econdata

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) are supported and expanded by the description, which clarifies default model is free, Anthropic requires BYO key, and cost implications.

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

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

Three sentences, each adding value: purpose, default behavior, and return format. 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?

Despite no output schema, the description fully explains return structure (per-model score, confidence, signals, raw_response) and key constraints (API key, disambiguation).

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%; the description enriches all parameters with examples ('Pipeworx', 'B2B SaaS') and clarifies optionality and key usage 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 specifies a unique verb ('probe') and resource ('LLMs for visibility'), lists default model and optional Anthropic, and clearly differentiates from siblings like scan_competitor_ai_presence by focusing on per-model scoring.

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

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

Provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') but does not mention when to avoid or compare with sibling tools.

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: Pipeworx 'picks the right tool, fills the arguments, and returns the result,' which explains the automated tool selection and parameter filling process. However, it doesn't mention rate limits, authentication needs, or error handling.

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

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

The description is front-loaded with the core functionality, followed by supporting details and concrete examples. Every sentence earns its place by clarifying the tool's value proposition, usage context, and practical applications 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?

For a single-parameter tool with no annotations and no output schema, the description provides good context about the automated backend process and example use cases. However, it doesn't explain the format or structure of returned answers, which would be helpful given the lack of 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 description coverage is 100%, so the schema already documents the single 'question' parameter. The description adds minimal value beyond the schema by reinforcing it's 'in plain English' and providing examples, but doesn't elaborate on constraints or format details. Baseline 3 is appropriate when 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 purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer from data source'), and distinguishes from siblings by emphasizing natural language interaction versus structured tool selection.

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 is provided: 'No need to browse tools or learn schemas — just describe what you need.' This clearly positions it as an alternative to sibling tools like discover_tools or get_series, indicating when to use this tool (natural language queries) versus others (structured tool invocation).

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 internal logic: market resolution, classification, fan-out to packs, and output format. Complements annotations (readOnly, openWorld) with functional details, 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?

Front-loaded with purpose, efficient use of sentences. Slightly verbose but no redundancy; every sentence adds information.

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

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

Given no output schema and only 2 simple parameters, description fully equips an agent to use the tool correctly, covering inputs, behavior, output structure, and use cases.

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 baseline is 3, but description adds value by explaining parameter flexibility (slug/URL/text for market, quick vs thorough for depth) 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?

Clearly states it researches Polymarket bets by pulling Pipeworx data, specifies inputs (slug, URL, or question), and outputs (evidence packet + model comparison). Differentiates from siblings like ask_pipeworx which is a general query 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?

Provides explicit use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. Lacks explicit alternatives but context makes it clear.

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

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

No annotations are provided, but the description discloses it returns paired data and resource URIs, and implies it's a read operation. It could explicitly state it has no side effects or is idempotent, but the given information is sufficient for safe usage.

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 the core action. Every sentence adds value: first sentence states what it does, second clarifies type-specific outputs and efficiency benefit.

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

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

Given no output schema, the description adequately explains the return data for each type. It could elaborate on the format or usage of the pipeworx URIs, but overall it provides sufficient context for an agent to understand the tool's 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?

The input schema descriptions already cover parameters fully, but the description adds significant context: for company type, it lists specific financial metrics (revenue, net income, cash, long-term debt), and for drug type, it lists counts (adverse events, FDA approvals, trials). This adds meaning beyond the schema.

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

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

The description clearly states the tool compares 2-5 entities side by side, specifies two entity types (company, drug) with distinct data fields, and distinguishes it from siblings by emphasizing batch comparison over sequential calls.

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

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

The description indicates this replaces 8-15 sequential calls, implying it's for multi-entity comparison where efficiency is needed. However, it does not explicitly state when not to use it or compare to specific siblings like resolve_entity.

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

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

No annotations are provided, so the description carries full burden. It discloses the behavioral trait of returning 'most relevant tools with names and descriptions' and suggests calling it first in large tool environments. However, it doesn't mention rate limits, authentication needs, or what happens with no matches, leaving some behavioral aspects unclear.

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?

Perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality, and the second provides crucial usage guidance. No wasted words, and information is front-loaded appropriately.

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 moderate complexity (search functionality with 2 parameters) and no output schema, the description provides good context about what the tool does and when to use it. However, without annotations or output schema, it could benefit from more detail about return format or error conditions to be 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%, so the schema already fully documents both parameters (query and limit). The description adds marginal value by emphasizing the natural language aspect of the query parameter ('by describing what you need'), but doesn't provide additional semantic context beyond what's in the schema.

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

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

The description clearly states the specific verb ('search') and resource ('Pipeworx tool catalog') with the purpose of finding relevant tools by describing needs. It distinguishes from sibling tools like get_cpi or get_employment_by_industry by focusing on tool discovery rather than data retrieval.

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

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

Explicitly provides when-to-use guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear context about appropriate usage scenarios and distinguishes it from direct data access 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns pipeworx:// citation URIs and bundles multiple data sources. It also hints at performance by noting that federal contracts are too slow to bundle. However, it does not detail failure modes, error handling, or what happens if an entity is not found.

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. It starts with the purpose, then lists the data sources, mentions the output format (URIs), and concludes with usage guidance. Every sentence adds value without redundancy.

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

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

Given the tool has only two parameters, no output schema, and no annotations, the description provides sufficient context for an agent to understand what the tool does, what it returns, and when to use alternatives. It covers the core functionality, constraints, and exceptions comprehensively for a tool of this 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?

The input schema already provides descriptions for both parameters (type and value) with 100% coverage. The description adds significant value beyond the schema by explaining that value can be a ticker or zero-padded CIK, and explicitly states that names are not supported, directing the user to resolve_entity. This extra context is helpful for correct usage.

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

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

The description clearly states that the tool returns a 'Full profile of an entity across every relevant Pipeworx pack in one call', specifies the 'company' type, and lists the specific data sources included (SEC filings, XBRL data, patents, news, LEI). It also distinguishes itself from sibling tools by noting it replaces multiple sequential calls and explicitly mentions that for federal contracts, one should use usa_recipient_profile 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?

The description provides explicit guidance on when to use the tool (for company profiles) and when not to (for federal contracts, use usa_recipient_profile). It also instructs that if only a name is available, one should first use resolve_entity, since names are not supported. This clear alternative and context are excellent.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Delete' implies a destructive mutation, the description doesn't specify whether this operation is reversible, what permissions are required, what happens on success/failure, or any rate limits. This leaves significant behavioral gaps for a destructive operation.

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

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

The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool and front-loads the essential information. Every word earns its place.

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

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

For a destructive mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'stored memory', what happens after deletion, error conditions, or return values. Given the complexity of a delete operation and lack of structured documentation, more context 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?

Schema description coverage is 100% (the 'key' parameter is fully documented in the schema), so the baseline is 3. The description adds no additional parameter semantics beyond what's already in the schema - it doesn't explain key format, constraints, or examples.

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 ('Delete') and the target resource ('a stored memory by key'), providing specific verb+resource information. However, it doesn't differentiate this tool from its sibling 'recall' (which likely retrieves memories) or 'remember' (which likely stores memories), missing explicit sibling differentiation.

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 like 'recall' or 'remember'. There's no mention of prerequisites, conditions for use, or exclusions. The agent must infer usage from tool names alone.

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

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

Annotations already declare the tool as read-only, open-world, and idempotent. The description adds value by explaining the exact behaviors: fetches the page, extracts title/description/key links, and emits standard markdown. This provides 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 very concise: three sentences that cover purpose, process, and use cases. No unnecessary words, and the most important information is front-loaded.

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 low complexity, the description is complete. It explains what is generated, how it works, and when to use it. The output schema is not needed since the output is described as a text blob. Annotations provide safety context.

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

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

The input schema has 100% coverage with descriptions for both parameters. The description mentions extracting 'key links' which relates to max_links, but doesn't add new semantics beyond what the schema provides. 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 generates an llms.txt file for a given URL, with the purpose of aiding AI crawlers. It specifies the action (generate), resource (llms.txt), and intended use cases. No sibling tool does this, so it's well-distinguished.

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

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

The description explicitly lists three concrete use cases: indexing a client's site, drafting for own project, or auditing competitor's AI visibility. It does not, however, mention when not to use the tool or alternatives, which would make it a 5.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool returns data but doesn't mention whether it's a read-only operation, potential rate limits, data freshness, error conditions, or authentication requirements. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is perfectly concise - two sentences that efficiently convey the tool's purpose and return format without any wasted words. It's front-loaded with the core functionality and follows with return details, making every sentence earn its place.

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

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

Given the tool's moderate complexity (economic data retrieval with optional date filtering), no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It explains what data is returned but doesn't cover behavioral aspects like data sources, update frequency, or error handling. The description meets basic requirements but leaves important contextual 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?

The description doesn't mention any parameters, but schema description coverage is 100% with both parameters well-documented in the schema (start_year and end_year as optional 4-digit strings). Since the schema does the heavy lifting, the baseline score of 3 is appropriate - the description adds no parameter information beyond what's already in the structured 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 specific action ('Get'), identifies the exact resource ('US Consumer Price Index for All Urban Consumers (BLS series CUUR0000SA0)'), and distinguishes it from siblings by specifying the particular economic indicator. It also details what data is returned ('year, month, and index value for each period'), 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 no guidance on when to use this tool versus the sibling tools (get_employment_by_industry, get_series, get_unemployment). It doesn't mention alternatives, prerequisites, or any context for choosing this specific CPI data tool over others. The agent must infer usage from the tool name alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the return format ('employment in thousands') but lacks details on data freshness, source, rate limits, error handling, or whether it's a read-only operation. For a data retrieval tool with zero annotation coverage, this leaves significant gaps.

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

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

The description is a single, efficient sentence that front-loads the purpose and includes essential details like industry options and return format. Every word earns its place without redundancy or unnecessary elaboration.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the core purpose and return units, but lacks behavioral context (e.g., data source, update frequency) and does not fully compensate for the absence of annotations or 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 description coverage is 100%, so the input schema fully documents all parameters. The description adds value by listing industry options and specifying the default, but does not provide additional context beyond what the schema already covers, such as date range implications or data availability 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?

The description clearly states the verb ('Get'), resource ('US non-farm payroll employment figures'), and scope ('by industry'), with specific industry options listed. It distinguishes from sibling tools like 'get_cpi' or 'get_unemployment' by focusing on employment data rather than inflation or unemployment rates.

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 employment data by industry, but does not explicitly state when to use this tool versus alternatives like 'get_series' or 'get_unemployment'. No guidance on prerequisites, exclusions, or comparative contexts 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?

No annotations are provided, so the description carries the full burden. It discloses the return format ('data points with year, period, and value') and provides example series IDs, which adds useful context. However, it lacks details on behavioral traits like rate limits, error handling, or data freshness, which are important for a data-fetching 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 front-loaded with the core purpose, followed by return details and examples, all in two efficient sentences with zero waste. Every sentence earns its place by clarifying functionality and usage.

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

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

Given no annotations and no output schema, the description is moderately complete for a simple data-fetching tool. It covers the purpose, return format, and examples, but lacks details on error cases, rate limits, or output structure beyond basic fields, which could be important for agent invocation.

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

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

Schema description coverage is 100%, so the schema already documents all parameters (series_id, start_year, end_year) with descriptions and optionality. The description adds value by providing example series IDs, but does not explain parameter semantics beyond what the schema provides, such as date format constraints or interactions between start_year and end_year.

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 specific action ('Fetch a BLS time series by series ID') and resource ('BLS time series'), distinguishing it from siblings like get_cpi, get_employment_by_industry, and get_unemployment by being a general-purpose series fetcher rather than a specific metric tool. It provides concrete examples of series IDs to illustrate its scope.

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

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

The description implies usage by providing example series IDs for common metrics (CPI, unemployment rate, employment), suggesting this tool is for general time series retrieval rather than specialized sibling tools. However, it does not explicitly state when to use this tool versus the alternatives or any exclusions, 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?

With no annotations provided, the description carries full burden. It discloses the return format (year, month, rate for each period) which is valuable behavioral information, but doesn't mention data freshness, source reliability, rate limits, error conditions, or whether this is a read-only operation (though 'Get' implies reading).

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 perfectly concise with two sentences that each earn their place: first establishes purpose and data source, second specifies return format. No wasted words, well-structured, and front-loaded with 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?

For a read-only data retrieval tool with 100% schema coverage and no output schema, the description provides good context: purpose, specific data series, and return format. However, it lacks information about data range defaults (what happens when no parameters provided), temporal granularity, or potential limitations that would be helpful for complete understanding.

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

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

Schema description coverage is 100%, so the schema already fully documents both optional parameters (start_year and end_year). The description adds no additional parameter information beyond what's in the schema, maintaining the baseline score for high schema coverage.

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

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

The description clearly states the specific verb ('Get') and resource ('US civilian unemployment rate over time'), identifies the exact data series (BLS series LNS14000000), and distinguishes from siblings by specifying the unemployment rate data rather than CPI, employment by industry, or generic series data.

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

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

The description implies usage for retrieving unemployment rate time series data, but provides no explicit guidance on when to use this tool versus alternatives like get_cpi or get_employment_by_industry. There's no mention of prerequisites, limitations, or comparative use cases.

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

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

Discloses rate limit (5/day/identifier) and describes feedback as 'Free.' Omits post-send behavior (e.g., confirmation) but sufficient for a simple feedback 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?

Concise four-sentence description front-loads purpose, then covers usage, exclusions, and constraints. 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?

Completeness adequate for a feedback tool: covers purpose, usage, constraints. Lacks post-send behavior details, but not essential.

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 with descriptions. Description adds content guidelines for 'message' field but does not significantly extend schema info for other 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?

Description clearly states the tool's purpose: 'Send feedback to the Pipeworx team.' It lists specific use cases (bug reports, feature requests, etc.) and distinguishes itself from sibling data/memory 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?

Explicit guidance on when to use (bug reports, etc.) and what not to include (end-user prompt). Rate limit stated. No need to exclude alternatives given distinct purpose.

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

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

Annotations already provide readOnlyHint, openWorldHint, etc. The description adds value by explaining data source ('CF analytics-engine'), privacy ('no PII'), data structure ('(pack, tool, count)'), and caching behavior ('Cached 5min-1h depending on window'). No contradiction.

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

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

The description is well-structured with a clear opening sentence followed by a bullet-like list of use cases. It is concise but could be slightly more streamlined without losing detail.

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

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

Despite no output schema, the description sufficiently explains the output format (top tools, top packs, total call volume) and data structure. Caching info is provided. Complete for a simple 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%. The description adds context for the window parameter: 'Shorter windows surface what's hot right now; longer windows show steady-state demand', explaining the semantics beyond the enum values.

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

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

The description clearly states the tool returns 'top tools, top packs, and total call volume over a recent window', with a specific verb and resource. It distinguishes itself from siblings like discover_tools by focusing on trending 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 explicitly lists three use cases (discovering hot data sources, confirming popular tool as canonical choice, seeing alignment with other agents). It does not mention when not to use or alternatives, but the use cases are 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 read-only, open-world, non-destructive. Description adds specifics: walks markets, groups them, checks monotonicity, returns ranked opportunities with reasoning. 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?

Efficiently front-loaded with purpose and modes. Uses bullet-like formatting for clarity. Slightly long but every sentence adds value given the two-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, but description specifies return type: ranked opportunities with suggested trade direction + reasoning. Covers all inputs, modes, and behavior comprehensively.

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

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

Schema coverage is 100% but description adds valuable context: event param accepts slug or URL, topic param describes cross-event search behavior with example.

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?

States it finds arbitrage by checking monotonicity violations. Clearly names two modes (event/topic) and explains the problem: single-event misses cross-event cases. Differentiates from siblings 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?

Explicitly tells when to use each mode: event for single slug, topic for cross-event. Explains why topic mode catches what event mode misses, providing clear exclusion logic.

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, non-destructive behavior. Description adds valuable context: fetches price history ONCE (caching), groups by asset, computes model probability, ranks by edge, and suggests 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?

Description is front-loaded with the main action, then details the model, workflow, and output. Slightly verbose but each sentence adds value. Could be more concise by trimming the model explanation, 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, but description explains return values (top N ranked by edge magnitude with suggested trade direction). Covers the entire workflow, parameters, and purpose. Fully adequate for an agent to understand tool behavior and 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?

Schema coverage is 100% with good parameter descriptions. The description mentions limit as 'Top N edges', window as 'Polymarket volume window', and min_edge_pp as 'minimum edge in percentage points', but does not add significant new meaning beyond the schema. Baseline of 3 is appropriate.

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

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

Description clearly states it scans highest-volume Polymarket markets for crypto-price bets, uses a lognormal model from FRED and live price data, and ranks by edge magnitude. It distinguishes itself from siblings like polymarket_arbitrage by focusing on opportunity discovery for daily betting. Specific verb+resource: 'scan...markets and return...where data disagrees most'.

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 it answers 'what should I bet on today' and saves users from manually paging through markets. Does not explicitly list exclusions or alternatives, but context implies other tools for deeper analysis on specific markets. Clear guidance on when 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 readOnly, idempotent, non-destructive. Description adds details on fetching data from both venues, pre-mapped topics, override behavior, and return format, 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?

Description is longer than minimal but well-structured with clear sections: purpose, arb signal explanation, modes, return format. Every sentence adds value, though could be slightly trimmed.

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

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

No output schema, so description adequately covers return values (leg-by-leg prices, spreads in percentage points). Explains modes and parameter interactions. Minor gap: no mention of error handling or response format details, but overall sufficient for 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 has 100% description coverage. Description adds semantic value by explaining the two modes, how topic maps to events, and that explicit parameters override the topic-mapped side, which is not fully captured in 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?

Clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same question. Distinguishes from sibling 'polymarket_arbitrage' by specifying the two venues and the arbitrage signal.

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

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

Explains two modes (topic shortcuts vs. explicit ticker/slug) and implies usage for arbitrage detection. Does not explicitly contrast with alternatives or state when not to use, but the context is sufficient.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool retrieves stored memories and works across sessions, which is useful behavioral context. However, it doesn't mention potential limitations like memory size, retrieval speed, or error conditions (e.g., if a key doesn't exist).

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 efficiently structured in two sentences. The first sentence explains the core functionality with parameter behavior, and the second provides usage context. Every word serves a purpose with no redundancy.

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

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

For a tool with no annotations and no output schema, the description provides adequate basic information about what the tool does and when to use it. However, it lacks details about return format (e.g., structure of retrieved memories), error handling, or session persistence specifics that would be helpful given the absence of structured metadata.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful context by explaining that omitting the key parameter results in listing all stored memories, which clarifies the optional parameter's semantic effect beyond the schema's technical 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's purpose: retrieving previously stored memories by key or listing all memories. It specifies the verb 'retrieve' and resource 'memory', but doesn't explicitly differentiate from sibling tools like 'remember' or 'forget' beyond mentioning context saved earlier.

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 the tool: 'to retrieve context you saved earlier in the session or in previous sessions.' It also explains the key parameter behavior: 'omit key to list all keys.' However, it doesn't explicitly contrast with alternatives like 'discover_tools' or 'get_series'.

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

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

With no annotations, the description carries the full burden. It discloses that for company type, it fans out in parallel to three sources and returns structured changes, count, and URIs. It is consistent and adds context, but could mention potential delays or failure modes.

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 efficiently covers purpose, type-specific behavior, parameter details, return shape, and use cases. Every sentence adds value with no redundancy.

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

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

Given the tool's complexity (fan-out to multiple sources) and the simple schema (3 params, well-documented), the description adequately explains the return structure. It is missing edge case behavior (e.g., empty results), but overall is clear and actionable.

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

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

Schema coverage is 100% with descriptions, but the description adds significant value: explains the format of 'since' (ISO date or relative), gives typical values ('30d', '1m'), and clarifies 'value' accepts ticker or CIK. This goes beyond the schema.

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

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

Description clearly states what the tool does: retrieves recent changes for an entity since a point in time. It specifies the supported type (company) and the data sources (SEC EDGAR, GDELT, USPTO), distinguishing it from sibling tools like entity_profile which provide static 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?

Explicitly provides use cases: 'brief me on what happened with X' or change-monitoring. It also gives practical examples for the 'since' parameter (e.g., '30d'). However, it does not explicitly mention when not to use this tool or contrast with alternatives like compare_entities or resolve_entity.

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

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

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it explains storage persistence differences (authenticated vs. anonymous sessions with 24-hour limit) and the tool's purpose for cross-call context. However, it doesn't cover potential limitations like size constraints or error conditions.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by usage context and behavioral details. Every sentence adds value without redundancy, making it efficient and 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?

For a tool with no annotations and no output schema, the description does a good job covering purpose, usage, and key behavioral aspects like persistence. However, it lacks details on return values or error handling, which would be helpful given the mutation nature and absence of structured output information.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter-specific information beyond what's in the schema, but it contextualizes their use with examples like 'subject_property' and 'user_preference', aligning with the baseline for high schema coverage.

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

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

The description clearly states the verb ('Store') and resource ('key-value pair in your session memory'), and distinguishes from siblings like 'recall' (retrieve) and 'forget' (delete). It specifies the storage mechanism and purpose, making the tool's function 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 clear context for when to use it ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly mention when not to use it or name alternatives like 'recall' for retrieval. It gives practical examples but lacks explicit exclusions.

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

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

With no annotations provided, the description carries full burden. It discloses that the tool is versioned (v1), returns stable citation URIs, and implies read-only behavior. However, it does not explicitly state side effects, authentication needs, rate limits, or data freshness, which is acceptable for a simple resolver but leaves gaps.

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

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

The description is two sentences, efficiently front-loaded with purpose, then details, then benefit. Every sentence adds value with no redundancy.

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

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

Given the simple 2-parameter schema (100% coverage) and no output schema, the description adequately covers purpose, inputs, and outputs. It could mention prerequisites or data source scope, but it's fairly complete for a v1 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% and both parameters have descriptions. The description adds concrete examples for the 'value' parameter (ticker, CIK, name) and clarifies the accepted formats, enhancing understanding beyond the schema.

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

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

The description clearly states the tool resolves an entity to canonical IDs, explicitly lists accepted inputs for type=company (ticker, CIK, name), and specifies outputs (ticker, CIK, name, URIs). It differentiates from sibling tools (which are data retrieval tools) by its unique entity resolution function and mentions efficiency gain (replaces 2-3 calls).

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

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

The description explains when to use the tool (when you need canonical IDs across Pipeworx data sources) and highlights its single-call efficiency. It implicitly distinguishes from siblings via its unique purpose, but it lacks explicit when-not-to-use guidance or alternative tool references.

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

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

Annotations (readOnlyHint, idempotentHint, etc.) already tell the agent the tool is safe and non-destructive. The description adds behavioral context by revealing it internally calls ai_visibility_check and returns a ranked list with score, confidence, and signal density, which 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 four sentences, front-loaded with the core purpose, then explains mechanism, use case, and output. Every sentence earns its place with no redundancy or fluff.

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

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

Given the moderate complexity (4 parameters, 1 required) and no output schema, the description fully explains the tool's behavior and return value ('ranked list with score, confidence, signal density per entity'). It is complete for an AI agent to select and invoke this 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 100%, so the baseline is 3. The description adds value by explaining that the first entity in the array is treated as the 'subject' and the rest as competitors, which is not in the schema. It also clarifies the role of context and models parameters 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 compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a concrete use case and distinguishes itself from the sibling tool ai_visibility_check, which operates on a single 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 gives a clear usage context: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"' It implicitly suggests using ai_visibility_check for single-entity checks, but does not explicitly state when not to use this tool or mention alternatives.

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

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

No annotations provided, so the description carries full burden. It discloses the return values (verdict, structured form, actual value with citation, percent delta) and data sources. However, it does not cover limitations, error cases, or edge cases (e.g., ambiguous claims, company not found). Adequate but not exhaustive.

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

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

The description is informative but slightly verbose. It starts with the core purpose, then domain, then return values, then efficiency. Could be trimmed by removing the last sentence about replacing agent calls, which is more of a marketing claim. Still well-structured and front-loaded.

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

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

Given no output schema, the description adequately explains what the tool returns (verdict, components). However, it lacks information on error handling, rate limits, or support for non-US companies. Mostly complete for a single-param tool but could be more thorough.

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

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

Only one parameter 'claim' with schema description providing an example. The description adds domain context (type of claims supported) but does not significantly enhance the schema's explanation. Schema coverage is 100%, so baseline 3 is appropriate.

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

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

The description clearly states it fact-checks natural-language claims against authoritative sources, specifies the supported domain (company-financial claims for public US companies), and lists specific metrics (revenue, net income, cash). It is distinct from siblings like ask_pipeworx or get_series, which serve different purposes.

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

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

The description implies when to use (for fact-checking financial claims) and mentions it replaces sequential agent calls, but does not explicitly state when not to use or suggest alternative tools for non-financial claims. The context is clear but lacks explicit exclusions.

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