Openf1

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

Annotations indicate safe read-only/idempotent behavior. The description adds critical details: returns per-model {score, confidence, signals, raw_response} plus combined view, and notes Anthropic calls require BYO key with direct billing. This goes beyond annotations.

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

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

Three concise sentences with front-loaded purpose. Every sentence adds value: main action, default model/pricing, return structure, and use cases. No waste.

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 return values (per-model details + combined view) and the key behavioral aspect of free vs paid probing. It covers all necessary information for an agent to use the tool effectively.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the 'context' parameter's purpose ('helps disambiguate common names') and clarifying the models array behavior. It provides practical context for each parameter.

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

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

The description clearly states the tool probes LLMs for entity knowledge and scores visibility (0-100). It specifies default model and optional features, distinguishing it from sibling tools like ask_pipeworx (direct Q&A) and scan_competitor_ai_presence (broader scanning).

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 usage context: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains free vs paid model (needs _apiKey for Anthropic). While it doesn't explicitly list when not to use, the context implies appropriate scenarios.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds significant context: internal routing to 3,744 tools across 884 sources, automatic argument filling, and return of structured answers with pipeworx:// citation URIs. 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 a single, dense paragraph that front-loads the key directive ('PREFER OVER WEB SEARCH'). While comprehensive, it is slightly long and could be broken into shorter sentences for easier scanning. Still, every sentence adds meaningful detail.

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

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

Given no output schema, the description explains the structured output format and citation URIs. It covers a broad range of use cases and examples. Minor gaps: does not mention error handling or what happens if the question cannot be routed to a tool.

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

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

Schema coverage is 100%, with all 7 parameters documented. The description adds value by explaining that the tool accepts natural language and automatically fills arguments, which is not evident from the schema alone. This extra context justifies a score above baseline.

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

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

The description clearly states the tool's purpose: answering factual questions about current/historical data from a wide range of authoritative sources. It lists specific domains (SEC filings, FDA drugs, etc.) and emphasizes structured citations, distinguishing it from web search and sibling tools like 'deep_research' or 'ask_pipeworx_grounded'.

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 directs to prefer this tool over web search for factual queries, provides example query patterns ('what is', 'look up'), and includes concrete examples. However, it does not compare directly to other sibling tools or specify when to use alternatives like 'deep_research'.

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 details the underlying behavior: same routing as ask_pipeworx, but then extraction from tool result only, return structure with evidence/confidence/refusal_reason, and lists explicit refusal reasons. This goes beyond annotations (readOnlyHint, idempotentHint) by revealing the extra LLM call cost and the grounding mechanism. 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 informative and front-loaded with the key purpose. It could be slightly more concise (e.g., shortening the list of refusal reasons or the routing details), but every sentence adds necessary context for high-stakes usage. No fluff, but a bit long for a quick scan.

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 (high-stakes reads, extra LLM call, explicit refusal types), the description covers everything an agent needs: when to use, what it does, how it differs from sibling, return structure, and failure modes. No output schema exists, but the description adequately explains the 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?

The input schema already has 100% coverage with descriptions for all 6 parameters. The description adds marginal value by listing the aliases (query, q, prompt, text, input) but these are also present in the schema. Since schema coverage is high, baseline 3 is appropriate; no significant extra meaning added beyond what's in the schema.

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

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

The description clearly states the tool's purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It distinguishes from sibling 'ask_pipeworx' by emphasizing groundedness and the extractive process. The verb 'answer' combined with 'hallucination-resistant' and 'high-stakes' gives a specific, actionable purpose.

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

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

Explicitly specifies when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' Provides concrete examples (financial verdicts, legal claims, medical lookups, public statements) and advises preferring the sibling 'ask_pipeworx' for casual lookups. No ambiguity.

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 provides extensive behavioral context beyond annotations, including resolver contract, market_match_confidence, safety short-circuiting, closed market handling, illiquid spread warnings, cancellation rule parsing, and parent event extraction. All are consistent with readOnlyHint and idempotentHint.

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 long but well-structured, starting with core purpose and then detailing classifiers, fan-out examples, response shapes, and edge cases. Every section adds important information for correct usage, though some redundancy exists (e.g., repeated mention of fan-out).

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 fully explains the response structure (market, analysis, evidence), key fields (market_match_confidence, parent_event, news fallback), and safety/compliance mechanisms. It covers closed markets, illiquid spreads, and cancellation rules, leaving no critical 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 description coverage is 100%, so baseline is 3. The description adds value by explaining how parameters affect behavior (depth controls evidence sources, include_raw impacts response size), along with practical examples and recommendations (e.g., default false for include_raw).

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 by clearly stating the tool researches Polymarket bets by pulling relevant Pipeworx data. It specifies it can take a slug, URL, or question text, and lists concrete use cases like 'should I bet on X'. This distinguishes it from sibling tools like ask_pipeworx or 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?

The description explicitly gives use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and implies it is specifically for Polymarket market analysis. While it does not directly contrast with alternatives, the detail on fan-out and evidence packet clarifies when this tool is appropriate.

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 (readOnlyHint, idempotentHint, etc.), the description discloses detailed behavior: it pulls specific data (10-K revenue, net income, cash, debt from SEC EDGAR/XBRL for companies; FAERS counts for drugs), handles off-calendar fiscal years, sorts results by primary metric, and returns citation URIs. 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 front-loaded with usage examples, then provides behavioral details, and concludes with efficiency claims. Every sentence adds value, and the structure is logical and concise for the information density.

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 tool with 2 parameters and no output schema, the description is remarkably complete. It explains input semantics, output contents (sorted data with citations), and edge cases (off-calendar fiscal years). 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?

Although schema coverage is 100%, the description adds substantial meaning beyond the schema: it explains what data is retrieved for each 'type' value and how 'values' are interpreted (tickers/CIKs for companies, drug names). This enriches the parameter understanding.

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

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

The description uses specific verbs ('compare', 'side-by-side') and resources ('companies', 'drugs') and clearly distinguishes from siblings by stating it replaces 8-15 sequential lookups. It is explicit about the action and scope.

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

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

The description provides explicit usage patterns ('Compare X and Y', 'X vs Y', etc.) and a strong recommendation to prefer this tool over sequential lookups. It implies the alternative of using single-entity tools, which are siblings, and gives clear context for 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?

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds behavioral details: returns structured findings with verbatim evidence, confidence, source, citation, and explicit gaps[] for unanswered facets. Also states it never invents and facts arrive pre-verified, all consistent 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 a single dense paragraph that front-loads the main purpose. It contains all necessary information without unnecessary fluff, though could be more structured with bullet points for readability.

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 covers input, process, output structure (findings with citations and gaps), prerequisites, usage guidance, and expected time. Complete given the tool's complexity.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant value beyond schema: explains depth values (quick=3, standard=5, thorough=8) and clarifies that broad/multi-part questions are acceptable for the question parameter.

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

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

The description explicitly states it performs 'grounded multi-source research in one call' and details the decomposition and parallel routing process. It clearly distinguishes from sibling tools like ask_pipeworx for single 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 guidance: 'Use for broad or multi-part questions... use ask_pipeworx for single lookups.' Also specifies prerequisites (Pipeworx account, paid plan for thorough depth) and expected time (15-60s).

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 read-only and idempotent. Description adds that results include full schemas with curated examples and are ready to call directly, avoiding second lookup.

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

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

Front-loaded with core purpose, lists domains, explains output, then usage guidance. Slightly long but each sentence adds value with 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 complexity: 6 parameters (mostly aliases), no output schema. Describes return of top-N tools with schemas. Slight gap on default N, but sufficient for invocation.

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

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

Schema covers 100% of parameters with descriptions. Description adds context on natural language queries, examples, and lists aliases, providing value 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?

The description clearly states the tool finds tools by describing data or task, with specific domains listed. It distinguishes itself from siblings by being a meta-tool for discovery.

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

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

Explicitly advises to 'Call this FIRST when you have many tools available and want to see the option set', offering clear context for use, though no exclusion criteria or alternative naming.

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, destructiveHint. Description adds detailed behavioral context: fans out across multiple sources, returns specific fields, notes USPTO patent soft-fail, and limits type to company. 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 starts with example queries (good front-loading), explains the fan-out, lists returned fields. Slightly long but every sentence adds value. Could be trimmed slightly, 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?

No output schema, so description fully compensates by detailing return fields (cik, company_name, recent_filings with URIs, fundamentals, patents with caveat, news, LEI). Covers sources and limitations comprehensively.

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 reaffirms valid values (ticker or CIK) and the name restriction, but does not add substantial new semantic info beyond the schema. 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?

Description clearly states it provides a full cross-source profile of a US public company in one call, using example queries. It distinguishes from siblings like resolve_entity by explicitly stating name resolution is needed 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?

Explicitly says to prefer this tool over chaining single-source lookups for holistic views, and warns that names are not supported (use resolve_entity first). Clear when-to-use and 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?

Annotations already indicate destructiveHint=true and idempotentHint=true. Description adds context about deleting stale or sensitive data, which is valuable but doesn't 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?

Two brief sentences with no wasted words. Essential information is front-loaded: the action, resource, and usage guidance.

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 destructive tool with one parameter and no output schema, the description fully covers purpose, usage, and behavior. 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 a single 'key' parameter described as 'Memory key to delete'. Description adds no extra meaning beyond the schema, which is adequate but not enhanced.

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 'Delete a previously stored memory by key', specifying the action and resource. It distinguishes from sibling tools like 'remember' and 'recall' by focusing on deletion.

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 scenarios: stale context, task completion, clearing sensitive data. Also advises pairing with 'remember' and 'recall', offering clear guidance over alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds process: fetches page, extracts title/description/key links, outputs standard markdown. This provides behavioral context beyond annotations, though could detail limitations like 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?

Three sentences: purpose, process, use cases. Front-loaded and no extraneous words. Efficient and clear.

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

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

Given simple params, rich annotations, and no output schema, description covers purpose, process, and use cases. Lacks error handling details, but overall 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?

Schema coverage is 100% with descriptions for both url and max_links. Description adds some context for url (e.g., 'Full URL') but largely repeats schema. Baseline 3 is appropriate as 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?

Description states the tool generates a production-ready llms.txt file for any URL, specifying the exact resource and purpose. It differentiates from siblings like ai_visibility_check and scan_competitor_ai_presence by focusing on file generation for AI crawlers.

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 lists specific use cases: getting a client's site indexed, drafting for own project, auditing competitor. It does not explicitly exclude scenarios, but the use cases are clear and practical.

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 context by noting 'live race data', indicating the data may change over time, which aligns with the openWorldHint annotation. It also lists exact fields returned. Annotations already cover read-only, idempotent, and non-destructive aspects, so the description enhances transparency 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?

Description is a single, well-structured sentence with a colon and bullet-like list of fields. It is concise, front-loaded, and every part is informative, making it easy for an agent to parse quickly.

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

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

For a simple list tool with two optional parameters, the description explains the return fields and provides a usage hint for the session_key. It does not mention potential ordering or pagination, but these are not critical given the tool's simplicity and the rich annotations.

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

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

Schema coverage is 100%, so baseline is 3. The description mentions session_key='latest' but that is already in the schema. No additional meaning or examples beyond what the schema provides, so score remains at baseline.

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

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

Description clearly states the tool lists F1 drivers for a session with specific fields (number, name, acronym, etc.). The verb 'List' and resource 'drivers' are precise, and it distinguishes from sibling tools like get_laps or get_sessions by focusing on driver data.

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

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

The description implies usage for retrieving driver information per session, but does not explicitly state when not to use it or mention alternatives. The context is clear, but lacks explicit guidance on when to choose this tool over others.

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 value beyond annotations by explaining that the tool provides live race data and how to use 'latest' for recent sessions. It does not contradict annotations, which already indicate read-only, idempotent, and non-destructive behavior.

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

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

The description is concise with two sentences, each adding essential information. It is front-loaded with the tool's core purpose and then provides filtering options, 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?

The description covers the key inputs and data outputs for a tool with 3 parameters and no output schema. It could mention handling of empty results or pagination limits, but the given detail is sufficient for basic usage.

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?

While the schema already describes all parameters (100% coverage), the description adds practical guidance like using 'latest' for session_key and optional filtering by driver_number or lap_number. This enhances understanding of how to use the parameters effectively.

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

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

The description clearly states that the tool retrieves lap times/telemetry for an F1 session, listing specific data fields (duration, sector times, speed-trap, pit-out flag). It distinguishes itself from sibling tools like get_drivers and get_sessions by focusing on lap-level data.

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

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

The description mentions live race data and the use of session_key='latest' for ongoing sessions, providing context for when to use the tool. However, it does not explicitly state when not to use it or contrast it with alternatives like get_sessions or get_drivers.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds 'Live race data source', which suggests dynamic data, but this is consistent. No contradiction, but little extra value beyond annotations.

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

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

The description is a single sentence that efficiently conveys purpose, filtering, and return fields. It is concise but not overly terse, earning a high score.

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. Tool is simple with two optional parameters. Adequately covers context for a list tool, though missing pagination or error info.

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 description does not add meaning beyond the schema. It restates filtering by year/country. Baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose: listing Formula 1 meetings with filtering options and specifying return fields. It distinguishes itself from sibling tools like get_drivers and get_laps.

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

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

The description implies usage for retrieving meeting data but provides no explicit when-to-use or when-not-to-use guidance compared to alternatives. The context 'Live race data source' is helpful but not 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 declare the tool as read-only, idempotent, non-destructive, and open-world. The description adds that it returns a list of sessions with filtering options but does not disclose additional behavioral traits like pagination or rate limits. Given annotations, this 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?

Two sentences: first defines the resource, second states purpose and filters. No extra words, front-loaded with 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 no output schema, the description explains the tool's role in the workflow (providing session_key) and covers filtering. It doesn't detail return format, but the open-world annotation implies variable results. Overall sufficient for the 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?

All 4 parameters are documented in the schema (100% coverage). The description reiterates them and notes that meeting_key comes from get_meetings, adding minor value. Baseline is 3 due to high schema coverage.

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

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

The description clearly defines the tool as listing F1 sessions (Race, Qualifying, or Practice) and states its purpose: to find the session_key needed for drivers/laps. This distinguishes it from sibling tools like get_meetings, get_drivers, and get_laps.

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 to find the session_key needed for drivers/laps' and lists filter parameters. It implies when to use it but does not explicitly mention alternatives or when not to use it, though sibling tools provide 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, idempotentHint=true, destructiveHint=false. The description adds return field details and default behavior ('active subscriptions'), which is helpful but not critical.

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 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?

Fully adequate for a simple list tool with one optional parameter and comprehensive annotations. Provides purpose, usage, and return fields.

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 parameter 'include_inactive' is already well-described in the schema. The description adds no extra meaning beyond what the schema provides.

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

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

The description clearly states the verb 'List' and resource 'subscriptions', specifies it's for the caller's active subscriptions, and lists the returned fields. This differentiates it from siblings like subscribe 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?

Explicitly advises using this tool to review subscriptions before adding more or to find an id to cancel. Does not mention exclusions or alternatives, but provides clear 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?

Discloses non-obvious behaviors: rate-limited to 5 per identifier per day, free, doesn't count against quota, and team reads daily. Annotations do not provide this context, so description adds substantial value.

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 comprehensive yet efficient, with clear sentences that each add value. Slightly longer than minimal but well-organized with actionable instructions.

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 modest complexity (3 params, nested object, no output schema), the description fully covers purpose, usage, behavior, and constraints. No gaps identified.

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 parameters 100%, but description adds usage context (e.g., be specific about tool/error) that aids correct parameter usage. While schema already explains enum values, the description reinforces the expected detail.

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: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It uses a specific verb and resource, and distinguishes from sibling tools by focusing on feedback.

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

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

Explicitly specifies when to use: for bugs, feature requests, data gaps, praise, or other. Provides guidance on what to include (specific tool/pack) and what to avoid (end-user prompts). Clearly differentiates from 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. Description adds no-PII guarantee, self-aggregating nature, and caching duration. No contradictions, but could note 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?

Two well-structured paragraphs. Front-loaded with essential info. Every sentence provides value. No filler.

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 a single parameter and no output schema, description covers purpose, use cases, caching, and data source. Complete for an agent to decide and invoke.

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 the single 'window' parameter with enum and description. Description adds context on window selection (shorter for hot, longer for steady-state), enhancing 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 clearly states it returns top tools, top packs, and total call volume over recent windows. Distinct from siblings like 'ask_pipeworx' and 'discover_tools' by focusing on trending aggregates.

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?

Lists three explicit use cases (discovering hot data, confirming canonical tools, aligning use case). Caching behavior noted. Could mention when not to use, but 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?

The description details internal checks (monotonicity, partition-sum, semantic anchor, partition filter) and output structure (opportunities[], partition_check, fill_check). It explains realizable_edge_pp ≤ 0 means 'do not trade it,' adding value beyond the readOnlyHint annotation by describing algorithm behavior and result interpretation.

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-organized with clear sections for modes, semantic anchor, partition filter, response, and fill check. Every sentence adds value, and it is front-loaded with the critical requirement. For a tool with this complexity, the length is appropriate and 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?

With no output schema, the description comprehensively covers the response format (opportunities[], gap_pp, suggested_trade, etc.), edge cases (skipped_low_similarity, placeholder filtering), and behavior (fill check with realizable_edge_pp). It also mentions a related tool for custom sizing, ensuring completeness.

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

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

Both parameters (`event` and `topic`) are fully described in the schema, and the description adds extensive context: mode selection, example values ('fed-decision-may-2026'), guidance on when to use each, and the behavior difference between single-event and cross-event modes.

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

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

The description clearly states the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) and provides specific examples, making it easy to differentiate from sibling tools like polymarket_edges or polymarket_fill_risk.

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 first sentence mandates requiring one of `event` or `topic`, stating 'call with no args fails.' It explains when to use each mode, recommends event mode for specific markets, and notes cross-event mode catches patterns single-event misses. It also references polymarket_fill_risk for custom sizing, providing an alternative.

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, openWorldHint) are present and consistent. The description adds valuable behavioral details: caching (1h KV-level), diagnostics for empty segments, Fed bets treatment, and edge calculations net of slippage. 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 informative but verbose, with dense technical details. It is front-loaded with the main purpose but could be more concise. Given the complexity, it balances completeness and readability moderately.

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 comprehensively explains response structure (by_segment segments, top-level fields, diagnostics). It covers caching, edge filtering knobs, and special Fed bets handling, making the tool's behavior fully predictable 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 coverage is 100%, so the parameter descriptions are already comprehensive. The description adds context for why some parameters exist (e.g., min_partition_leg_kelly vs min_kelly), enhancing 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 explicitly states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price' and positions it for 'what should I bet on today'. It clearly distinguishes from siblings by detailing three model families and mentioning Fed bets exclusion, making the tool's unique role evident.

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?

Usage is implied ('what should I bet on today') and caching is noted, but there is no explicit guidance on when not to use the tool or alternatives among siblings (e.g., polymarket_arbitrage, bet_research). The description assumes the agent knows the 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?

Beyond the annotations (readOnlyHint, etc.), the description details output structure (tracked, expired, snapshot_dates), data computation (decay on absolute value), and limitations (snapshot TTL, cache-miss gaps). This provides comprehensive behavioral context.

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

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

The description is dense and front-loaded with purpose, but it could benefit from structured formatting (e.g., bullet points for output fields) to improve readability. Still, 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?

With no output schema, the description fully explains the return structure (three arrays with fields) and notes limitations (TTL, daily closes). This is complete for a tool with only two optional parameters.

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 already describes both parameters clearly. The description adds minimal extra context (e.g., 'snapshot family' for window), but does not significantly enhance 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 it provides 'edge persistence and decay telemetry' and answers a specific question about edge freshness. It distinguishes itself from the sibling 'polymarket_edges' by focusing on time-series persistence rather than current snapshots.

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

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

The description implies when to use it (to distinguish fresh vs. old edges) with a concrete example ('a 3-week-old wide edge... is wide for a reason nobody is willing to take'), but it does not explicitly mention alternatives 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructive action. The description adds operational detail: it walks the order-book ladder, computes fills, and returns verdicts (clean/degraded/cannot_fill). It explains behavioral traits like forced directional risk, which is not captured by annotations. No contradiction.

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

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

The description is well-structured with bold headers (REQUIRES, SINGLE-MARKET, BASKET, USE THIS). Every sentence is informative—no fluff. It efficiently packs mode descriptions, default behaviors, and critical warnings into a compact but complete form.

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 completely documents return fields for both modes (top_of_book, vwap_fill_price, slippage, etc.) and explains edge cases like thin_legs and directional risk. It covers all necessary context for an agent to use the tool correctly.

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

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

With 100% schema description coverage, the description still adds value by explaining the interplay between parameters: mutual exclusivity of market and event, default sides for each mode, and interpretation of size_usd as spend vs settlement notional. It clarifies the auto-detection logic for basket side.

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 identifies the tool as a realizable vs theoretical edge check against live order-book depth. It distinguishes between single-market and basket modes, and explicitly states it should be used before acting on certain signals from siblings like polymarket_arbitrage and polymarket_edges.

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

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

The description provides explicit usage guidance: when to use (before executing arb signals or trades above ~$500), what parameters are required (market or event), and how defaults work. It warns about partial fills and unhedged directional risk, giving clear when-to-use and when-not-to-use 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?

The description extensively details behavioral traits beyond annotations: compatibility warning conditions (non-equivalent bet shapes, unrelated events), temporal alignment constraints, and skipped cross-type/subtype counters. It confirms the readOnly, idempotent, and open-world nature from annotations. No contradiction.

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

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

The description is relatively long but well-structured, with sections for modes, response fields, and safety fields. Each sentence adds useful detail. It could be slightly tightened by removing some redundancy, but overall it is well-organized and front-loaded with the main 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 tool's complexity (cross-venue comparison, multiple modes, safety checks) and the absence of output schema, the description fully explains all necessary aspects: purpose, modes, response structure, compatibility warnings, temporal alignment, and why most pre-mapped topics are not tradeable. No information gaps remain 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?

The input schema covers all three parameters with descriptions, achieving 100% coverage. The description adds value by explaining the pre-mapped topics list and how explicit tickers override topic mapping, but the schema already provides the core semantics, so the description's incremental contribution is modest.

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

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

The description clearly states it computes the cross-venue spread between Polymarket and Kalshi for the same resolving question. It specifies two modes (topic shortcuts and explicit tickers) and explains the output. It distinguishes itself from sibling tools like polymarket_arbitrage by being cross-venue and focusing on spread rather than intra-venue arbitrage.

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

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

The description provides clear context on when to use each mode and includes safety fields (compatibility_warning, temporal_alignment) that guide when results are meaningful. It warns that most pre-mapped topics are not tradeable. However, it does not explicitly compare to alternative tools like polymarket_arbitrage or bet_research, leaving some ambiguity about when to choose this tool over others.

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 scoping (anonymous IP, BYO key hash, account ID) and behavior when key is omitted (list all keys), consistent with readOnlyHint and idempotentHint.

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 front-loaded with core functionality, usage context, and scoping; 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?

Complete for a simple read tool with one optional parameter and no output schema; covers behavior, scoping, and pairing with other 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 covers key parameter fully, and description adds behavior of omitting key to list all keys, providing additional 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?

Clearly states the tool retrieves a saved value or lists all keys, with specific verb 'retrieve' and resource 'value saved via remember', distinguishing from siblings 'remember' 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 says to use for looking up context stored earlier, and pairs with remember/forget, but does not explicitly state when not to use it.

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

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

The description adds behavioral details beyond the annotations: explains that mark_read flags events as read, and that polling is safe. Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as false, so the description complements them well.

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

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

The description is four sentences, front-loaded with the main action, and each sentence serves a clear purpose: purpose, return fields, filtering options, and usage note. 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 simplicity (5 optional params, no output schema), the description covers all essential aspects: what it returns, how to filter, the mark_read side effect, and an alternative access method. Complete for agent selection and invocation.

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

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

Schema coverage is 100%, but the description adds value by providing an example for type ('sec_8k') and explaining the effect of mark_read. It also mentions return fields (source, citation_uri, payload) which are not in the input 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 starts with a specific verb+resource: 'Pull fired events from your subscription feed.' It clearly distinguishes this tool from siblings like 'list_subscriptions' by focusing on event alerts. The purpose is unambiguous.

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

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

The description notes that 'Polls work fine' and mentions an alternative endpoint for scripts/dashboards, providing usage context. However, it does not explicitly contrast with sibling tools or state when not to use it.

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

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

Annotations indicate read-only, idempotent, open-world, non-destructive. The description adds extensive behavioral details: it fans out to SEC EDGAR, GDELT→GNews fallback, USPTO (soft-fails), returns structured changes grouped by source with citation URIs, and explains the 'since' parameter format. 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 somewhat long (4-5 sentences) but every sentence serves a purpose: examples, sources, fallback, parameter clarification, sibling comparison. It is well-structured with the main purpose front-loaded. Could be slightly more concise, but overall effective.

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?

There is no output schema, so the description must explain return values. It states 'Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs.' This is adequate but could be more detailed about the structure of individual changes or potential empty results. Given the complexity of multiple sources and fallbacks, the description is fairly complete but not exhaustive.

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

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

The input schema has 100% coverage with descriptions for each parameter. The description provides additional nuance: for 'since', it gives relative shorthand examples and a recommended value; for 'value', it clarifies ticker or CIK; for 'type', it states only 'company' is supported. While schema already covers meaning, the description reinforces and adds context, justifying a score above baseline.

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

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

The description explicitly states the tool's purpose with example queries ('What's new with X', 'latest on Y'), specifies it fetches changes from SEC, news, and patents for a company in a time window, and distinguishes from sibling 'entity_profile' which returns static profiles. The verb and resource are clear.

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 directly tells when to use this tool vs. the sibling 'entity_profile' ('Use entity_profile instead when you want the static profile... regardless of window'). It also provides a recommendation for the 'since' parameter ('Use "30d" or "1m" for typical monitoring'). This gives explicit guidance on 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?

Discloses key behaviors beyond annotations: scoped by identifier, persistence for authenticated vs anonymous, 24-hour retention for anonymous sessions. 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?

Three sentences, front-loaded with purpose. Every sentence adds distinct value, no redundancy or fluff.

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

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

Fully describes the tool's purpose, scoping, persistence behavior, and relationship with siblings. No output schema needed for this simple storage tool.

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

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

Schema coverage is 100% with descriptions, but description adds naming conventions for keys and clarifies value as any text, enriching 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?

Description clearly states the tool saves data for later reuse, with a specific verb ('save') and resource ('data'). It distinguishes from sibling tools (recall and forget) by focusing on storage.

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 guidance ('when you discover something worth carrying forward') and mentions complementary tools (recall, forget). Does not explicitly state when not to use, but 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context beyond these by noting that each call cascades through several lookup endpoints internally and that it replaces 2-3 manual lookups, giving the agent performance expectations.

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 examples and clear sections for supported types. It is slightly lengthy at about 150 words but every sentence adds value, and the front-loading with query patterns helps agents quickly understand 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 of resolving entities for two different types (company and drug) and the absence of an output schema, the description is remarkably complete. It covers input formats, output fields, internal cascading behavior, and provides citation URI patterns, leaving no critical gaps 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?

Input schema has 100% coverage. The description adds rich meaning beyond the schema by detailing what each supported type returns (e.g., for company: ticker, CIK, company_name, citation URI; for drug: RxCUI, ingredient, brand, citation URI), including internal disambiguation and auto-detection logic.

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 like 'resolve' and gives concrete examples ('ticker for...', 'CIK for...') that clearly define the tool's function. It distinguishes its purpose from sibling tools by focusing on identifier lookup, which is a unique capability 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 states 'Use FIRST whenever you have a name but need an ID,' providing clear guidance on when to use this tool. While it doesn't list when not to use it, the 'use first' directive and the context of sibling tools imply the alternatives.

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

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

Annotations already declare safe, idempotent, read-only. Description adds that it probes, ranks, surfaces most/least recognized, and returns score, confidence, signal density per entity. Does not contradict annotations; adds behavioral details.

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

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

Three sentences, front-loaded with main purpose, then details. No wasted words; 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?

No output schema, but description specifies return type (ranked list with score, confidence, signal density). All parameters covered. Sufficient for an agent to understand tool behavior and output.

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 extra meaning: first entity is treated as 'subject' for narrative, optional context disambiguates names. This 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?

Description clearly states the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, and ranks them by score. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic).

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

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

Explicitly states use case: competitive AI-marketing audits. Implies when to use vs. alternatives by mentioning it uses ai_visibility_check, but does not explicitly list when not to use.

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

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

Annotations provide readOnlyHint, idempotentHint, destructiveHint. The description adds significant behavioral context: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s and will be listed in sources_failed if timeout. Also describes 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?

The description is moderately long but well-structured. It front-loads the core purpose, then provides usage guidance, return format, limitations, and edge cases. Every sentence adds value; could be slightly tighter but is not 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?

Given no output schema, the description explains the return format (summary block fields, advisories, links, alternative versions), ecosystem limitation, partial failure behavior, and timing. This is comprehensive for a composite tool querying external APIs.

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

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

Schema coverage is 100%. Description adds meaning beyond schema: clarifies that version defaults to latest published, scoped packages (e.g., '@types/node') are accepted. This adds value 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 and resource: 'composite should I add this npm package check'. It clearly states it aggregates info from deps.dev and bundlephobia. Distinguishes from sibling tools by its domain (npm packages) and aggregated nature.

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

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

Explicitly says when to use: 'when an agent asks is X safe / popular / small' or 'what does adding lodash cost me'. Also provides exclusion: 'NPM ecosystem only; other ecosystems fall under deps.dev:version directly', giving clear context 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?

Adds significant behavioral context beyond annotations: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and return structure (passages with offsets and similarity scores). 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?

Three well-structured sentences: purpose, usage guidance, and technical details. Front-loaded with key verb and resource. No redundant or vague wording.

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?

With 3 parameters and no output schema, the description fully explains return format, technical details, limits, and use case. Handles both input constraints and expected output, 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 coverage is 100% with descriptions for all three parameters. Description adds value with examples for query, clarifies default for limit (5), and confirms max chars for text (~200K).

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 'semantic search INSIDE a fetched record' with clear verb+resource. Distinguishes from siblings by mentioning pair with ask_pipeworx_grounded and use case for large records.

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 guidance ('when the record is too big to cram into the prompt') and mentions alternative tool (ask_pipeworx_grounded) for grounded responses.

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 important behaviors beyond annotations: returns subscription id, delivery webhook secret returned only once, SMS 10/day cap, webhook auto-disabled after 10 failures. Annotations indicate non-read-only and idempotent, which align with description.

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

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

The description is a single paragraph that is well-structured with examples and constraints, but it is quite long. It is front-loaded with the main purpose, and every sentence adds value, though slight trimming could improve conciseness.

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 (3 parameters, nested objects, no output schema), the description covers the return value, prerequisites, subscription types, delivery options, and constraints comprehensively, making it complete 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?

The input schema has 100% description coverage, and the description adds significant context with examples, constraints (e.g., phone must be verified, email validation), and detailed parameter formats for each subscription type.

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 creates a proactive monitoring subscription to a live-data event stream, with specific verbs and resource. It distinguishes from sibling tools like 'unsubscribe' and 'list_subscriptions' by focusing on creation.

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

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

The description provides clear context for use, including the requirement for a Pipeworx OAuth account, and details on delivery channels. However, it does not explicitly exclude scenarios or mention alternatives like when to use 'list_subscriptions' instead.

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 confirm safety (readOnly, idempotent, non-destructive). Description adds that results are 'category-bucketed example questions with the exact tool + argument shape' and drawn from a live catalog. This provides useful 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?

The description is a single, well-structured paragraph that front-loads alternative phrasings and states purpose. It is comprehensive but slightly dense due to the long parenthetical list of categories, which could be more concise.

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

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

Given no output schema, the description adequately explains the return format (category-bucketed example questions with tool + argument shape). It covers the tool's role, invocation options, and output content, making it complete for an onboarding tool.

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

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

Schema coverage is 100% with a clear description for the 'topic' parameter. The description repeats the examples and usage, adding marginal value. Baseline of 3 is appropriate as the schema already documents the parameter well.

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 as the onboarding entry point that returns example questions with tool calls. It uses specific verbs ('suggest', 'return') and distinguishes from siblings by positioning as a first-use resource.

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 this FIRST when you do not yet know what Pipeworx can do for you' and provides guidance on calling with no arguments vs. topic filter. Clearly identifies when to use it over other meta-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 indicate mutation (readOnlyHint false), non-destructiveness (destructiveHint false), and idempotency (idempotentHint true). The description adds valuable context: ownership enforcement and deactivation behavior (soft delete), which is consistent 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 concise sentences, each serving a distinct purpose: action, constraint, and consequence. 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?

For a simple tool with one parameter and no output schema, the description covers all necessary aspects: what it does, who can use it, and what happens to the data. It also references a related tool (recent_alerts) for historical visibility.

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 single parameter 'id' is well-described in the schema. The description does not add further detail about the parameter, so it meets the baseline without extra 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 clearly states the action ('Cancel a subscription by id') and differentiates from sibling tools by explaining that the row is deactivated, not deleted, and historical events remain accessible via recent_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?

Explicitly states ownership enforcement ('you can only cancel your own subscriptions'), which tells the agent when and when not to use it. However, it does not explicitly name alternative tools for cancelling others' subscriptions.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by detailing the return structure (verdict with five possible values, actual value with pipeworx:// citation, percent delta) and explaining that it replaces multiple sequential calls, which informs the agent about efficiency and the nature of the response.

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 concrete examples and usage scenarios, and each sentence adds value. It is slightly long but still efficient for the amount of information conveyed. Could be tightened by removing the list of verdict values if they are documented elsewhere, but currently it's 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 tool with one parameter, no output schema, but comprehensive annotations, the description fully covers what the tool does, its domain, return values, and efficiency gains. It leaves no significant gaps for an agent to infer behavior.

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% for the only parameter 'claim', and the schema already provides a clear description with examples. The description reiterates the parameter's purpose without adding new details about format or constraints, so it meets but does not exceed the baseline expectation 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 like 'verify', 'check', and 'confirm or refute' alongside resource 'natural-language claim verification against authoritative sources'. It clearly distinguishes the tool from siblings by noting it replaces 4-6 sequential calls, and the examples (e.g., 'Apple's FY2024 revenue') precisely scope its domain.

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

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

The description explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and further narrows to 'company-financial claims (revenue, net income, cash position for public US companies)'. It does not explicitly list alternative tools for non-financial claims, but the context signals suggest these exist among siblings.

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