Polygon Io

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is clear. However, the description adds no additional behavioral context such as pagination, rate limits, or output structure, which would be helpful.

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 (2 words) for a tool with 8 parameters and 5 required fields. It is under-specified and does not earn its place by conveying useful information beyond the name.

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

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

Given the complexity of the tool (8 params, no output schema), the description is severely incomplete. It fails to explain what the tool returns, how parameters affect results, or any side effects.

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% for 8 parameters. The description 'OHLC bars' does not explain any parameter meanings (e.g., 'ticker', 'multiplier', 'timespan'), leaving the agent to infer or guess.

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 'OHLC bars' identifies the resource (OHLC bar data) but lacks a verb specifying the action (e.g., get, list). While it vaguely indicates the tool returns OHLC data, it does not fully clarify the operation or distinguish it from siblings like '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 provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, context, or scenarios where this tool is preferred over other data tools.

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

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

Annotations already indicate readOnly, openWorld, non-destructive. Description adds rich behavioral context: routes to 2,520 tools, fills arguments, returns structured answer with pipeworx:// 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 relatively long but front-loaded with the key 'PREFER OVER WEB SEARCH' directive. Each sentence adds useful information (use cases, examples, how it works). Could be slightly trimmed, but no fluff.

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

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

Given the tool's complexity (routing to many sources) and simple schema (1 param, no output schema), the description fully covers behavior: routing, citation URIs, and extensive examples of applicable domains. No gaps.

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

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

Schema coverage is 100% with parameter 'question' described as natural language. The description adds value by listing example questions and domains, which clarifies the expected input format and scope beyond the basic schema.

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

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

The description clearly states the tool's purpose: answering factual questions about current/historical data across many domains, with a strong directive to prefer over web search. It distinguishes itself from sibling tools by being a general-purpose routing tool, unlike the specific financial/entity tools listed.

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 'PREFER OVER WEB SEARCH' and gives specific when-to-use cues ('what is', 'look up', etc.) and examples. It implies web search as an alternative but does not explicitly state when not to use, though the scope is clear enough.

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

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

Annotations already indicate read-only behavior (readOnlyHint=true) and no destructive actions (destructiveHint=false). Description adds good context about automatic market resolution, bet classification, and fan-out to multiple data packs. Could mention any rate limits or caching behavior, but overall transparent.

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

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

Description is a single paragraph that front-loads the core purpose. Every sentence adds value, but it is slightly verbose. Could be tightened, but still efficiently conveys essential information.

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

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

No output schema, so description must explain returns. It does so: 'evidence packet plus a simple market-vs-model comparison'. Also mentions classification. Misses details on evidence packet structure, but for a 'core demo product' the description is sufficient for an agent to understand 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?

Schema covers both parameters with descriptions (100% coverage). Description adds meaning by clarifying the 'market' parameter accepts slugs, URLs, or question text. The 'depth' parameter is mentioned indirectly via 'fans out', but not explicitly described; however, schema enum covers this adequately.

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 researches Polymarket bets by pulling Pipeworx data, resolving markets, classifying bets, and returning an evidence packet with comparison. It distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on context rather than arbitrage or edges.

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

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

Explicitly lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. Implicitly excludes arbitrage tasks covered by separate tools.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds value by naming data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs) and mentioning citation URIs, providing behavioral context 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 compact yet thorough, using two paragraphs to cover purpose, use cases, parameter details, and data sources. Every sentence contributes meaningful information without redundancy.

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

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

Given the tool's complexity (two entity types, multiple data sources) and lack of output schema, the description adequately describes inputs and hints at outputs ('paired data + citation URIs'). A more detailed output structure would improve completeness, but it's sufficient for selection.

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 enriches parameter meaning by providing examples (e.g., tickers for company, drug names) and clarifying mapping per entity type. This helps the agent understand the expected format beyond the enum and array 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 identifies the tool's function: comparing 2–5 companies or drugs side by side. It uses specific verbs ('compare') and resources, and distinguishes from sibling tools that focus on single entities or other operations.

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

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

The description provides explicit usage triggers like 'compare X and Y' and example use cases. While it doesn't explicitly state when not to use, the context of sibling tools and the 'replaces 8–15 sequential calls' implies it's the preferred comparison tool.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds minimal behavioral context beyond the data fields, but does not contradict annotations.

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

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

The description is extremely short (one phrase), which is concise but lacks structure. It front-loads meaning but omits essential details, so it is borderline inadequate.

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 three parameters, no output schema, and sibling tools, the description is insufficient. It does not explain return values, data range, or how to interpret the output, leaving significant gaps.

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

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

Schema description coverage is 0%, and the description provides no information about any parameter. The agent receives no help on formats, defaults, or the meaning of 'adjusted'. This is a critical gap.

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 'Daily O/H/L/C + after-hours' clearly indicates the tool provides daily open, high, low, close, and after-hours data. However, it does not explicitly state the action (e.g., 'retrieves'), and does not differentiate from sibling tools like 'aggregates' 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?

No guidance is provided on when to use this tool versus its siblings. There is no mention of context, prerequisites, or alternatives, leaving the agent uncertain about selection.

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 description adds only that it returns names+descriptions, which is sufficient 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?

Single paragraph covering purpose, scope, and usage instruction. Efficient but could be broken into shorter sentences for readability.

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

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

No output schema, but description explains return format (top-N tools with names+descriptions). Provides clear usage context and domain examples, fully adequate for a discovery 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 has 100% description coverage for both parameters (query, limit). Description provides example queries but adds no new constraints or format details 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 explicitly states 'Find tools by describing the data or task' and contrasts with sibling tools by positioning as a discovery tool to browse the option set before using specific tools.

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

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

Explicitly advises 'Call this FIRST when you have many tools available' and lists multiple domains (SEC filings, financials, etc.), but lacks negative guidance on 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 indicate readOnlyHint=true and openWorldHint=true. The description adds no additional behavioral context, such as pagination, date handling, or data source, which are needed for a data retrieval tool.

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

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

The description is extremely short, but this is under-specification rather than conciseness. It lacks structure and fails to provide essential information.

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

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

Given the presence of three parameters and no output schema, the description is completely inadequate. It does not help an AI agent understand input constraints, expected output, or how to use the tool reliably.

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 three parameters (limit, ticker, ex_dividend_date). Without compensation, the parameters remain meaningless.

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 is a single word 'Dividends.', which is a tautology of the tool name. It does not specify any verb or resource beyond the name, failing to clarify the tool's 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?

No guidance is provided on when to use this tool versus alternatives like 'aggregates' or 'splits'. The description lacks any context for appropriate usage.

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

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

Describes return data (SEC filings, fundamentals, patents, news, LEI) and citation URIs, adding context beyond annotations like readOnlyHint. 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?

Six efficient sentences front-load the purpose, then cover use cases, return contents, and parameter usage without extraneous 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?

Despite no output schema, description fully explains returned data types and source systems, sufficient for an agent to understand expected output.

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

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

While schema covers 100% of parameters, description adds meaning: type currently only 'company', value requires ticker or CIK (not name), and references resolve_entity for name resolution.

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 'Get everything about a company in one call' with specific examples like 'tell me about X' clearly defines the tool's purpose and distinguishes it from siblings like ticker_details or aggregates.

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

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

Explicitly lists when to use (e.g., user asks 'tell me about X') and when not to (e.g., names not supported, use resolve_entity first), providing clear 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 readOnlyHint=true, openWorldHint=true, and destructiveHint=false, providing basic behavioral context. However, the description adds no additional information beyond the annotations, such as response format, pagination, or specific constraints. The openWorldHint is not clarified.

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 (one word), but this is under-specification rather than effective conciseness. A single word fails to convey necessary information and does not earn its place as a tool description.

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

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

Given the tool's complexity (2 unspecified parameters, no output schema), the description is severely incomplete. It provides none of the context needed for an agent to use the tool correctly, such as filter behavior, default values, or response structure.

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 two parameters ('locale' and 'asset_class') with no descriptions (0% schema coverage). The description does not explain their meaning, expected formats, or possible values. With no enum constraints or additional context, the agent has no way to know how to use these parameters.

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

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

The description is a single word 'Exchanges,' which is a tautology of the tool name. It does not specify a verb or resource, leaving the tool's purpose entirely ambiguous. There is no differentiation from sibling tools like 'aggregates' or '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?

There are no usage guidelines. The description provides no context on when to use this tool versus alternatives, no prerequisites, and no exclusions. Sibling tools have clear purposes (e.g., 'ticker_details'), but 'exchanges' offers no guidance.

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

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

Annotations already indicate destructiveHint=true. Description adds context about clearing sensitive data, but doesn't detail side effects or reversibility. Still adequate.

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

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

Two sentences, front-loaded with action, no unnecessary words. Efficient and clear.

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

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

For a simple one-parameter destructive tool with no output schema, the description covers purpose, usage scenarios, and sibling relationships 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% (key parameter described). Description adds no additional meaning beyond 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 explicitly states 'Delete a previously stored memory by key,' using a specific verb and resource. It distinguishes from siblings by mentioning 'Pair with remember and recall.'

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

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

Provides explicit conditions: 'Use when context is stale, the task is done, or you want to clear sensitive data.' Also references siblings for 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 indicate readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context beyond that. It does not mention what data is returned, date range handling, or ticker scope, which are important for an agent to invoke the tool correctly.

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 this is under-specification rather than conciseness. A useful description would add at least one more sentence explaining the tool's output and parameters.

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 0% schema description coverage and no output schema, the description is the sole source of context, yet it provides almost nothing. The agent cannot determine the tool's purpose, input format, or return value.

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, but it provides zero information about the 'date' and 'adjusted' parameters. The agent has no guidance on date format or the meaning of 'adjusted'.

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 'All-ticker daily' is vague and does not specify a verb or resource. It is unclear what action the tool performs (e.g., retrieves, groups, calculates) and what 'grouped' means. Sibling tools like 'daily_open_close' and 'previous_close' have clearer purposes.

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

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

No guidance is provided on when to use this tool versus alternatives like 'aggregates' or 'daily_open_close'. The description does not differentiate it from siblings or indicate context for 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, destructiveHint=false, and openWorldHint=true. The description adds that the tool returns 'upcoming' holidays, but provides no further behavioral context (e.g., data source, update frequency, error cases). It adds minimal value 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 extremely short (two words), which is concise but borderline under-specified. While it avoids verbosity, it could benefit from slight expansion (e.g., 'Returns a list of upcoming market holidays') without losing conciseness.

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

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

Given the simple interface (no parameters, no output schema), the description is adequate but vague. It does not specify which markets or holidays, nor the format of the output. The 'openWorldHint' annotation suggests dynamic data, but the description alone leaves ambiguity.

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, and the schema coverage is 100% (empty). The description does not need to explain parameters, so the baseline score of 4 applies.

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 'Upcoming holidays' clearly states the tool returns a list of holidays, and the word 'upcoming' indicates temporal scope. It distinguishes from sibling tools like 'market_status' (market condition) and 'daily_open_close' (trading hours) by focusing strictly on holidays.

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 provided on when to use this tool versus alternatives. There is no mention of prerequisites, limitations, or when not to use it, leaving the agent to infer based solely on the name.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds no additional behavioral context (e.g., what data is returned, latency, or scope). 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?

Extremely concise and front-loaded with a single sentence. However, the lack of structure (e.g., no bullet points or expanded details) limits its helpfulness.

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 parameters and no output schema, the description is minimally adequate. However, for a tool named 'market_status' among many market-related siblings, more context about what status includes would improve 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?

No parameters exist, so the schema coverage is trivially 100%. The description does not need to add parameter info, earning a baseline score of 4.

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

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

The description 'Current market status.' indicates the tool returns market status, but is vague about what 'status' entails (e.g., open/closed, trading hours). It partially distinguishes from siblings like market_holidays and daily_open_close but lacks specificity.

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

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

No guidance on when to use this tool versus alternatives like market_holidays or daily_open_close. No comparison or context 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 indicate readOnlyHint: true and destructiveHint: false, suggesting a safe read operation. However, the description adds no behavioral context beyond what the annotations already imply, such as rate limits or data sources.

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 lacks essential details. It is underspecified, not concise; every word earns its place but there is too little substance.

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 5 parameters with no descriptions, no output schema, and sibling tools for financial data, the description is completely inadequate. An agent cannot determine what the tool returns or how to use it properly.

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 tool description provides zero parameter information. Parameter names like 'sort', 'ticker', 'limit' are somewhat self-explanatory, but no types, formats, or meanings are clarified, leaving the agent to guess.

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 'News.' is a tautology, merely repeating the tool name. It fails to specify what the tool does, what kind of news it provides, or how it differs from sibling tools like 'ticker_details' 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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or 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?

Beyond annotations (readOnlyHint=false, destructiveHint=false), the description adds rate limiting (5 per identifier per day), quota info (free, doesn't count against tool-call quota), and lifecycle details (digests read daily, roadmap impact). Fully transparent about 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 about 5 sentences, front-loaded with purpose, and each sentence adds essential information: usage scenarios, constraints, and tips. No fluff; efficiently communicates all necessary 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 3 parameters (including a nested object) and no output schema, the description sufficiently covers purpose, when to use, behavioral traits, and parameter context. It provides all needed context for correct invocation.

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

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

The input schema already provides detailed descriptions for all parameters (100% coverage). The description adds no new parameter-level semantics beyond what the schema offers, so baseline score of 3 applies.

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

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

The description clearly states the tool's purpose: sending feedback (bug, feature, praise) to the Pipeworx team. It distinguishes from any sibling tool by focusing on feedback, unlike the other tools which handle data retrieval or 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?

Explicitly tells when to use (bug, feature/data gap, praise) and when not to (avoid pasting end-user prompts). Provides clear guidance on context and what to describe in terms of Pipeworx tools/packs.

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 state readOnlyHint=true and openWorldHint=true, indicating no mutation and external data. The description adds behavioral context: it searches markets, groups them, checks monotonicity, and returns ranked opportunities with reasoning. No contradictions with annotations.

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

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

The description is well-structured, starting with the main purpose, then explaining two modes with clear separation. Every sentence adds value without redundancy. It is appropriately sized for the complexity of the tool.

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

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

Given no output schema, the description sufficiently explains return values (ranked opportunities with reasoning). It covers all aspects: purpose, modes, example inputs, and output. No gaps for an arbitrage detection tool with two modes.

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 event and topic parameters, each with clear descriptions. The tool description adds value by explaining the two modes and providing usage examples, going beyond the schema's basic descriptions.

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

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

The description clearly states the tool finds arbitrage opportunities by checking monotonicity violations on Polymarket, with two distinct modes. It distinguishes between single-event and cross-event modes, and explains why cross-event catches cases single-event misses. This specificity differentiates it from sibling tools like polymarket_edges.

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

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

The description explicitly lists two modes and when to use each, with examples. It explains the limitation of single-event mode and when cross-event is necessary. It does not directly compare to sibling tools, but provides clear context for mode selection.

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 and destructiveHint. The description adds significant behavioral context: it explains the model, grouping by asset, fetching price history once, ranking by edge magnitude, and returning suggested trade direction. 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 that front-loads the main action. While it is somewhat lengthy (5 sentences), each sentence provides necessary context. Could be slightly more concise but overall well-structured.

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

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

No output schema is provided, and the description only vaguely mentions returning 'top N ranked by edge magnitude with suggested trade direction.' It does not specify the fields or types of the output, which would aid agent understanding. Adequate but not comprehensive.

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

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

Input schema has 100% description coverage with clear descriptions for limit, window, and min_edge_pp. The description restates default values but does not add new semantic meaning 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 the tool scans highest-volume Polymarket markets and returns those with the largest disagreement between Pipeworx data and market price, using a specific model (lognormal from FRED, coinpaprika). It distinguishes from siblings like 'polymarket_arbitrage' by focusing on edge detection for betting opportunities.

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 frames the tool for the 'what should I bet on today' question, indicating its intended use. However, it does not provide explicit when-not-to-use guidance or alternatives, though the context implies it is for opportunity discovery.

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

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

The description adds no behavioral insight beyond the annotations. Annotations already indicate read-only and non-destructive behavior, but the description fails to mention any side effects, data sources, or limitations.

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 but at the expense of clarity. It is under-specified, failing to convey essential information in minimal 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 and lack of output schema, the description is incomplete. It does not explain return values, format, or how it differs from related tools like 'daily_open_close'.

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 explain what 'ticker' and 'adjusted' mean. Instead, it provides no parameter information, leaving the agent to guess that 'adjusted' relates to adjustment for splits/dividends.

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 'Previous close.' is a tautology that restates the tool name without adding a verb or clarifying what action the tool performs. It does not differentiate from sibling tools like 'aggregates' 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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of typical use cases, prerequisites, or exclusions.

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

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

Annotations already declare read-only and non-destructive behavior. The description adds that it is scoped to an identifier (IP, key hash, account ID) and that omitting the key lists all saved keys, providing behavioral context beyond annotations.

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

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

The description is front-loaded with the main purpose and each sentence adds value, though it is slightly verbose. Could be more concise but still 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?

No output schema exists, but the tool is simple. The description covers scope, usage, and pairing. It lacks details on return format but is sufficient for retrieval.

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 already describes the 'key' parameter with 100% coverage. The description adds the important detail that omitting it lists all keys, enhancing meaning.

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

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

The description clearly states the tool retrieves a saved value or lists keys, with specific examples (ticker, address, notes). It distinguishes from sibling tools 'remember' and 'forget' by mentioning them.

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

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

Explicit guidance: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' Also mentions scoping and pairing with other tools, providing clear when-to-use context.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds operational transparency by stating it fans out to three sources in parallel and returns structured changes with citation URIs. 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?

The description is concise, containing all essential information in a single paragraph without redundancy. It is front-loaded with the core purpose. Minor improvements could include bullet points for clarity, but the current structure 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?

Despite having no output schema, the description adequately explains the return format (structured changes, count, URIs). It covers parameter formats, data sources, and typical usage. No gaps are apparent given the tool's complexity.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the `since` parameter format (ISO or relative, with example '30d'), the `type` parameter is limited to 'company', and `value` can be a ticker or CIK. This goes beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: retrieving recent changes for a company over a specified time window. It provides specific query examples ("what's happening with X?") and distinguishes itself by detailing the three data sources (SEC, GDELT, USPTO), setting it apart from sibling tools like '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?

The description explicitly states when to use the tool with natural language examples ("Use when a user asks 'what's happening with X?'"). However, it does not mention when not to use it or suggest alternative tools for other scenarios, which would strengthen the 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?

Discloses key-value storage, scoping by identifier, and retention policy (persistent for authenticated, 24h for anonymous). Annotations align (readOnlyHint=false, destructiveHint=false).

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 purpose, efficient sentences, 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?

Covers all essential aspects: storage purpose, key-value format, scoping, retention, and tool pairs; no output schema 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?

Input schema has 100% coverage with descriptions; description adds examples of keys and values, enhancing beyond schema.

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

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

Description clearly states verb 'Save' and resource 'data', lists example use cases, and distinguishes from sibling tools like recall and forget.

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

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

Explicitly says when to use (discover something worth carrying forward), mentions pairing with recall/forget, and explains persistence behavior.

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

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

Annotations already indicate read-only, open-world, non-destructive. Description adds that it returns IDs plus citation URIs and handles multiple input formats. This provides useful behavioral context beyond annotations, though no mention of error handling or authority.

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

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

Four sentences, front-loaded with purpose, no redundant information. Every sentence adds value, including examples and usage hints.

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 lookup tool with no output schema, description sufficiently explains what is returned (IDs + citations) and the input types. Could mention behavior on no match, but otherwise complete for its complexity level.

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

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

Schema covers both params with descriptions (100% coverage). Description adds examples and explains acceptable input formats for company (ticker, CIK, name) and drug (brand/generic), exceeding schema 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 states the tool resolves entities to canonical identifiers (CIK, ticker, RxCUI, LEI), with specific verb 'Look up' and resource type. It distinguishes from siblings by focusing on identifier resolution for company/drug and mentions it replaces multiple lookups.

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

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

Explicitly says when to use ('when a user mentions a name and you need... the ID systems that other tools require as input') and that it should be used before other tools needing identifiers. Examples illustrate usage and the tool replaces 2-3 calls, providing clear context.

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

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

Despite annotations indicating readOnlyHint=true and safe behavior, the description adds no behavioral context. It does not disclose effects, return properties, or any operational details beyond what annotations already provide.

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

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

The description is extremely short but not concisely useful. A single word without structure or front-loading of key information represents under-specification rather than efficiency.

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 presence of three parameters and no output schema, the description is completely inadequate. It fails to equip an agent with sufficient information to select and invoke the tool correctly.

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

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

With 0% schema description coverage and three undocumented parameters (limit, ticker, execution_date), the description fails to add any meaning beyond the input schema. An agent receives no hints about parameter roles or formats.

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 'Splits.' is a tautology that merely restates the tool name without providing any verb or resource specification. It fails to convey what action the tool performs or what data it retrieves.

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?

There is no guidance on when to use this tool versus alternatives like aggregates or ticker_details. The description offers no context about appropriate scenarios or exclusions.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, covering safety. However, the description adds no extra behavioral context such as result size, pagination, or rate limits, which would be valuable 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 overly terse (3 words) and omits critical information. While conciseness is valued, it sacrifices clarity and completeness.

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

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

Given no output schema and minimal description, the tool lacks essential guidance on what data is returned. For a simple tool with one parameter, the description should at least hint at the output breadth (e.g., 'detailed reference 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?

With 0% schema description coverage, the description must compensate but fails. It does not explain what 'ticker' accepts (e.g., symbol format) or its role, merely echoing the parameter name.

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 'Ticker reference detail' is vague and does not specify what kind of detail (e.g., price, fundamentals, metadata). It is almost tautological with the tool name and fails to distinguish from siblings like 'aggregates', 'entity_profile', 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?

No guidance is provided on when to use this tool versus other ticker-related tools such as 'tickers' or 'entity_profile'. The description offers no context for selection.

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. The description adds nothing about behavior (e.g., pagination, result structure, rate limits). With annotations present, the bar is lower but the description still fails to provide 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 only two words, which is underspecified rather than concisely informative. It does not earn its place; it is too short to be useful.

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 8 parameters, no output schema, and many sibling tools, the description is completely inadequate. It does not mention return values, default behaviors, or how the search works.

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%, yet the description 'Ticker search.' explains none of the 8 parameters. The agent cannot understand what 'sort', 'type', 'search', etc., mean without additional information.

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

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

The description 'Ticker search.' is extremely vague. It does not specify what kind of search (e.g., fuzzy, exact) or distinguish from siblings like 'ticker_details' or 'aggregates'. It barely exceeds a tautology of the name.

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

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

No guidance on when to use this tool vs alternatives. Siblings like 'ticker_details' suggest this returns a list, but the description offers no context or exclusions.

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

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

Beyond annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false), description discloses domain limitation (company-financial claims, SEC EDGAR+XBRL) and return structure (verdict, citation, delta), but could mention that claims outside domain will fail.

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 succinct: 4 sentences covering purpose, usage trigger, domain restriction, and return value. No fluff.

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

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

With no output schema, description fully explains output types (verdict, citation, delta) and domain scope, making it complete for an agent to understand without additional info.

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

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

Schema coverage is 100%; description adds meaning with examples of natural-language claims ('Apple's FY2024 revenue was $400 billion'), clarifying the expected input format beyond parameter 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?

Description states specific verb 'fact-check, verify, validate' and resource 'natural-language factual claim against authoritative sources', clearly distinguishing from sibling tools like aggregates or compare_entities.

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

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

Provides explicit when-to-use: 'Use when an agent needs to check whether something a user said is true' and gives query examples, while also noting that it replaces 4-6 sequential calls.

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