that starts with the core purpose, lists use cases, describes output, and gives a timing recommendation. Every sentence serves a purpose 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?

Given 6 parameters (1 required, 100% documented schema), no output schema, and full annotations, the description sufficiently covers what the tool does, when to use it, and what output to expect. No gaps remain.

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

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

Schema coverage is 100% with all parameters described. The description adds value by providing example queries ('look up FDA drug approvals', 'analyze housing market trends') and noting that 'query' accepts multiple aliases. This enhances understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, FDA drugs, etc.) and distinguishes itself from sibling tools (e.g., bet_research, scan_dependency) by being a discovery meta-tool.

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

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

Explicit guidance: 'Use when you need to browse, search, look up, or discover what tools exist for:' followed by domain list, and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This clearly tells when 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?

Annotations indicate read-only, idempotent, non-destructive behavior. Description adds valuable context: fans out across multiple external sources (SEC, XBRL, USPTO, news, GLEIF), returns specific fields, and includes a soft-fail note for patents. No contradiction with annotations.

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

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

The description is detailed and well-structured with examples, bullet-like lists, and clear sections. It is slightly long but every sentence adds value; front-loading with examples helps agent quickly grasp usage.

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 (multiple data sources, fallbacks, input constraints), the description fully covers what the tool returns, input requirements, limitations, and even future deprecation. No output schema exists, but return fields are described in sufficient detail.

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 for type and value. Description goes beyond by explaining the purpose of each parameter (e.g., type restricted to 'company', value as ticker or CIK) and linking to the resolve_entity tool for name resolution, adding significant use 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 starts with clear usage examples (e.g., 'Tell me about X') and states it provides a 'full cross-source profile of a US public company in ONE parallel call'. It lists all data sources and fields returned, making the purpose explicit and differentiating it from sibling tools like chaining single-pack lookups.

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 (holistic company view), when not (names not supported, use resolve_entity first), and provides alternatives (chaining single-pack tools). Also specifies input formats (ticker or zero-padded CIK) and notes a deprecation (patents API sunset).

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

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

Description adds context beyond annotations: mentions clearing sensitive data and staleness. Consistent with destructiveHint=true.

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 purpose, 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?

Single-param tool with no output schema; description covers usage, purpose, and pairing with siblings. Annotations cover safety.

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 param 'key' with description 'Memory key to delete'. Description restates 'by key' without adding new 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?

Description uses specific verb 'Delete' with resource 'memory by key'. It distinguishes from siblings '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: 'when context is stale, the task is done, or you want to clear sensitive data'. Also advises pairing with related tools.

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

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

The description adds behavioral context beyond annotations by explaining the fetch-extract-emit process and the output format. It aligns with annotations (readOnly, idempotent) with 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 well-structured with a clear first sentence stating purpose, followed by process and use cases. It is slightly verbose but 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?

The description fully explains the tool's functionality, output format, and use cases without needing an output schema. It is complete for the tool's complexity and parameter set.

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

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

Schema coverage is 100%, so the schema already fully documents both parameters. The description adds minimal extra meaning beyond context, meeting the baseline 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 clearly states the tool generates an llms.txt file for any URL, specifying the output format and use cases. It distinguishes from siblings by being the only tool dedicated to llms.txt generation.

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

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

The description lists three specific use cases (client indexing, personal project drafting, competitor auditing) but does not explicitly state when not to use or compare to alternative tools like ai_visibility_check or scan_competitor_ai_presence.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context about default behavior (active only) and return fields, which is helpful but not contradictory.

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

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

Two sentences efficiently convey purpose, return fields, and usage guidance. No redundant or irrelevant 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 optional parameter, no nested objects, clear annotations), the description covers purpose, usage, and return fields sufficiently. No missing critical information.

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% for the single parameter include_inactive, which is documented in the schema. The description implies the default (active) but doesn't add significant new meaning beyond the schema, so 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 explicitly states 'List the caller's active subscriptions' and lists the return fields, clearly identifying the verb (list) and resource (subscriptions). It distinguishes from siblings like subscribe/unsubscribe.

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

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

The description provides clear when-to-use context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It lacks explicit when-not-to-use or alternative named tools, but given the siblings, it's adequate.

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?

Discloses rate limit (5 per identifier per day), cost (free, no quota impact), and impact (daily digest, affects roadmap). Annotations already declare non-destructive nature; description adds operational details without contradiction.

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

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

Five sentences covering purpose, usage, guidance, impact, and limits. Front-loaded with core action. Every sentence is essential; no verbose or redundant text.

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

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

Covers what, when, how, and limitations. Missing details on return value or error handling, but for a simple feedback tool this is sufficient. No output schema exists, so not heavily penalized.

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 schema fully describes parameters. Description adds value by clarifying message should describe issues in terms of tools/packs and avoid user prompts. This extra guidance improves parameter usage beyond schema details.

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

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

Description clearly states the tool sends feedback (broken, missing, needed) to the Pipeworx team. It distinguishes from siblings like 'ask_pipeworx' by specifying this is for tool-level feedback, not user prompts.

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 scenarios: bug, feature/data_gap, praise. Also states what not to do (don't paste end-user prompt). Implicitly contrasts with other tools that answer questions.

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 adds significant context beyond annotations: mentions derived from CF analytics-engine, no PII, cached 5min-1h, and the nature of the data (tools, packs, volume). Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, which description supports without contradiction.

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

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

The description is concise (3 sentences plus a bullet list of use cases), front-loaded with purpose, and every sentence earns its place with no wasted words.

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

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

Given the tool has one parameter, no output schema, and rich annotations, the description adequately explains return values (top tools, top packs, total call volume) and caching behavior. It could mention pagination or size limits, but the provided information is sufficient for correct invocation.

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

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

Schema coverage is 100% with a single parameter 'window' having an enum and description. The description adds utility: 'Shorter windows surface what's hot right now; longer windows show steady-state demand', adding 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 'Returns the top tools, top packs, and total call volume over a recent window', using specific verb and resource, and is distinct from sibling tools like discover_tools or ask_pipeworx.

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

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

The description explicitly lists three use cases (discovering hot data sources, confirming canonical tool, seeing alignment), but does not explicitly state when not to use or name alternatives, though the context makes it 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?

The description discloses detailed behavioral traits: monotonicity checks, partition sum checks, similarity threshold (Jaccard ≥0.30), placeholder filter, fill check with book depth analysis, and conditions like realizable_edge_pp ≤ 0 indicating no executable trade. This goes far beyond the annotations (readOnlyHint, idempotentHint) and provides comprehensive 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 packed with information but well-organized: it starts with the main purpose, then explains each mode with details, and ends with response structure and fill check. While somewhat long, every sentence contributes value. Minor improvement could be more structured formatting (e.g., bullet points), but it remains 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?

Despite no output schema, the description thoroughly explains the response fields (opportunities[], partition_check, fill check results) and conditions for each. It also references the sibling tool for custom sizing. The description fully covers what an agent needs to understand and use the tool correctly.

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

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

Both parameters (event, topic) are described with examples and usage context in the description. The input schema already has good descriptions, but the tool's description adds meaning: explains that event accepts slugs or URLs, topic accepts seed questions, and what each mode does. Since schema coverage is 100% and the description enriches the semantics, this is highly effective.

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

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

The description explicitly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes: no args for trending scan, event for per-event partition check, and topic for cross-event scan. This provides a specific verb-resource pairing and clearly differentiates usage scenarios.

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: no args for a trending scan, event for a specific market, topic for cross-event scanning. It explains the rationale for cross-event mode and mentions the sibling tool polymarket_fill_risk for custom sizing. However, it lacks explicit 'when not to use' statements or direct comparisons with all sibling tools.

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

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

The description adds substantial behavioral context beyond annotations, detailing edge calculation, Kelly sizing, slippage handling, caching, diagnostics, and tradeable-edge filters. 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 lengthy but well-structured with clear sections. Every sentence adds value given the tool's complexity, though it could be slightly more concise.

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

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

The description covers response structure, segments, diagnostics, caching, and parameter behavior in detail. Without an output schema, it fully compensates by explaining the fields and organization.

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 9 parameters have schema descriptions (100% coverage), and the description enriches them with usage details, examples, and behavior explanations (e.g., min_partition_leg_kelly per-leg application).

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 top Polymarket markets to find opportunities where Pipeworx data disagrees with market price, using a specific verb and resource. It distinguishes itself from siblings by detailing unique model families and segments.

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 for 'what should I bet on today' and elaborates on the model families, knobs, and filters. While it doesn't directly compare to sibling tools, the context clearly implies its distinct purpose.

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?

Discloses data freshness (daily snapshots, cache-miss triggers), 60-day TTL, decay calculation from daily closes not intraday, snapshot gap behavior. Annotations already show readOnly/idempotent, so description adds rich operational context 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?

Front-loaded with core purpose, but includes detailed output format and edge cases that lengthen it. Some technical details could be streamlined, but overall efficient for the complexity of the 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?

No output schema, but description thoroughly explains return fields (tracked, expired, snapshot_dates) with subfields, limitations, and behavior (gaps, sign convention). Covers all key aspects an agent needs to interpret results.

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 provides default and range for days, allowed values for window. Description adds default values and constrains days to 2-30, but schema already has clamp logic. Provides window options explicitly, adding slight value over 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 tool provides 'edge persistence and decay telemetry' from daily snapshots, answering how long an edge existed and if it's shrinking. Distinguishes from sibling polymarket_edges by focusing on historical trends vs current 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?

Explains when to use (to differentiate fresh vs old edges) and warns that old wide edges are suspicious. Does not explicitly contrast with alternatives like polymarket_edges, but context implies usage for temporal analysis.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds significant behavioral context: it explains the ladder-walking mechanism, the verdicts (clean|degraded|cannot_fill), the forced directional risk, and the interpretation of size_usd for baskets. No contradiction with annotations.

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

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

The description is well-structured with clear sections for single-market and basket modes, usage guidance at the end, and timely warnings. It is informative but somewhat lengthy; a few sentences could be trimmed without losing essential information. Still, it is efficient for the complexity.

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

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

Given 4 parameters, no output schema, and no nested objects, the description is complete. It explains both modes, details return fields (theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill detail, thin_legs, max_clean_notional_usd, forced_directional_risk), and provides usage warnings. No output schema makes this thorough coverage essential, and it delivers.

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. However, the description adds substantial meaning: it clarifies the difference between single-market and basket modes, explains the default side logic for baskets, and defines size_usd as 'settlement notional S (shares per leg)' for baskets. It also specifies default values and clamps, going 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 performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes between single-market and basket modes, lists returned fields (top_of_book, vwap_fill_price, slippage_pp, etc.), and differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by specifying pre-use context.

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: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains the rationale—theoretical overround on thin books is not capturable, and partial basket fills convert arb into unhedged directional position—providing clear decision criteria.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive behavior. The description goes well beyond by detailing safety fields (compatibility_warning, temporal_alignment, skipped counters), explaining when spreads are invalid due to bet shape mismatch or temporal gap, and disclosing limitations (most pre-mapped topics not tradeable). No contradiction with annotations.

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

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

The description is relatively long but well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS). It is front-loaded with the core purpose. Every sentence contributes necessary information, though some redundancy could be trimmed.

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

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

Given the tool's complexity and the absence of an output schema, the description thoroughly covers what the tool returns (leg-by-leg prices, spread, safety fields) and when the output is meaningful. It addresses edge cases like temporal misalignment and bet shape incompatibility, making the tool's behavior fully understandable.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds significant context: it explains the topic mode (10 pre-mapped shortcuts) and how explicit parameters override, providing usage examples. This adds value beyond the schema's raw 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: computing cross-venue spread between Kalshi and Polymarket for the same resolving question. It uses specific verbs ('Cross-venue spread') and distinguishes from siblings like polymarket_arbitrage (single-venue) and other research 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 explains two modes (topic shortcuts vs explicit tickers) and provides clear guidance on when spreads are meaningful (equivalent bet shapes, temporal alignment). It warns that most pre-mapped topics return compatibility warnings, thus implicitly guiding when not to use. However, it does not explicitly mention alternative tools for similar purposes.

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

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

Description adds scoping behavior (by identifier) and listing behavior beyond annotations. No contradiction with annotations (readOnlyHint, idempotentHint, destructiveHint).

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

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

Three sentences, each essential: first states core functionality, second provides usage examples, third adds context. 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?

For a simple read-only tool with one optional parameter and no output schema, the description fully covers purpose, usage, parameter behavior, and 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?

Schema coverage is 100%, and the description adds meaning by explaining the effect of omitting the key parameter (list all keys), which adds value 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 retrieves values saved via 'remember' or lists all saved keys, with specific verb and resource. It distinguishes from sibling tools (remember, 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 states when to use (look up stored context), when to omit key (list all keys), and pairs with related tools (remember, forget). Provides clear guidance on scoping.

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. Description adds context about returned fields (source, citation_uri, payload), filtering behavior, and side effect of mark_read. No contradiction.

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

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

Description is concise (3 sentences) and front-loaded with primary purpose. Every sentence adds value: purpose, return fields, filtering, mark_read mechanism, and alternative access. 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?

For a read-only polling tool with good annotations and full schema coverage, the description is nearly complete. It mentions return fields and alternative endpoint. Lacks explicit bounds on limit and details on error handling, but adequate.

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

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

All 5 parameters have schema descriptions (100% coverage). Description adds practical context: type example 'sec_8k', since as ISO timestamp, mark_read flags events read, and limit default. This significantly enhances schema info.

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

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

Description clearly states it pulls fired events from subscription feed, mentioning filtering and mark_read. However, it does not explicitly differentiate from sibling tools like list_subscriptions or ask_pipeworx, which might also retrieve alerts.

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

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

Description implies usage for retrieving recent alerts and mentions polling is fine, plus notes an alternative HTTP endpoint. But it does not explicitly state when not to use or contrast with 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?

Beyond annotations (readOnly, idempotent, not destructive), the description adds key behavioral traits: fan-out to multiple sources, fallback logic, and USPTO sunset limitation. It also describes the return structure (grouped changes, total count, citation URIs), which annotations lack.

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 detailed yet concise, front-loaded with example queries. Every sentence serves a purpose: stating function, listing sources, explaining fallback, defining parameter semantics, and distinguishing from siblings. 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 (multiple data sources, fallback, output without schema), the description covers all essential aspects: input examples, source behavior, return structure, and limitations. It fully equips an agent to use the tool correctly.

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

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

Schema coverage is 100%, but the description adds valuable examples and usage hints (e.g., ISO date format, relative shorthand like '30d', ticker vs. CIK). It clarifies that 'type' only supports 'company' and provides typical values for 'since'. This exceeds baseline 3 by offering practical guidance.

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

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

The description clearly states it's a change feed for a company in a time window, listing example queries and specific data sources (SEC, GDELT/GNews, USPTO). It explicitly distinguishes from the sibling tool 'entity_profile' for static profiles, ensuring no confusion.

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 when-to-use guidance (e.g., 'What's new with X') and directs users to 'entity_profile' for static profiles. It also details fallback behavior (GDELT→GNews) and notes USPTO soft-fail, giving clear context for usage.

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

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

Adds value beyond annotations by explaining persistence behavior (authenticated users permanent, anonymous 24h). Annotations already indicate idempotent and non-destructive; description complements well.

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

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

Four sentences, front-loaded with purpose, then when-to-use, then behavioral details. 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 simple schema and no output schema, the description covers all necessary aspects: purpose, usage context, data persistence, and related tools. Complete.

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

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

Schema coverage is 100% and description repeats key-value pair, but does not add new semantics. Baseline score 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?

Clearly states the tool saves data for later reuse across conversations/sessions. Distinguishes from sibling tools recall and forget by naming them explicitly.

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

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

Provides specific when-to-use guidance: when discovering something worth carrying forward (examples given). Also recommends pairing with recall and forget.

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 safe, read-only, idempotent behavior. The description adds that it cascades through multiple lookup endpoints internally, replacing manual lookups, and describes citation URIs returned. This provides useful context beyond annotations.

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

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

Description is relatively long but front-loaded with examples and key instructions. It is well-structured with clear sections for supported types. Minor redundancy (e.g., repeating 'Use FIRST') but overall efficient for the information conveyed.

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

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

Given no output schema, the description adequately explains return values and citation URIs for both types. It covers all necessary context for using the tool, including internal cascading logic. Could mention pagination or limits, but not critical for a lookup tool.

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

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

Schema coverage is 100% for both parameters. The description adds substantial meaning: explains return values per type (company returns ticker, CIK, name, citation; drug returns RxCUI, ingredient, brand, citation) and clarifies input formats (ticker, CIK, name for company; brand/generic for drug). This complements the schema effectively.

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

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

Description starts with clear example queries and states it resolves a name to a canonical identifier. It emphasizes 'Use FIRST whenever you have a name but need an ID.' This distinguishes it from sibling tools that may require IDs or perform other functions.

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

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

Provides explicit guidance on when to use: 'Use FIRST whenever you have a name but need an ID.' It details supported entity types and input formats, aiding appropriate selection. However, it does not explicitly mention when not to use or list alternatives, but the context is sufficient.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds behavioral details: it probes each entity with `ai_visibility_check`, ranks by score, and returns a ranked list with score, confidence, and signal density. 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 three sentences, each carrying essential information: purpose, process, and use case. No redundant or filler content. Properly front-loaded with the main action.

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

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

Given no output schema and moderate complexity, the description covers purpose, process, and output items (score, confidence, signal density). It does not explain 'signal density' in detail, but the explanation suffices for agent understanding.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds extra semantics: for `entities`, it specifies the first entry is treated as 'subject' for narrative; for `models`, it explains omission defaults to workers-ai. This additional guidance improves usability 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 compares AI visibility across multiple entities, using `ai_visibility_check` internally, and ranks results by score. It distinguishes from siblings like `ai_visibility_check` by focusing on side-by-side competition.

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 highlights the tool's value for competitive AI-marketing audits ('does Claude know about us as well as our competitors?'), implying when to use it over single-entity tools. It does not explicitly list when not to use, but the context is sufficiently 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?

Description adds behavioral details beyond annotations: fans out to two sources, first-time bundlephobia measurement can take 5-30s, partial failures graceful with sources_failed list. No contradiction with annotations.

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

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

Single well-structured paragraph covering purpose, usage, behavior, and return values. Slightly long but each sentence adds value; front-loads core 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?

Despite no output schema, description lists all return fields (summary block, advisory detail, links, alternative versions). Covers all necessary context for agent invocation.

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

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

Schema coverage 100% with descriptions for both parameters. Description adds context: scoped packages accepted, version defaults to latest if omitted, providing 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?

Description clearly states it's a composite check for npm package evaluation (safety, popularity, size). Distinguishes from siblings like scan_competitor_ai_presence by focusing on dependency analysis, not competitive presence.

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

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

Explicitly specifies when to use: when agent asks about safety/popularity/size of an npm package. Also notes limitations (NPM only, fallback for other ecosystems) and partial failure behavior.

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 safe read-only behavior. The description adds valuable behavioral details: returns top-N passages with offsets and similarity scores, mentions the embedding model (BGE-base-en), window strategy (500-char overlapping), and a 200K char cap with truncation flag. This exceeds 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 5-6 sentences, front-loaded with the core purpose, and every sentence adds value. Slightly verbose in technical details (model name, window size) 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?

Despite no output schema, the description fully explains the return format (passages with offsets and scores) and behavioral constraints (truncation). Combined with high schema coverage and transparent annotations, this is entirely sufficient 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?

Schema coverage is 100%, but the description enhances each parameter: 'text' gets context about max size, 'query' includes realistic examples, and 'limit' states default and range. This adds meaning 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 uses a specific verb+resource combination: 'Semantic search INSIDE a fetched record.' It clearly distinguishes from siblings by contrasting with 'ask_pipeworx_grounded' and implying this is for searching within a single document rather than broader research 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?

Explicitly states when to use: 'Use when the record is too big to cram into the prompt' and provides an alternative: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages.' This gives clear context and exclusion criteria.

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?

Discloses beyond annotations: requires OAuth account, returns subscription id, SMS 10/day cap, webhook auto-disabled after 10 failures, and details on signing secret. Annotations show readOnlyHint=false, idempotentHint=true, destructiveHint=false, and description adds rich behavioral context without contradiction.

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

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

Well-structured with front-loaded purpose, then constraints, then type details, then delivery channels. Every sentence adds value, though slightly verbose with nested examples; could be tightened without losing clarity.

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

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

Covers all relevant aspects: prerequisites, parameter formats, delivery options with technical constraints, rate limits, and failure behavior. No output schema, but description adequately addresses what the tool returns. Complete for a subscribe 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%, yet description adds substantial meaning: type-specific filter examples (e.g., sec_8k items codes, polymarket_edge topics), delivery details (SMS must match verified phone, webhook signing secret returned once). Significantly enhances 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 explicitly states 'Create a proactive monitoring subscription to a live-data event stream.' This is a specific verb+resource with clear distinction from sibling tools like list_subscriptions and unsubscribe.

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?

Describes prerequisites (Pipeworx OAuth account, anonymous/BYO cannot persist), lists supported subscription types with parameter examples, and explains delivery channels with important caveats. Lacks explicit when-not-to-use or alternative tool references, but provides strong contextual 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=true, destructiveHint=false, idempotentHint=true, so the safety profile is clear. The description adds behavioral context about the tool returning live example questions with tool+argument shapes, but does not discuss limitations like rate limiting or response size. The description does not contradict 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 moderately long but well-organized with parentheses and lists. It front-loads the key use cases and ends with usage tips. Every sentence adds value, though could be slightly more concise.

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

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

Despite no output schema, the description fully explains the response structure (category-bucketed examples with tool+argument shapes). It covers the argument, behavior, and context for first use. For a simple onboarding tool, this is complete.

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

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

Input schema has 1 optional parameter with a description listing general categories. The description expands on this by providing the exact allowed topic values (finance, pharma, etc.) and clarifies the effect of omission. Schema coverage is 100%, so the description adds meaningful value 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 is the onboarding entry point for new users, providing example questions and capability overview. It uses specific verbs and resources like 'returns category-bucketed example questions' and distinguishes from siblings such as ask_pipeworx and discover_tools by explicitly stating to use this first.

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 this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains when to pass a topic for focused results. While it doesn't list when NOT to use, the guidance is clear and actionable.

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 destructiveHint false and idempotentHint true. The description adds 'deactivated (not deleted)' and explains that historical events remain, providing additional behavioral context 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?

Two sentences, no extraneous words, front-loaded with the core action. 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?

For a simple tool with one parameter and no output schema, the description fully covers purpose, behavior, ownership, and side effects. No gaps.

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

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

Schema coverage is 100% with a clear description for the 'id' parameter. The tool description does not add new information beyond what the schema provides, so 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 verb 'Cancel' and the resource 'subscription', and distinguishes from siblings like 'subscribe' and 'list_subscriptions' by specifying ownership enforcement and deactivation behavior.

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

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

It explicitly says ownership is enforced, telling the agent when it can be used (only for own subscriptions) and what happens (deactivated, not deleted, historical events remain). It does not explicitly contrast with other tools but provides enough context.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safety. The description adds valuable behavioral context: it breaks down the two processing pipelines, explains the return verdict categories (confirmed, refuted, etc.), and mentions the use of exact percent-delta math for financial claims. No contradiction with annotations.

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

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

The description is fairly lengthy but well-structured with a clear opening list of trigger phrases. It includes detailed processing logic that could be trimmed slightly, but overall it conveys necessary information without excessive 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?

Given the absence of an output schema, the description adequately explains the return structure: verdict, actual value with citation, and reasoning. It covers both claim types and mentions the two processing paths. The description is sufficiently complete for the tool's moderate complexity.

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

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

Schema description coverage is 100%, so the schema already documents both parameters. The description adds context for tolerance_pct, explaining its default (implied by wording, capped at 5) and a use case (hallucination detection). This enriches the schema but is not essential for basic usage.

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 specifies the tool's purpose: natural-language claim verification against authoritative sources. It gives explicit trigger phrases ('Is it true that', 'fact check', etc.) and distinguishes between company-financial claims (SEC EDGAR path) and other claims (grounded pipeline), setting it apart 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 explicitly states when to use this tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It describes the two processing paths for different claim types. While it doesn't explicitly say when not to use, the guidance is clear and 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.