Polygon Io

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds value by specifying the data source (Polygon.io) and the exact fields returned, which is beyond the annotation scope. 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?

Two sentences efficiently convey the tool's core identity, data contents, and use cases. No extraneous information; every sentence adds value.

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

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

Given the output schema exists, the description adequately covers the return data (OHLC, volume, VWAP) and the source. However, it misses parameter explanations and constraints like date format or ticker scope, which are important for correct 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?

With 0% schema description coverage, the description should compensate by explaining key parameters, but it does not. It fails to describe ticker, timespan, multiplier, from, to, limit, etc. The schema examples provide minimal context, but the description itself adds no parameter-level 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 it returns OHLC price bars for US stock tickers with granularity from 1 minute to quarterly, listing specific fields (open/high/low/close, volume, VWAP). This is a specific verb+resource, and the mention of Polygon.io and historical data distinguishes it from sibling tools like daily_open_close or previous_close.

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 recommends use cases: charting equities, intraday analysis, backtesting historical prices. However, it does not provide guidance on when NOT to use this tool or mention alternative tools for similar purposes, like grouped_daily or daily_open_close.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral details: default model is free, Anthropic requires BYO API key, and output includes per-model {score, confidence, signals, raw_response} plus combined view. 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?

Three sentences, front-loaded with core purpose and key details. Every sentence adds value: default model, optional Anthropic, use cases. No wasted words.

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

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

Given no output schema, the description specifies return format ('per-model {score, confidence, signals, raw_response} + combined view'). It covers free vs paid models, API key requirement, and use cases. For a 4-param tool with 100% schema coverage, this is fully complete.

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

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

Schema description coverage is 100%, but the description adds meaning beyond the schema: explains default model for 'models', BYO key for '_apiKey', and context helps disambiguate. This adds value for the agent.

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 'Probe one or more LLMs for what they know about a business/brand/product/topic and score visibility (0-100) per model.' It specifies the verb 'probe' and the resource 'LLMs for visibility scoring'. The purpose is distinct from sibling tools like 'scan_competitor_ai_presence' or 'resolve_entity'.

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 use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly state when not to use or name alternatives, but the context is clear enough for an AI agent to decide.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds useful behavioral context: it 'Routes the question to the right one of 5,462 tools across 1419 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs.' It also notes 'one fast call' and 'works on every tier.' No contradictions exist with annotations.

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

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

The description is well-structured and front-loaded with the most important directive. Each sentence earns its place: usage priority, routing behavior, trigger phrases, examples, and escalation paths. Despite length, it avoids redundancy and remains skimmable with clear imperative statements.

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 absence of an output schema, the description covers the essential context: what it does, when to use it, how it differs from siblings, and what it returns (structured answer with citations). It also addresses edge cases like breaking news and multi-part questions, making it sufficiently complete for an agent to invoke it correctly.

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

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

Schema coverage is 100% (all parameters including aliases are described). The description itself does not add parameter-specific meaning, but the examples ('What was Apple's revenue in 2024?') illustrate the expected input format. This meets the baseline expectation when the schema handles 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: 'answers questions with authoritative structured data with citations.' It specifies the verb ('ask', 'routes', 'returns'), the resource (structured data from verified sources), and distinguishes from siblings by naming ask_pipeworx_grounded and deep_research as alternatives. The phrasing 'PREFER OVER WEB SEARCH' leaves no ambiguity about its primary use.

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?

Extensive guidance is provided: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and explicit 'Step up only when needed' with exact alternative tools. It lists trigger phrases ('what is', 'look up', 'find', 'get the latest', 'how much', 'current') and concrete examples, making the usage condition crystal clear.

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

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

Beyond annotations (readOnly, openWorld, idempotent, non-destructive), the description reveals it is a full working router with no fallback, that candidate improvements may be live, and that it currently matches the stable version. This gives the agent important context about how to interpret its outputs.

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

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

The description is about 100 words across four sentences, each providing distinct information (beta status, current state, usage, fallback). It is detailed without being verbose.

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

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

For a universal router, the description covers the key aspects: identical toolset and response shape, experimental nature, current state, and usage guidance. Without an output schema, it still gives enough context by referencing ask_pipeworx's response shape.

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

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

The input schema already describes all six parameters with 100% coverage, including aliases. The description adds no additional parameter-specific information, so it relies on the schema as the 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 it is a beta universal router identical to ask_pipeworx, distinguishing it from the stable sibling and other tools. It specifies the core function (routing questions) and the experimental 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 instructs to use it exactly like ask_pipeworx when wanting the newest routing, and notes that it currently matches ask_pipeworx. It also mentions that results are compared against the stable router, implying it's for experimental evaluation.

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 goes beyond annotations by detailing the internal process: picks a tool, fills arguments, fetches data, extracts answer using only tool result. It discloses the refusal behavior with specific refusal_reason values, and mentions the extra LLM call cost. Annotations declare readOnlyHint and idempotentHint, but the description adds meaningful behavioral context like the refusal mechanism and cost, without contradiction.

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

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

The description is somewhat lengthy but well-structured and front-loaded. The first sentence immediately conveys the tool's purpose, followed by a logical explanation of the routing and extraction mechanics, return format, and usage guidance. Every sentence contributes value, though a sharper edit could reduce wordiness without losing information.

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

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

For a tool with no output schema and 100% schema coverage on a single required question parameter, the description fully compensates. It details the return shape (answer, evidence, confidence, source, refusal reasons), the routing mechanism, and usage context. It also distinguishes from sibling ask_pipeworx and provides cost trade-offs, making the description complete for an agent's decision-making.

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

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

The schema already provides 100% coverage for all six parameters, with 'question' and its aliases clearly documented. The description adds no new parameter-specific semantics beyond the schema, but it does imply the input is a natural language question suited for high-stakes reads. The baseline of 3 applies since the schema does the heavy lifting.

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

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

The description clearly states the tool's function: a hallucination-resistant answer mode for grounded reads. It specifies the mechanism (same routing as ask_pipeworx, extracting answers only from tool results) and distinguishes it from the sibling ask_pipeworx by emphasizing grounded evidence and refusal behavior. The verb 'answer' and resource 'pipeworx grounded' are explicit.

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

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

Explicitly states when to use this tool: when answers will be quoted, cited, or acted on and facts must not be invented (financial verdicts, legal claims, medical lookups). It also provides an exclusion: prefer ask_pipeworx for casual lookups due to the extra LLM call cost. This is a clear when-to-use and when-not-to-use with 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?

The description extensively discloses behavioral traits beyond annotations: resolution workflow, classifier and fan-out logic, error handling (low_confidence_match, market_closed_or_inactive), tradeability warnings, resolution-rule risk, and data provenance (e.g., GDELT 429 fallback). No contradiction with annotations (readOnly, idempotent, non-destructive) as it describes read-only data aggregation.

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 a clear purpose statement, but it becomes verbose with many examples and internal details (classifier list, fan-out examples) that could be summarized. While comprehensive, it lacks conciseness; every sentence is informative but the volume may hinder quick comprehension.

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

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

Given no output schema, the description thoroughly explains the response structure (market, analysis, evidence, resolver contract), handles edge cases (low confidence, closed markets, wide spreads, cancellation rules), and covers safety mechanisms. It is fully complete for an agent to understand tool behavior and expected outputs.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds context for the 'market' parameter (slug, URL, or question text) and provides examples, which is helpful but not extensive. For 'depth' and 'include_raw', the description does not add meaningful information beyond the schema descriptions, meeting rather than exceeding expectations.

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 researches a Polymarket bet by pulling Pipeworx data in one call. It specifies input types (slug, URL, question text) and explicitly lists use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'), making the purpose highly specific and distinguishable from sibling tools like polymarket_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 usage scenarios ('Use for...') and covers when to trust the results (e.g., inspecting market_match_confidence). However, it lacks explicit 'when not to use' or direct comparison with alternatives, though the context implies it is for research rather than trading or arbitrage.

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

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

Annotations already indicate read-only, open-world, idempotent behavior. The description adds valuable context: it handles fiscal year variations, returns LATEST data, sorts by primary metric, and includes 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 example phrases and a clear usage directive. It is slightly verbose but each sentence provides essential context. The structure is logical: examples, then straight-to-use advice, then type-specific details.

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

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

Given the tool has 2 parameters with 100% schema coverage and no output schema, the description fully explains what the tool returns (paired data + citation URIs), covers both entity types, and specifies sorting behavior. It is complete enough for correct invocation.

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

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

Schema coverage is 100%, but the description adds meaning by detailing what each type retrieves (e.g., 'pulls LATEST 10-K revenue + net income + cash + long-term debt' for company), explains values format (tickers/CIKs for company, names for drug), and confirms min/max items. This goes beyond the schema's minimal 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 explicitly states the tool does side-by-side comparison of 2-5 companies or drugs, with concrete example phrases like 'Compare X and Y' and 'head to head'. It clearly distinguishes its purpose from sequential lookups by stating 'ALWAYS PREFER over sequential single-pack lookups'.

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

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

The description provides explicit when-to-use guidance with example queries and a strong preference directive. It explains when to use company vs drug type, and what data each retrieves. This helps the agent decide between this tool and sibling tools like entity_profile or ticker_details.

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, so the safety profile is clear. The description adds context about the data source (Polygon.io) and the specific fields returned, which is useful but not extensive.

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 the purpose and key details. It is front-loaded with the action and resource, but could benefit from structured parameter descriptions.

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

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

Given that an output schema exists, the description does not need to explain return values. It lists the fields returned (open, high, low, close, volume, after-hours/pre-market) and specifies the source (Polygon.io). It lacks parameter details but is otherwise complete for a read-only 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 description coverage is 0%, so the description bears full burden. It mentions 'YYYY-MM-DD' for date but does not explain the 'adjusted' parameter or provide any additional semantics for the ticker parameter. The examples help but are separate from the description.

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

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

The description clearly states the verb 'Fetch' and the specific resource: official open, high, low, close, volume, and after-hours/pre-market prices for a US stock ticker on a specific date. It distinguishes from siblings like 'previous_close' which only returns previous close, and 'aggregates' which provides custom intervals.

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 fetching a single day's data but does not explicitly state when to use this tool versus alternatives like 'previous_close' or 'aggregates'. No exclusions or when-not guidance is provided.

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 only indicate readOnlyHint and idempotentHint, but the description adds substantial behavioral context: account/auth requirements, expected latency (15-60s, thorough up to 90s), return of verbatim evidence with confidence/source/fetched_at/citations, explicit gaps[] rather than invented answers, semantic excerpting, contradictions[] flags, and hop/citation_uri semantics. 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?

Although long, the description is dense with unique, non-redundant information. It is front-loaded with the critical account constraint and opens with a clear 'this is NOT open-web search' framing. Every sentence earns its place, covering use cases, exclusions, parameters, behavior, return format, and performance expectations without 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?

Despite having no output schema, the description fully specifies the return value (findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[], hop, citation_uri). It also covers latency, error behavior (empty gaps for off-catalog topics), pagination/recovery semantics, and how it handles large records. For a complex research tool, this is complete.

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

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

Schema coverage is 100% already, but the description enriches both parameters: it clarifies 'question' accepts broad/multi-part natural language ('Broad/multi-part is fine — decomposition is the point') and explains depth levels in behavioral terms (quick=3 facets, standard=5 with gap recovery, thorough=8 with iterative chases and contradictions scan). This goes well beyond the enum list.

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 a specific verb+resource: 'grounded multi-source research across Pipeworx's 1419 STRUCTURED data sources' that 'decomposes your question into focused facets' and returns a findings packet. It explicitly distinguishes itself from open-web search and from sibling tools like ask_pipeworx, which is exactly what is needed.

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 gives explicit when-to-use/when-not-to-use guidance: 'For a single lookup use ask_pipeworx (one LLM call, not many)' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx' because deep_research returns mostly empty gaps[] for non-structured topics. It also specifies account requirements and tier limitations, leaving 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?

Annotations already convey readOnly, idempotent, non-destructive. The description adds value by detailing the return format (top-N tools with schemas and examples) and that results are ready to call directly. It does not contradict annotations.

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

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

Front-loaded with the core purpose. Structured logically: purpose, when to use, what it returns, explicit instruction. No superfluous sentences; each part earns its place despite the fairly long list of domains.

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 meta-tool with no output schema, the description fully explains the return value (tool names, descriptions, schemas with examples) and the 'ready to call' nature. Parameter count and aliases are handled well. Completely 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 all parameters. The description adds a list of domains for the query parameter but does not significantly improve parameter understanding 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?

The description clearly states 'Find tools by describing the data or task' with a specific verb and resource. It distinguishes itself from sibling tools (e.g., tickers, news) by being a meta-tool for discovery, listing many domains.

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 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available'. Provides clear when-to-use context and a direct instruction, though no when-not-to-use is needed.

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

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

Annotations already indicate read-only, idempotent, and open-world traits. The description adds that the tool returns both historical and upcoming data and is limited to US-listed tickers, providing behavioral context beyond the annotations without contradiction.

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

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

The description is two sentences, front-loading the key information about the data returned and a primary use case. No redundant phrases, every sentence earns its place.

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

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

Given the existence of an output schema and good annotations, the description is adequate for a simple data retrieval tool. However, it does not explain the input parameters or mention the output structure, leaving some potential confusion for agent 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?

With 0% schema description coverage, the description partially compensates by mentioning the ticker and implied fields returned, but does not explain the 'limit' or 'ex_dividend_date' parameters. It adds some value but leaves gaps for agent understanding.

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

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

The description clearly states it provides historical and upcoming cash and stock dividends for US-listed Polygon.io tickers, listing specific fields. It differentiates itself from general financial data tools like aggregates but does not explicitly contrast with sibling tools like splits or daily_open_close.

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 suggests use for income analysis and dividend-capture strategies, implying context. However, it provides no guidance on when not to use the tool or alternatives. Sibling tools are not mentioned, leaving the agent to infer appropriate 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 already indicate read-only, idempotent, non-destructive. Description adds specific behavioral details: fans out across multiple sources, returns up to 5 filings, soft-fail for patents (USPTO sunset May 2025), sorted fundamentals. 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?

Moderately long but well-structured: starts with illustrative examples, then core purpose, then detailed return structure. Every sentence adds value. Could be slightly more concise but remains efficient.

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

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

Despite no output schema, description fully compensates by listing all return fields (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI). Covers limitations (patents sunset), prerequisites (ticker or CIK), and fallback behavior (GDELT→GNews). Comprehensive for a complex 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 covers type and value with descriptions. Description adds critical nuance: type only 'company' currently, value must be ticker or zero-padded CIK, names not supported. Also clarifies zero-padding requirement and that CIK is accepted.

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 parallel call, with specific examples and explicit preference over chaining individual lookups. Distinguishes from sibling tools like aggregates, ticker_details, and news.

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 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also mentions when not to use: names not supported, use resolve_entity first. Provides clear trigger examples.

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 a safe, idempotent read operation. The description adds behavioral context by listing returned fields and covered asset classes, going beyond what annotations provide. No contradictions.

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

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

The description is a single, efficient sentence that front-loads the purpose and covers scope and return values without redundancy.

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

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

The tool is simple, has an output schema, and the description covers scope and returns. Missing parameter details (especially for locale) lower completeness slightly.

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 0%, so the description must compensate. It partially does: 'asset_class' is implied via categories (stocks, options, etc.), but 'locale' parameter is not explained. Description adds moderate value but is incomplete.

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's a 'reference list of exchanges Polygon.io covers' and lists specific categories (US stock exchanges, options venues, crypto exchanges, OTC tiers). It also specifies the returned fields (MIC code, name, asset class, type, locale), making the purpose crystal clear and distinct from sibling tools like 'tickers'.

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 clearly implies when to use this tool (to get exchange metadata), but does not explicitly contrast with alternatives or state when not to use it. Given the simple nature of a reference list, this is adequate.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. The description adds context about clearing stale or sensitive data, but does not elaborate on behavior like what happens if the key doesn't exist or any side effects. The description adds moderate 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 two sentences long, front-loaded with the core purpose, and provides usage guidance and sibling references without any unnecessary words.

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

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

Given the tool's simplicity (one parameter, no output schema) and the presence of annotations, the description covers the purpose, usage context, and parameter sufficiently. It is complete for the tool's complexity.

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

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

Schema coverage is 100% and the schema already describes the 'key' parameter as 'Memory key to delete'. The description does not add any additional semantic detail beyond that, so it meets the baseline but does not exceed it.

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 the verb 'Delete' and specifies the resource 'a previously stored memory by key'. It distinguishes from siblings by explicitly mentioning 'Pair with remember and recall', making it clear what this tool does relative to its companions.

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

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

The description provides clear guidance on when to use this tool: 'when context is stale, the task is done, or you want to clear sensitive data'. It also explicitly mentions sibling tools 'remember and recall' as alternatives to pair with.

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

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

Annotations declare readOnlyHint, idempotentHint, and destructiveHint as false. The description adds behavioral details: fetches the page, extracts title/description/key links, emits standard markdown format, and mentions network fetch. 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 paragraph, concise and front-loaded with key action. It could be slightly more structured (e.g., bullet points), but every sentence contributes meaning without redundancy.

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

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

Given only 2 parameters, full schema coverage, no output schema, and comprehensive annotations, the description covers purpose, behavioral traits, output format, and usage scenarios. It is complete for an agent to understand and invoke correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by specifying default max_links (25) and maximum (50), which the schema does not provide. This extra context aids the agent in parameter selection.

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

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

The description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb 'Generate' and resource 'llms.txt file'. It explicitly mentions AI crawler indexing and emits standard format. Sibling tools are unrelated, so differentiation is not needed.

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

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

The description provides concrete use cases: getting a client's site indexed, drafting for own project, auditing competitor. It does not explicitly state when not to use or give alternatives, but given no closely related sibling tools, the guidance is sufficient.

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

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

Annotations already cover readOnly, openWorld, idempotent, and non-destructive behaviors. Description adds value by specifying the single-call efficiency, data source, and date format. It does not contradict annotations, so this is a solid 4.

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?

One sentence that is front-loaded with the verb 'Fetch', efficiently conveying purpose, scope, source, and usefulness. 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?

Output schema exists so return values are covered. Given the tool's complexity (all US stocks in one call), the description provides enough context for basic usage, though some agents might benefit from mentions of data frequency or potential size.

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 0% description coverage for its 2 parameters. The description explains the 'date' parameter format (YYYY-MM-DD) but does not mention the 'adjusted' parameter. This partially compensates for the missing schema descriptions, but not fully.

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 action (Fetch OHLCV bars), target (all US stocks on a given date), source (Polygon.io), and use case (market-wide snapshot or screening). This distinguishes it from per-ticker tools like 'daily_open_close' and 'previous_close' among 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?

Description mentions it is 'useful for market-wide snapshot or screening', providing clear context for when to use. However, it does not explicitly state when not to use or compare to specific siblings, though the scope is implied.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds context about scope (caller's subscriptions) and returned fields, but no additional behavioral traits beyond what annotations suggest.

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

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

The description is extremely concise, with two sentences that cover purpose, output fields, and usage guidance. There is no wasted text, and the structure is 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?

For a simple list tool with one optional parameter and no output schema, the description is fairly complete. It lists returned fields and provides usage context. It could mention any limits or ordering, but overall it is 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?

The single parameter 'include_inactive' is fully described in the schema with 100% coverage. The description does not add any additional meaning beyond the schema, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool lists the caller's active subscriptions and specifies the returned fields. It distinguishes itself from sibling tools like 'subscribe' and 'unsubscribe', which perform different actions.

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

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

The description provides explicit guidance on when to use this tool: to review monitoring before adding more subscriptions or to find an ID to cancel. This gives clear context, though it 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?

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds value by specifying the data source (Polygon.io) and the exact fields returned (date, holiday name, exchange status), providing 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 sentence that front-loads the main purpose and includes essential detail without any 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 zero parameters and an existing output schema (as noted in context signals), the description fully conveys what the tool does and what data it returns. No additional information is needed.

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

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

There are no parameters, so the baseline is 4. The description does not need to add parameter info because the input schema is empty and coverage is 100%.

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

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

The description clearly states it returns a list of upcoming US stock market holidays from Polygon.io, specifying the data fields (date, name, exchange status). This directly and uniquely identifies the tool's function, distinguishing it from siblings like market_status or exchanges.

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 holiday schedules, but does not explicitly state when to use this tool over alternatives (e.g., market_status for current status) or when not to use it. No exclusions or comparisons are provided.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, but the description adds no extra behavioral context such as update frequency or data source scope.

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

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

The description is extremely short but under-specified; it fails to provide enough detail to justify its existence, being more of a label than a helpful explanation.

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 having an output schema, the description does not clarify what market status entails (e.g., indices, trading hours), leaving the tool's purpose incomplete.

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 tool has zero parameters and 100% schema coverage, so the baseline is 4. The description does not add parameter information, but none is needed.

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 'Current market status' is essentially a tautology of the tool name, providing only a vague sense of returning status data without specifying what aspects of market status are covered.

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

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

No guidance is given on when to use this tool versus sibling tools such as market_holidays or daily_open_close, or what distinct data it provides.

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=false. Description adds that it returns ticker-tagged headlines with sentiment and fields like URL. No contradictions, and the output schema further clarifies return 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?

Two sentences: first describes tool's offering, second provides usage context. No redundant 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?

Output schema exists, so return structure is covered. However, with 5 undocumented params and no parameter explanations, the description is incomplete for fully autonomous 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 has 0% description coverage (5 params undefined). Description does not explain sort, limit, order, ticker, published_utc. While 'ticker' is implied in use cases, the lack of guidance on other parameters leaves ambiguity for an AI agent.

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 retrieves financial news with specific fields (publisher, URL, image, summary, sentiment). Differentiates from web search and implies a distinct purpose among siblings like aggregates and ticker_details.

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 use cases ('what is the news on $TICKER', 'market-moving headlines today') and recommends over web search for equity-focused news. Does not directly compare with sibling tools like aggregates or ticker_details, but still gives actionable 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 are all false (non-read-only, non-idempotent, non-destructive) but the description adds valuable context: rate-limit of 5 per identifier per day, free/no quota impact, and that the team reads digests daily. This goes beyond the structured hints and helps set expectations for side effects and usage constraints.

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

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

The description is longer than average but every sentence serves a purpose: purpose, usage criteria, exclusions, content guidance, rate limits, and impact. It is front-loaded with the core action and well-structured with clear separations. 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 feedback tool with nested context, an enum, and no output schema, the description is remarkably complete. It covers purpose, selection criteria, exclusions, message content guidelines, rate limits, quota, and what happens to the feedback. An agent has everything needed to invoke it correctly without further clarification.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds important usage guidance beyond the schema by instructing agents to describe issues in terms of Pipeworx tools/packs and to not paste end-user prompts. This enriches the semantics of the 'message' and 'context' parameters.

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

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

The description opens with a clear verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It explicitly differentiates from sibling tools (ask_pipeworx, discover_tools) by focusing on reporting issues/feedback rather than retrieving information. The specific examples (bug, feature/data_gap, praise) remove any ambiguity.

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 when-to-use criteria ('Use when a tool returns wrong/stale data...') and clear exclusions ('ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server... file it with that server instead'). This directly guides an agent on when to select this tool 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 indicate read-only, idempotent, non-destructive behavior. The description adds valuable context: no PII, cached (5min-1h), derived from analytics engine. This goes beyond the annotations.

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

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

The description is concise and well-structured: main result first, then use cases, then additional behavioral details. No fluff, every sentence adds value.

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

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

Given the low complexity (one optional parameter, no output schema), the description covers purpose, usage, parameter semantics, and behavioral traits completely. An agent has all needed info to invoke correctly.

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

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

Schema coverage is 100% and the description adds semantic guidance: shorter windows surface what's hot, longer windows show steady-state demand. This adds meaning beyond the schema's enum and basic description.

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

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

The description clearly states the tool returns top tools, packs, and call volume, with specific verb and resource. The three use cases (discovering hot data, confirming canonical tool, checking alignment) further clarify its purpose and distinguish it from siblings like aggregates or ask_pipeworx.

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

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

The description provides three explicit use cases that guide when to use this tool. It does not explicitly state when not to use it or suggest alternatives, but the context is clear enough for an agent to decide.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral details: fill check (realizable_edge_pp ≤ 0 means do not trade), semantic anchor (Jaccard similarity threshold), partition filter (placeholder slugs). No contradictions with annotations.

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

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

The description is lengthy but well-structured: first sentence states purpose, then breaks down modes with examples and technical details. Every sentence adds value given the tool's complexity. Could be slightly more concise, but the front-loading is 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?

The description covers all aspects of the tool: three modes, fill check, semantic anchor, partition filter, response structure (opportunities[], partition_check with fields), and even references custom sizing via polymarket_fill_risk. Despite no output schema, the response is well detailed. The tool is fully explained with 2 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% with descriptions for both parameters. The description adds extra value by explaining how to use slugs vs URLs, providing seed question examples, and describing the behavior of each mode. This goes beyond the schema's basic description, enhancing the agent's understanding.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes: no args (trending_scan), event, and topic. The verb 'find' and resource 'arbitrage opportunities' are specific. It differs from sibling tools like polymarket_edges and polymarket_fill_risk by focusing on comprehensive arbitrage detection.

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

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

The description explicitly tells when to use each mode: no args for trending scan, event for a specific market, topic for cross-event scanning. It includes recommendations (e.g., 'event recommended for a specific market') and explains the benefit of cross-event mode. However, it does not explicitly list alternatives or when not to use this tool, though the context is clear.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant context: explains caching (1h at KV level), model families, slippage handling, tradeable-edge filters, and diagnostics. 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?

While the description is front-loaded with purpose, it is very long and dense with technical details on model families (e.g., GDELT, lognormal barrier, Kelly fractions). An AI agent may find it overwhelming, though the detail is informative.

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

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

For a tool with 9 parameters and no output schema, the description thoroughly explains the output structure (by_segment, fed_candidates, _diagnostics), edge calculation, and tradeable-edge filters. It compensates for missing output schema completely.

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

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

Schema coverage is 100%, so baseline is 3. The description adds practical usage advice for parameters like slippage_pp ('Bump for very thin partitions') and min_liquidity, providing value beyond schema descriptions.

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

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

The description clearly states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price,' with a specific verb and resource. It distinguishes itself from siblings like polymarket_arbitrage by focusing on edge detection from data disagreement.

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 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets,' indicating when to use. It does not explicitly state when not to use or compare to alternatives, but the context is clear.

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

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

With readOnlyHint=true, idempotentHint=true, etc., the description adds behavioral details: reads from snapshots (not live), decay computed from daily closes, history bounded by TTL, gaps meaning no scan. No contradiction with annotations.

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

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

The description is detailed and front-loaded with the core question. However, it is somewhat verbose with extensive explanation of response structure and limits. Could be slightly tightened.

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

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

Despite no output schema, the description fully explains the response fields (tracked, expired, snapshot_dates) and covers edge cases (gaps, TTL, decay computation). Completeness is excellent.

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

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

Schema has 100% coverage, but description adds value: default values, clamp range for 'days', list of options for 'window', and explains effect on history depth and snapshot availability.

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: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It clearly distinguishes from sibling tool 'polymarket_edges' by focusing on historical tracking and decay analysis.

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

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

The description implies usage context (when you need edge history and decay) but does not explicitly state when not to use this tool or name alternative tools. However, it provides practical constraints like snapshot TTL and gaps.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, establishing safety. The description adds rich behavioral context: walking the ladder, returning top_of_book, vwap_fill_price, slippage_pp, verdict categories, thin legs, forced directional risk. Warns about partial fills and unhedged positions. 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?

Description is comprehensive yet efficiently structured with clear sections: REQUIRES, SINGLE-MARKET, BASKET, and a usage note. Every sentence adds value, no redundancy. Length is justified by the tool's dual-mode complexity.

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

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

No output schema exists, so description fully covers return fields for both modes: lists all return values (top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict for single; theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg detail, thin_legs, max_clean_notional_usd, forced_directional_risk for basket). Also explains verdict categories and risk of partial fills. Completeness is high given tool 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%, but description adds meaning beyond schema: clarifies size_usd interpretation (max spend on buys, target proceeds on sells; for basket, settlement notional shares per leg). Explains side defaults per mode (single-market default buy_yes, basket default auto from partition sum). All parameters are elaborated with usage context.

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

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

The description clearly states the tool checks realizable-vs-theoretical edge using live CLOB order-book depth. It distinguishes between single-market and basket modes and specifies return fields. It also explicitly contrasts with sibling tools polymarket_arbitrage and polymarket_edges, advising use before acting on their signals.

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

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

Explicitly states when to use: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also explains consequences of not using it, e.g., 'partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L)'.

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 extensive detail: explains compatibility_warning cases, temporal alignment checks, skipped leg counters, and the logic behind spread computation, revealing all non-obvious behaviors.

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

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

The description is detailed and well-structured with sections, but somewhat lengthy. Some information (e.g., full list of topic shortcuts) could be derived from schema examples, making it slightly less concise.

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

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

Despite lacking an output schema, the description thoroughly explains the response format, safety fields, edge cases, and temporal alignment. Covers all necessary information for an agent to understand and use the tool correctly.

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

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

Schema description coverage is 100%. The description adds meaning by explaining the topic parameter's enumerated values and the override behavior of explicit tickers, providing context beyond the schema definitions.

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

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

The description clearly states the tool calculates the cross-venue spread between Kalshi and Polymarket for the same resolving question, with two modes (topic shortcuts and explicit tickers). It distinguishes itself from similar sibling tools like polymarket_arbitrage by focusing on inter-venue comparison.

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 describes two modes and when to use each, explains that most pre-mapped topics return compatibility warnings, and warns that spreads are rare despite shortcuts. Provides clear context for appropriate 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 already declare readOnlyHint=true, so the description's 'Fetch' is consistent. Beyond annotations, the description adds limited behavioral context (e.g., specific fields returned, data source Polygon.io), but does not disclose rate limits, authentication needs, or return format. Given annotation coverage, this is adequate but not exceptional.

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

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

The description is a single concise sentence (17 words) that immediately states the tool's purpose. No superfluous information. It is front-loaded and efficient.

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

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

Given the tool's simplicity (2 parameters, output schema exists), the description covers the core function and data source. However, it lacks usage guidance and parameter details, which are important for correct invocation. For a simple tool with rich annotations and output schema, the description is minimally adequate but could be more complete.

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

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

Schema description coverage is 0%, so the description must compensate. It implies the 'ticker' parameter through 'US stock ticker', but does not explain the 'adjusted' boolean parameter. An agent would not know what 'adjusted' controls without external knowledge. This is insufficient for the low 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 states the verb 'Fetch' and resource 'previous trading session's open, high, low, close, and volume', specifying it's for US stock tickers from Polygon.io. However, it does not explicitly differentiate from sibling tools like 'daily_open_close' or 'aggregates', which might also provide price 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 provides no guidance on when to use this tool versus alternatives such as 'aggregates' or 'daily_open_close'. It does not mention prerequisites or exclusions, leaving the agent to infer usage context.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the tool is safety. The description adds scoping details (anonymous IP, BYO key hash, account ID) and explains the behavior of omitting the key (list all). No contradictions.

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

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

The description is concise and front-loaded with the main action. Every sentence adds value: retrieval purpose, scoping, pairing advice. No fluff or redundancy.

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

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

Given no output schema, the description partially explains return (value or list of keys) but does not specify format or confirm return structure. It covers key behavioral aspects but lacks explicit detail on what the response looks like.

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

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

Schema coverage is 100% for the single optional parameter 'key'. The description adds meaning by explaining that omitting key lists all saved keys, and that it functions as a memory key. This goes beyond the schema's basic description.

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

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

The description clearly states the tool retrieves a value saved via remember or lists all keys if omitted. It distinguishes itself from sibling tools remember (save) and forget (delete) by specifying the retrieval action and pairing advice. Concrete examples (ticker, address, notes) clarify usage.

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 when to use (look up context stored earlier, avoid re-deriving) and mentions pairing with remember/forget. It doesn't explicitly state when not to use, but the purpose is clear. No alternative tools are mentioned, but siblings are implied.

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), the description discloses that returned events carry source, citation_uri, and raw payload. It explains the mark_read parameter's effect on subsequent calls, adding useful 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 concise at three sentences, front-loaded with the purpose. Each sentence adds value: purpose, return fields, usage of parameters, and alternative endpoint. 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 lack of output schema, the description covers return fields and parameter semantics well. It also mentions polling suitability and an alternative endpoint. Minor omission: no mention of pagination or limit behavior, but overall sufficient.

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

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

With 100% schema coverage, the baseline is 3. The description adds value by explaining the effect of mark_read and providing an example for type filtering. However, it largely rephrases schema descriptions without introducing new meaning.

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

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

The description clearly states the tool pulls fired events from the user's subscription feed and returns recent alerts. It specifies the verb 'pull' and the resource 'subscription feed', making the purpose distinct from sibling tools like list_subscriptions or aggregate tools.

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

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

The description does not explicitly state when to use this tool versus alternatives. It mentions that polling works fine and provides an alternative REST endpoint for scripts, but does not give exclusions or compare against sibling tools like list_subscriptions or news.

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. The description adds significant behavioral context: multi-API fan-out, fallback logic (GDELT→GNews), soft-fail for USPTO, date format support, and return structure with citation URIs. 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 a single, dense paragraph with examples front-loaded for quick understanding. Every sentence provides essential information, avoiding any 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?

Despite the tool's complexity (multiple sources, fallback logic, date formats) and no output schema, the description thoroughly covers all behavioral aspects: source details, fallback, return structure, and alternative tool usage. It is complete for effective agent invocation.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds value by clarifying usage (e.g., relative shorthand examples like '7d', '30d') and typical values ('30d' for monitoring), beyond the schema's basic definitions.

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

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

The description clearly defines the tool as a change feed for a company aggregating from SEC EDGAR, GDELT/GNews, and USPTO. It distinguishes from sibling tool 'entity_profile' by specifying when to use each.

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 example queries and explicitly directs users to 'entity_profile' for static profiles. While it doesn't list all when-not scenarios, the context is clear enough for typical 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?

Description adds behavioral context beyond annotations: key-value scoping by identifier, persistence differences between authenticated and 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?

Four well-structured sentences, front-loaded with purpose, no unnecessary words.

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

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

For a simple key-value tool with two parameters, the description covers usage, behavior, and pairing with sibling tools 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 description coverage is 100%, so baseline is 3. The description adds minimal extra meaning (e.g., example keys) 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 the tool's function: 'Save data the agent will need to reuse later' as a key-value store. It distinguishes itself from siblings by mentioning pairing with recall and forget.

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

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

The description provides clear guidance on when to use ('when you discover something worth carrying forward') and mentions scoping and persistence. It lacks explicit when-not-to-use but otherwise 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?

Beyond annotations (readOnly, idempotent), the description reveals internal cascade behavior, auto-disambiguation for companies, and returns of citation URIs. These are meaningful operational details that inform the agent about side effects (none destructive) and output richness.

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 but well-structured, opening with examples, then the core purpose, then usage directive, and then detailed supported types. It packs many useful details into a few sentences, though slightly verbose for a resolver tool.

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

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

With no output schema, the description fully covers return values for both types, including citation URIs. It also explains input variants and auto-disambiguation, making it complete for an agent to understand behavior without additional lookup.

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, but the description adds value by explaining type-specific outputs (ticker+CIK vs RxCUI+brand) and providing concrete input examples (AAPL, ozempic). It goes beyond the schema's basic field descriptions.

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

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

The description clearly states a specific verb+resource: 'resolve a user-spoken NAME to the canonical/official identifier other tools require as input.' It also provides concrete examples and distinguishes itself from siblings by positioning it as the first tool to use when an ID is needed, not just another lookup tool.

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

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

Explicit usage guidance is present: 'Use FIRST whenever you have a name but need an ID.' It also specifies supported types and notes that it replaces multiple manual lookups, effectively telling agents when to invoke it as a shortcut 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, idempotentHint, and destructiveHint false, so the safety profile is clear. The description adds the return format (ranked list with score, confidence, signal density) and notes that the first entity is treated as the subject, which is useful behavioral detail. This is adequate but not exceptional.

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

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

Two sentences with no wasted words. First sentence states the action and key output, second provides concrete use case and return details. Perfectly front-loaded and efficient.

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

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

Despite having no output schema, the description adequately explains return values (ranked list with score, confidence, signal density). It covers constraints (2-8 entities, first treated as subject) and optional parameters (models, apiKey, context). The annotations already cover safety, so the description is complete.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema, such as noting that context disambiguates common names and that entities are brand/business/product names. This is sufficient but does not significantly enhance understanding.

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

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

The description clearly states it compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a concrete use case example and distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison).

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 mentions competitive AI-marketing audits and gives an example question, providing clear context for when to use. It does not explicitly state when not to use or list alternatives, but the context is sufficient for most agents.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true. Description adds significant behavioral context: fans out to two services, partial failure behavior (sources_failed list), and timing (5-30s for first measurement). No contradiction.

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

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

Description is front-loaded with the core purpose, then details, use cases, and limitations. Every sentence provides unique, actionable information without redundancy. Well-structured for quick comprehension.

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 lacking output schema, the description fully explains return values including summary block, advisory details, links, and alternative versions. Also covers edge cases (partial failures, fallback) and ecosystem scope. Complete for a composite scanning 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 clear descriptions. Description adds value by explaining version defaults to latest, and that package is for npm ecosystem. It also ties parameters to the composite check behavior, slightly exceeding the 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 composite purpose: 'should I add this npm package to my project' check, listing specific data sources (deps.dev and bundlephobia) and output fields. It distinguishes the tool from alternatives by noting that other ecosystems fall under deps.dev:version directly.

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

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

Explicitly tells when to use: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also provides when-not-to (other ecosystems) and behavioral caveats (bundlephobia first-time delay, graceful degradation).

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable behavioral details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K character cap with truncation flag, and character offsets on passages.

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

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

Four dense sentences with zero waste. Front-loads purpose and usage, then technical details. 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?

Covers purpose, when to use, technical behavior (embeddings, windows, cap), pairing with sibling, and return value format. No output schema but mentions passages with offsets and scores, which suffices.

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

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

Schema coverage is 100% but description enriches each parameter: explains text max length, query as natural-language with examples, and limit default and range. Adds context beyond schema.

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

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

Description starts with 'Semantic search INSIDE a fetched record', clearly stating the verb (semantic search) and resource (inside a fetched record). Provides examples like SEC 10-K body and distinguishes from sibling 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?

Explicitly states when to use: 'when the record is too big to cram into the prompt'. Also pairs with ask_pipeworx_grounded, giving clear alternative usage context.

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

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

Annotations already declare read-only, open-world, idempotent, non-destructive. Description adds context: data source (Polygon.io), geographic scope (US-listed), and output fields. No contradictions.

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

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

Two concise sentences, front-loaded with purpose and key details. No superfluous content.

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?

Output schema provides structure, so description need not detail return format. It covers domain, source, use case, and output fields. Lacking parameter explanations reduces completeness slightly.

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 0% schema coverage, description does not explain parameters (limit, ticker, execution_date) beyond mentioning that ticker and execution_date appear in output. Agent lacks guidance on parameter usage, format, or constraints.

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 returns historical stock splits for US-listed Polygon.io tickers, including specific fields (split ratio, execution date, ticker). This distinguishes it from sibling tools like 'aggregates' or 'dividends'.

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 describes a use case: adjusting historical price comparisons across split events. However, it does not mention when not to use it or directly compare with alternatives.

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

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

Annotations declare idempotentHint=true, but the description indicates each call returns a new subscription id, implying non-idempotent behavior. This is a contradiction. The description does disclose auth requirements and constraints (SMS cap, phone verification), adding value beyond annotations, but the idempotency issue reduces transparency.

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

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

The description is front-loaded with purpose and return, then requirements, types, and delivery. It is fairly long but well-structured. Some detail could be removed or placed in schema, but it remains clear and organized.

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

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

Given the tool's complexity (multiple types, delivery options, constraints), the description covers auth, type-specific parameters, delivery details (including webhook signing and auto-disable), and return value. No output schema exists, but the return is adequately described. It enables an agent to use the tool correctly.

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

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

Schema covers all parameters (100% coverage), and the description adds significant detail with examples for each type (e.g., sec_8k items, polymarket_edge topic) and delivery channel constraints (webhook signing, SMS cap). This goes well beyond the schema descriptions.

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

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

The description clearly states the verb (create), resource (subscription), return value (new subscription id), and distinguishes from siblings like list_subscriptions and unsubscribe. It lists supported types and delivery channels, making the purpose unambiguous.

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

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

The description provides context for when to use the tool (proactive monitoring), specifies requirements (Pipeworx OAuth account), and details type-specific parameters. However, it does not explicitly exclude use cases or compare to alternatives like ask_pipeworx or polymarket_edges, leaving some 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?

Annotations already indicate readOnly, idempotent, and openWorld. The description adds substantial behavioral detail: it returns category-bucketed example questions with exact tool+argument shapes, drawn from a live catalog, and can be called with no arguments or a topic. No contradictions.

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

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

The description is a bit long but front-loaded with common user questions and well-structured. Every sentence adds value: scope, return format, parameter usage, and when to use. A slight trim could improve conciseness, but overall 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?

Given no output schema, the description adequately covers the return format (category-bucketed examples). It also addresses how to call meta-tools and provides sufficient context for an agent to understand the tool's role and behavior without additional information.

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

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

The schema coverage is 100% with one parameter already described. The description goes further by listing example topic values and explaining the effect of omitting the parameter (full spread) versus passing a topic (focused). This adds significant value beyond the schema.

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

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

The description clearly states it is the onboarding entry point for a newly connected agent, using common trigger phrases and explicitly listing what it returns (category-bucketed example questions). It distinguishes itself from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on demonstrating the agent's capabilities.

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 when to use it ('FIRST when you do not yet know what Pipeworx can do') and explains how to pass an optional topic for focus. While it doesn't state explicit cases when NOT to use it, the usage context is clear and actionable.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description adds specific behavioral context by listing the fields returned, enhancing transparency 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?

Single sentence, front-loaded with key purpose and a list of returned fields. Every word adds value; no redundancy.

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

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

Given the tool's simplicity (one parameter, read-only) and the presence of an output schema, the description is fully sufficient. It explains what the tool returns without needing to detail the output schema.

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

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

Schema coverage is 0%, so description must compensate. The parameter name 'ticker' is self-explanatory and the schema includes an example ('AAPL'), but the description does not elaborate on format or constraints, providing minimal added value.

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

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

The description clearly states it fetches full reference details for a single ticker, listing specific fields. This distinguishes it from sibling tools like 'tickers' which likely handle multiple tickers.

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

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

The description provides clear context for when to use this tool (detailed info for a single ticker), but does not explicitly exclude alternatives or mention 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 already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false, covering key behavioral traits. Description adds that it searches a 'reference universe' and returns specific fields, which is useful but not substantial beyond annotations.

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

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

Two sentences: first states the core function and output, second provides a practical use case. No redundant or wasted words. Front-loaded with the most important 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?

With an output schema present, return values are covered. Annotations cover safety. Description adds use case and return fields. However, it lacks parameter explanations for a tool with 8 parameters and 0% schema coverage, which slightly reduces completeness.

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

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

Schema description coverage is 0%, and the description does not explain any of the 8 parameters (e.g., search, limit, market, active). It provides an example with 'search: Apple' but fails to add meaning for other parameters, leaving the agent without guidance on parameter usage.

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

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

Description clearly states the tool searches Polygon.io for US stocks, options, indices, forex, and crypto tickers, specifying the return fields (symbol, name, market, etc.) and gives an example use case (resolving company name to symbol). This distinguishes it from sibling tools like ticker_details which serve a different 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?

Description explicitly says 'Use to resolve a company name to a tradable symbol', providing clear context for when to use this tool. It does not list alternatives or exclusions, but the sibling tool names (e.g., ticker_details) imply differentiation.

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 that the row is deactivated (not deleted) and that historical events remain available. This adds value beyond annotations, which already indicate 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?

Two sentences with no wasted words. Front-loaded with the action, then additional context. Highly efficient.

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

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

For a simple tool with one parameter and no output schema, the description covers all necessary aspects: purpose, ownership constraint, and side effect (deactivation). Complete.

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

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

Schema coverage is 100%, and the description adds that the id is returned by subscribe, which is minor. No additional semantic value beyond the schema for the 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 action 'Cancel a subscription by id', specifying the verb and resource. It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by focusing on cancellation.

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 context: ownership is enforced so only own subscriptions can be cancelled. It does not explicitly list alternatives but implies when to use by stating the limitation.

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 goes well beyond the annotations (readOnlyHint, openWorldHint, idempotentHint) by explaining the two verification paths, the tolerance override behavior, the return structure with verdicts and citations, and the absence of side effects. No contradiction with annotations exists; the description enriches understanding of the tool's 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 slightly lengthy (6 sentences) but efficiently front-loads key information. Every sentence serves a purpose, with no redundancy. Minor improvement might be trimming the example permutations at the beginning, but overall structure is good and not overly 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 fully explains return values (verdict types, actual value with citation, reasoning) and covers the two pipeline cases. For a tool with 2 parameters and no nested objects, the description provides complete operational context for an AI agent to invoke and interpret results correctly.

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

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

Schema description coverage is 100% for both parameters, but the description adds significant meaning: it explains the tolerance_pct's override functionality, default behavior, and provides a concrete example format for the claim parameter. This adds substantial value beyond the schema alone.

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

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

The description clearly defines the tool as a natural-language claim verification against authoritative sources, with a specific verb (validate) and resource (claim). It distinguishes from siblings by stating it replaces 4-6 sequential calls and details two distinct pipelines (SEC EDGAR for financial claims, grounded pipeline for others), making the purpose unambiguous.

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

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

The description explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and provides example claim types. While it mentions automatic routing and replaces multiple steps, it does not explicitly state when NOT to use it or list alternatives, which would improve guidance. However, the context is clear enough for correct selection.

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