Bls

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

Annotations already declare readOnly, idempotent, openWorld, non-destructive. The description adds behavioral context: default model is free, Anthropic requires BYO key, and calls go to api.anthropic.com. No contradictions.

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

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

The description is three concise sentences with no wasted words. It front-loads the core function and efficiently covers parameters and use cases.

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 summarizes the return format (per-model score, confidence, signals, raw_response + combined view). It covers all parameters, use cases, and model options, making it complete for an agent to use effectively.

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

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

Schema description coverage is 100%, baseline 3. The description adds value by explaining the default model and the role of _apiKey in enabling Anthropic probes, which is not in 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 probes LLMs for knowledge about entities and scores visibility 0-100 per model, specifying default model and optional Anthropic integration. It distinguishes from siblings by focusing on multi-model visibility 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?

The description provides clear use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). It does not explicitly mention when not to use or compare to alternatives, but the context is adequate for an agent.

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

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description reveals that the tool routes questions to one of 3,765 verified sources and returns structured answers with stable citation URIs. No contradiction with annotations.

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

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

The description is front-loaded with the key directive 'PREFER OVER WEB SEARCH', followed by a clear list of use cases, behavioral details, and examples. Every sentence adds value, and the structure aids quick comprehension.

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

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

Despite lacking an output schema, the description explains the return format (structured answer with citation URIs) and how the tool handles routing. It could discuss error handling or the difference from 'ask_pipeworx_grounded', but overall sufficient for a broad-coverage 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 baseline is 3. The description does not add new parameter-specific meaning beyond acknowledging that the tool fills arguments. The aliases for 'question' are listed in the schema but not elaborated in the description.

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

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

The description clearly identifies the tool as a preferred alternative to web search for factual, structured data queries. It lists specific domains (SEC filings, FDA data, etc.) and provides examples, distinguishing the tool from general web search.

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

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

The description explicitly recommends the tool over web search for a wide range of factual queries and provides example triggers. However, it does not differentiate from the sibling tool 'ask_pipeworx_grounded', and does not clarify when web search might still be preferred.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds critical behavioral details: the return structure (answer, evidence, confidence, etc.), explicit refusal reasons (not_in_source, no_tool_match, etc.), and the cost of an extra LLM call. No contradiction with annotations.

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

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

The description is well-structured and appropriately sized, front-loading the core purpose and usage, then detailing the return format and refusal reasons. Every sentence adds value with no wasted words.

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

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

Despite having no output schema, the description fully compensates by detailing the exact return structure and all possible refusal reasons. It covers the tool's behavior, use cases, and limitations comprehensively for an agent.

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

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

Schema coverage is 100% with all parameters documented. The description mentions that it accepts 'question' and aliases, but adds little beyond the schema's own descriptions (e.g., 'Your question in natural language'). Baseline 3 is appropriate as the description does not significantly enhance parameter understanding.

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

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

The description states it is a 'Hallucination-resistant answer mode for high-stakes reads' and clearly explains it routes like ask_pipeworx but extracts answers only from tool result. It distinguishes itself from the sibling ask_pipeworx by noting the extra LLM call and grounded behavior.

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

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

Explicitly says 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and lists example domains. Also advises 'Prefer ask_pipeworx for casual lookups', providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already indicate read-only, idempotent, not destructive. The description adds extensive behavioral details: fan-out patterns, response shapes, resolver contract, parent event extractor, news fallback, safety mechanisms, and resolution-rule risk. No contradictions.

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

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

The description is lengthy but well-structured with sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.). Every sentence adds value, but it is somewhat verbose. Front-loaded main purpose.

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

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

For a complex tool with multiple data sources and response fields, the description covers all necessary aspects: how to call it, expected response shapes, edge cases (closed markets, wide spreads), safety mechanisms, and resolution-rule risk. No output schema, but response shapes are thoroughly explained.

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

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

Schema coverage is 100% and the description adds significant context: explains the 'market' parameter accepts slugs, URLs, or question text; details 'depth' and 'include_raw' with defaults and trade-offs; provides examples of market input.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data, resolving the market, classifying the bet, fanning out, and returning an evidence packet. It distinguishes from sibling tools by being specifically for bet research, not general data queries.

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

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

Explicit usage guidance is given: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides fan-out examples and safety checks. However, it does not explicitly state when not to use or mention alternative sibling tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds that it 'Returns dated data points with values', which is a minor addition but does not disclose rate limits, error behavior, or other traits 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 three sentences: first states purpose, second describes return, third gives an example. Every sentence is essential and front-loaded, 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 existence of an output schema and full parameter documentation in the schema, the description adequately covers the tool's purpose, return type, and usage example. It is complete for an agent to understand basic usage, with no major gaps.

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

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

Schema description coverage is 100%, so the schema fully documents all 4 parameters. The description only mentions the series ID example, adding no new semantic meaning beyond what the schema provides. Baseline score of 3 is appropriate.

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

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

The description uses a specific verb 'Fetch' and resource 'historical time series data', listing categories (employment, inflation, etc.). It clearly distinguishes from sibling tools like bls_latest and bls_search by emphasizing historical 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?

The description implies usage for historical data and provides an example series ID, but it does not explicitly state when to use this tool versus alternatives (e.g., bls_latest for current data, bls_search for finding series). No direct comparison or exclusion guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating safe, non-destructive behavior. The description adds that the tool returns the latest value and date, aligning with these annotations. No contradictions exist, and the description complements the annotations without redundancy.

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, consisting of two short sentences that front-load the key information. Every sentence serves a purpose: first specifies what it does, second adds return value and usage context. No wasted words.

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

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

Given the presence of annotations and an assumed output schema, the description provides sufficient context for an AI agent to correctly select and use the tool. It explains the function, return values, and appropriate usage, making it complete within the broader context of the tool's 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?

Schema coverage is 100%, so the input schema already describes both parameters (series_id and _apiKey) adequately. The description does not add extra semantic detail beyond the schema, meeting the baseline for high coverage. No additional clarification is provided.

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 gets the most recent data point for a BLS series, specifying it returns the latest value and date. This verb+resource combination distinguishes it from siblings like bls_get_series (which likely retrieves historical data) and bls_search (which searches for series). The purpose is specific and 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 advises using the tool 'when you need current figures without historical context,' providing clear guidance on appropriate use cases. While it does not explicitly exclude alternatives, the context is sufficient to differentiate from batch or historical retrieval tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true, and destructiveHint false. The description adds no further behavioral context (e.g., edge cases, restrictions) beyond stating it returns series IDs and descriptions, which is minimal.

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 sentences, no fluff, and the main action ('Browse popular BLS series') is front-loaded. Every sentence adds value, making it highly concise and well-structured.

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

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

Given the tool's low complexity (one optional parameter, no required fields), output schema exists, and annotations are thorough, the description adequately covers the core functionality. It could potentially mention that it returns a list or pagination, but with output schema present, it is sufficient.

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

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

The input schema covers the parameter 'category' with a description listing possible values. The tool description lists similar categories, but with slight discrepancies (e.g., 'inflation' in description vs 'prices' in schema), which may cause minor confusion. Overall, the schema does the heavy lifting, so the description adds limited new meaning.

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

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

The description clearly states the verb 'Browse' and the resource 'popular BLS series by category', and specifies what it returns (series IDs and descriptions). However, it does not explicitly differentiate from sibling tools like bls_search or bls_get_series, which could confuse agents on which to choose.

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 phrase 'Start here to explore available data' provides implied usage context as a first step for exploration. However, there is no explicit guidance on when not to use this tool or when to prefer alternatives such as bls_search or bls_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?

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating safe read operations. The description confirms it returns series IDs and titles, adding some context but not substantial behavioral details beyond annotations.

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

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

Two sentences, front-loaded with purpose and usage guidance. No redundant words; every sentence adds value.

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

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

For a simple search tool with a single parameter and output schema available, the description adequately covers purpose, usage, and return type. No gaps identified.

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

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

The input schema covers 100% of parameters and includes an example. The tool description adds no additional meaning beyond what the schema provides for the 'keyword' parameter.

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

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

The description uses a specific verb 'Search' and identifies the resource as 'BLS economic data series', clearly stating it returns series IDs and titles. It distinguishes from sibling tools like bls_get_series.

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 instructs when to use this tool (searching by keyword) and when to use an alternative: 'Use bls_get_series with an ID to fetch historical data points.'

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds valuable context: data sources (SEC EDGAR/XBRL, FAERS), off-calendar fiscal year handling, sorted results, citation URIs. 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?

Packed with useful information but slightly lengthy. Uses examples and bold keywords effectively. Each sentence adds value, but could be tightened slightly.

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 inputs, behavior, data sources, sorting, return format (paired data + URIs). No gaps given the missing output schema. Completely equips an agent to select and invoke the 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?

100% schema coverage but description enriches meaning: explains what each type (company/drug) fetches, gives value formatting examples (tickers vs names), and clarifies the parallel invocation pattern.

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 compares 2-5 companies or drugs side-by-side, with specific data pulls for each type. Examples and explicit differentiation from single-entity lookups make purpose unmistakable.

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

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

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Provides concrete query examples and constraints (max 5, one call). No ambiguity about 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds significant behavioral context: parallel execution across 3,767 tools, extraction of verbatim evidence with confidence/source/citation, explicit handling of gaps (never invented), and returns a structured findings packet. 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 information-dense but well-structured: starts with value proposition, then mechanism, then usage guidance, then requirements/caveats. Could be slightly more concise, but every sentence adds value.

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

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

Despite no output schema, description compensates by fully describing the return value (structured findings packet with evidence, confidence, source, gaps). Covers all relevant aspects: purpose, behavior, usage context, limitations (time, paid plan). Complete 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?

Schema coverage is 100%, so baseline is 3. Description adds meaning beyond schema: explains that depth controls number of facets (quick=3, standard=5, thorough=8) and that question is automatically decomposed. Also describes output structure (findings packet with evidence, confidence, source, etc.).

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

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

Description clearly states it's a grounded multi-source research tool that decomposes questions and runs parallel sub-questions across many tools. It distinguishes from siblings like ask_pipeworx by noting that deep_research is for broad/multi-part questions whereas ask_pipeworx is for single lookups.

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

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

Provides explicit when-to-use ('broad or multi-part questions'), when-not-to-use ('use ask_pipeworx for single lookups'), and prerequisites (Pipeworx account, paid plan for thorough depth). Also mentions expected response time (15-60s).

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: it returns top-N relevant tools with full schemas and curated examples, ready to call without a second lookup. This goes beyond annotations and helps the agent understand the return format and efficiency benefit.

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

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

The description is moderately long but each sentence contributes value: the main verb and purpose, use cases, return format details, and a usage directive. It is front-loaded with the core action. Could be slightly tighter but remains 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?

Given no output schema, the description adequately explains the return format ('top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly'). It also covers the limit parameter's default and max. No gaps remain for an agent to misunderstand what the tool produces.

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 schema already describes all parameters, including aliases for 'query'. The description does not add significant additional meaning beyond what the schema provides. Therefore, the baseline of 3 is appropriate.

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

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

The description clearly states the tool's purpose with a specific verb ('Find tools') and resource ('tools'), and provides extensive examples of data/task areas (SEC filings, financials, etc.). It distinguishes from sibling tools by emphasizing browsing and discovery of the tool set, rather than executing specific queries like search_within 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?

Explicitly states when to use: 'Use when you need to browse, search, look up, or discover what tools exist for...' and advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This clearly indicates the appropriate context and implies alternatives when the agent already knows the needed tool.

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

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

Beyond annotations (readOnly, openWorld, idempotent), the description details the data sources fanned out, return fields, soft-failure for patents, and the format of filings URIs, giving rich 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 somewhat lengthy but well-structured with example queries first, then details. Every sentence adds value; could be slightly more concise but remains 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?

Given no output schema, the description covers return fields and limitations adequately. It could more explicitly describe the response structure (e.g., keys present), but overall provides sufficient context for an agent to use the 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%, so baseline is 3. The description adds value by explaining that type is only 'company', value is ticker or CIK, and that names are not supported, making the parameters' usage clearer than the schema alone.

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

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

The description clearly states the tool provides a full cross-source profile of a US public company in one call, distinguishing it from sibling tools that chain multiple single-pack lookups.

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

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

Explicitly instructs to prefer this tool over chaining individual lookups for holistic views, and specifies that if only a name is available, use resolve_entity first. Provides clear when-to-use and when-not-to-use guidance.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. Description adds contextual usage scenarios but no additional behavioral traits beyond what annotations state. Adequate but not improved.

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: two sentences with no waste. Front-loaded with purpose. Every sentence adds value (usage, examples, pairing).

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?

Simple tool with no output schema. Description covers purpose, usage context, and sibling relations. Could mention return value or behavior on missing key, but not necessary given idempotency. Complete enough for an agent.

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

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

Schema coverage is 100% with one parameter 'key' described as 'Memory key to delete.' Description adds no new meaning beyond the schema, meeting baseline for high coverage.

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

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

Description uses specific verb 'Delete' and resource 'previously stored memory by key.' It explicitly mentions siblings 'remember' and 'recall,' providing clear differentiation. No ambiguity.

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

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

Clearly states when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' Also recommends pairing with remember and recall. Does not provide explicit 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?

Annotations already indicate the tool is read-only, idempotent, open-world, and non-destructive. The description adds behavioral details: fetches the page, extracts title/description/key links, and outputs a standard llms.txt markdown blob. This contextualizes the annotations without contradiction.

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

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

The description is two concise sentences with a bulleted use-case list. Information is front-loaded with the core purpose, and 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?

The tool's complexity is moderate, and the description, combined with annotations and schema, fully captures what the tool does, how it behaves, and what output to expect (a single text blob). No output schema is needed as the description clarifies the return format.

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

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

Schema description coverage is 100%, and the description adds no new parameter information beyond what is already in the schema. The description does mention 'title/description/key links' which provides some context, but the baseline is 3 due to full 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 tool generates an llms.txt file for any URL, specifying the resource (llms.txt) and the action (generate). It distinguishes from siblings like scan_competitor_ai_presence or ai_visibility_check by focusing on file generation rather than scanning or checking.

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

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

The description lists explicit use cases: getting a client's site indexed, drafting for own project, or auditing a competitor's crawler view. It provides context on when to use but does not explicitly state when not to use or offer alternatives.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by specifying the exact return fields and the effect of the include_inactive parameter, without contradicting annotations.

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

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

Two sentences with no wasted words. The purpose is front-loaded, and every sentence earns its place by providing 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 simple read-only tool with one optional parameter and no output schema, the description fully covers purpose, return fields, and usage 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?

Schema has 100% coverage, so baseline is 3. The description does not add extra meaning beyond the schema's description of the 'include_inactive' boolean parameter.

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

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

The description clearly states 'List the caller's active subscriptions' and lists specific return fields. It distinguishes itself from sibling tools like subscribe and unsubscribe through the context of reviewing current subscriptions.

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 when to use: 'review what you're monitoring before adding more or to find an id to cancel.' This provides clear contextual guidance for the agent.

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 limits (5 per identifier per day), that it's free, and doesn't count against tool-call quota. Annotations only show non-destructive, non-readOnly, etc., so description adds significant behavioral context beyond schema.

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: 5 sentences covering all key aspects. Front-loaded with purpose, then specific use cases, then additional instructions. No wasted words.

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

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

Given the tool's simple purpose (feedback submission) and well-documented schema, the description fully covers usage context, parameter guidance, behavioral traits, and best practices. Complete for an agent to use correctly.

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

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

Schema coverage is 100% with descriptions for each parameter. Description adds marginal value: guidance on message content (avoid end-user prompt) and context usage, but no significant new semantics for type or context beyond the schema's enum/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 it is for sending feedback about broken, missing, or needed features. It uses specific verbs ('tell', 'is broken, missing, or needs to exist') and distinguishes from siblings by being the only feedback tool among many query/analysis 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 specifies when to use: bugs, feature gaps, data gaps, or praise. Also mentions what not to do ('don't paste the end-user's prompt') and provides usage tips ('Describe the issue in terms of Pipeworx tools/packs'). Includes rate limits and quota info.

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

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds caching behavior (5min-1h) and PII disclosure, which is useful context beyond annotations.

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

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

Three sentences, front-loaded with core function, no redundant words. Each sentence adds value.

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

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

For a 1-param tool with no output schema, description explains return values (top tools, packs, call volume) and caching, providing enough context for an agent to understand what to expect.

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

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

Schema covers 100% of parameter details with enum and description. Description reinforces window semantics with usage guidance, adding minimal but helpful value.

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

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

Description clearly states what the tool does: 'What other AI agents are calling on Pipeworx right now' and lists return types (top tools, packs, call volume). Distinguishes from siblings like ask_pipeworx or discover_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 lists three use cases and explains when to use different windows ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). No need for alternative tools mention.

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 already indicating read-only, idempotent, and non-destructive behavior, the description adds rich behavioral details: internal algorithms (monotonicity, partition checks), fill check against CLOB depth, semantic anchor for cross-event mode, partition filter, and warnings about unrealizable edges. This goes well beyond what annotations provide.

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

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

The description is long but well-structured with clear sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). It front-loads the key requirement. However, some redundancy and excessive detail could be trimmed without losing clarity. Still, it is organized and readable.

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 complex tool with no output schema, the description thoroughly explains the response structure (opportunities array with fields, partition_check object) and covers edge cases (fill check, placeholder filters, similarity threshold). It leaves little ambiguity for an AI agent to understand invocation and interpretation.

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 parameters are already documented. The description enhances meaning by elaborating on the purpose of each mode ('event recommended for a specific market', 'topic for cross-event scanning'), providing examples, and explaining the algorithms behind them. This adds significant context 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 identifies the tool's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) and provides specific use cases. While it does not explicitly compare to sibling tools, the level of detail makes the purpose unmistakable.

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 that one of 'event' or 'topic' is required, and that calling with no arguments fails. It recommends when to use each mode and even directs users to an alternative tool ('For custom sizing use polymarket_fill_risk'). This provides clear decision 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 read-only, open-world, idempotent, non-destructive. The description adds rich details: caching at KV level (1h), diagnostics reporting, edge computation methodology, and model family groupings, exceeding annotation information.

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

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

Front-loaded with core purpose, then structured by model segments. A bit verbose but every sentence provides value; could be slightly condensed 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?

Despite no output schema, the description fully explains the response structure (by_segment, diagnostics, fed_note) and covers all behaviors for a complex tool with 9 parameters.

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

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

Schema coverage is 100%, and the description adds deep semantic context for each parameter, including rationale (e.g., slippage explanation, min_liquidity filter purpose), which helps agents set appropriate 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 it 'scan[s] top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It uses specific verbs and resources, and distinguishes from siblings like polymarket_arbitrage and polymarket_kalshi_spread.

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 targets 'what should I bet on today' use case, explains tradeable-edge knobs, and mentions diagnostics for empty segments. Could be slightly improved by explicitly stating when not to use, but provides clear context.

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

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

Annotations already mark it as read-only and idempotent. The description adds detailed behavioral context: response includes tracked and expired arrays, time-series, trend, decay, and snapshot dates. It also notes that history is bounded and decay is from daily closes, not intraday.

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

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

The description is well-structured with clear sections (CAPS for response fields) and front-loaded with the key question. It is somewhat lengthy but every sentence adds value. Could be slightly more concise, but overall effective.

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

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

Given there is no output schema, the description thoroughly explains the response structure and edge cases (expired opportunities, gaps in snapshots). It covers all relevant information for a tool with two simple parameters.

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

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

Schema coverage is 100% with descriptions. The description adds context: explains what 'window' refers to (snapshot family), provides default values, and clarifies the parameter semantics beyond the schema. No further detail needed.

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

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

The description clearly states it provides edge persistence and decay telemetry from daily snapshots, answering a specific question about edge duration. It uses specific verbs like 'answers' and explains the distinction between fresh and old edges, which distinguishes it from the sibling tool 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 explains the use case (assessing edge age and decay) and provides context on when to use it. It mentions limitations like 60-day TTL and snapshot start. However, it does not explicitly state when to use alternative tools like polymarket_edges for current data.

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

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds substantial behavioral context: it walks the order book ladder, returns specific fields (top_of_book, vwap_fill_price, slippage_pp, etc.), and for basket mode, it warns about thin legs and forced directional risk. The warning about partial basket fills leading to unhedged positions adds critical transparency.

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

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

The description is long but well-structured. It starts with the core purpose, then explains requirements, mode-specific behavior, and ends with a strong usage suggestion. Every sentence adds information, though a slightly more compact presentation could improve conciseness. However, given the complexity of the tool, this is a well-crafted 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?

Despite no output schema, the description thoroughly explains return values for both modes, including verdict, thin_legs, forced_directional_risk, etc. It covers edge cases like partial fills and unhedged positions. Minor omissions include not defining 'clean|degraded|cannot_fill' exactly, but overall completeness is high for such a complex tool.

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

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

Schema description coverage is 100%, but the description adds significant value beyond the schema. It explains the mode selection implicit in using 'market' vs 'event', the auto-detection of side in basket mode, and the interpretation of size_usd as settlement notional for baskets. It also clarifies that market and event accept slugs or full URLs, and gives the default and clamp range for size_usd.

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

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

The description clearly states the tool's purpose as a 'realizable-vs-theoretical edge check against live CLOB order-book depth.' It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by explicitly stating when to use it: before acting on signals from these tools, especially for trades above $500. The description also explains the two modes (single-market and basket), 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 explicit usage guidelines: it requires either 'market' or 'event' parameter, and explains when to use each mode. It gives a clear directive to use this tool before acting on polymarket_arbitrage or polymarket_edges signals, and specifies the size threshold ($500). It also notes the default settings and the interpretation of size_usd for each mode.

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

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds significant transparency beyond annotations by detailing internal logic: compatibility_warning triggers for non-equivalent bet shapes or semantically unrelated events, temporal_alignment flags mismatched resolution periods, and skipped_cross counters expose dropped comparisons. This level of disclosure is exceptional.

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

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

The description is well-structured with clear sections (TWO MODES, RESPONSE, SAFETY FIELDS) and uses bold for key terms. It is dense with information but every sentence earns its place. Could be slightly more concise, but the complexity of the tool warrants the length.

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

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

Despite lacking an output schema, the description comprehensively documents the response structure: leg-by-leg prices, matched spreads with top_spreads_pp, and safety fields like compatibility_warning, temporal_alignment, and skipped counters. It covers edge cases (non-equivalent bet shapes, temporal gaps) and provides actionable guidance. This is complete for a tool with no output schema.

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

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

Schema coverage is 100% and each parameter is described in the schema. The description adds value by explaining the relationship between parameters: the topic parameter auto-fills both sides, while explicit tickers override the topic-mapped side. It also provides examples in the schema. This goes beyond basic schema descriptions, earning a 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 clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It distinguishes itself by specifying two modes (topic shortcuts vs explicit tickers) and provides a clear verb+resource (compute spread). While sibling tools like polymarket_arbitrage exist, the description implicitly differentiates by focusing on cross-venue spreads, which is a unique function.

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 each mode (topic for pre-mapped shortcuts, explicit for custom pairings) and provides critical context about limitations: pre-mapped topics often return compatibility warnings and are not necessarily tradeable. It also explains the meaning of compatibility_warning fields, helping users interpret results. However, it does not explicitly compare to sibling tools or state when not to use the tool at all.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds scoping to an identifier and pairing with remember/forget. No contradictions. Adds useful behavioral context beyond annotations.

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

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

Three well-structured sentences: first defines core function, second provides use case and examples, third adds scoping and pairing. No filler, each sentence earns its place.

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

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

Given one optional parameter, no output schema, and rich annotations, the description covers key usage, scoping, and pairing. It could mention what happens if a key is missing or return format, but overall near complete for the tool's complexity.

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

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

Schema coverage is 100% with one optional string parameter 'key'. The description explains that omitting the key lists all saved keys, adding significant meaning beyond the schema's generic description.

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

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

The description clearly states the tool retrieves a saved value or lists keys, with specific verb 'retrieve' and resource 'value saved via remember'. It distinguishes from sibling tools remember and forget by explicitly pairing with 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?

Provides clear context: use to look up stored context (target ticker, address, notes) without re-deriving. Mentions scoping. Does not explicitly state when not to use, but the context is strong enough to infer.

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

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

Annotations indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds further detail: it returns events with specific fields (source, citation_uri, payload), explains the mark_read flag's effect on subsequent calls, and describes filtering. 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 four sentences, front-loaded with the primary action, then lists return fields, parameter usage, and an alternative. Every sentence adds information without redundancy, making it efficient and easy to parse.

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 5 optional parameters and no output schema, the description adequately covers what the tool returns (events with specific fields), how to filter and paginate (via limit), the mark_read behavior, and alternative access. This is sufficient for an agent to use the tool correctly.

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

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

Schema coverage is 100% with descriptions for all 5 parameters. The description enhances understanding by giving a concrete example for type ('e.g. "sec_8k"') and explaining the purpose of mark_read. This adds value 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 starts with a specific verb 'Pull' and resource 'fired events from your subscription feed', clearly stating what the tool does. It distinguishes from sibling tools like 'list_subscriptions' and 'subscribe' by focusing on retrieving events rather than managing subscriptions.

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 usage context: filtering by type and since, marking events read, and that polling works fine. It also mentions an alternative access method via a direct URL. However, it does not explicitly state when not to use it or contrast with siblings beyond the alternative endpoint.

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 fan-out behavior to multiple sources, fallback from GDELT to GNews, soft-fail for USPTO, and return structure including citations. All consistent with annotations (readOnly, openWorld, idempotent, non-destructive).

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

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

Well-structured with examples first, then details, but slightly verbose. Every sentence adds value; minor tightening possible.

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

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

Fully covers complexity: multiple sources, fallback, parameter formats, return structure, and alternatives. No output schema but describes return fields sufficiently.

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?

Adds meaning beyond schema: explains `since` accepts ISO or relative with typical values, `value` can be ticker or CIK, and `type` is limited to 'company'. Schema already covers basics.

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 provides a change feed for a company over a time window, with example queries. It distinguishes itself from sibling entity_profile by directing users to the latter for static profiles.

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

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

Explicitly advises to use entity_profile for static profiles, giving clear when-to-use guidance. Also implies use of this tool for recent changes within a window.

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

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

Adds useful context beyond annotations: memory is scoped by agent identifier, persistence details (24h for anonymous, persistent for authenticated). Annotations already indicate idempotentHint=true and non-destructive, description aligns and expands.

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 that each add unique value: core purpose, use case, scoping/persistence, paired tools. Slightly verbose but no redundancy. Could be tightened slightly without losing clarity.

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

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

For a simple tool with 2 required params and no output schema, the description covers all relevant aspects: purpose, when to use, scope, persistence, and companion tools. 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 descriptions for both parameters. Description reinforces naming conventions (e.g., 'subject_property') and clarifies that value accepts any text, adding practical guidance 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?

Clear verb+resource combination ('save data') with specific examples of what to store. Explicitly distinguishes from sibling tools recall and forget, which are mentioned as complementary 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?

Explicitly states when to use (discovering something worth carrying forward) and provides context on persistence differences (authenticated vs anonymous). Names companion tools recall and forget for retrieval and deletion.

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

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

Beyond annotations (readOnlyHint, idempotentHint), description reveals internal cascading through multiple endpoints and provides 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?

Description is efficient, starting with common queries, then use case, then details for each type. No superfluous 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?

Despite no output schema, description fully explains return values for both entity types, including citation URIs. Complete for the tool's purpose.

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 has good descriptions (100% coverage), but description adds rich details about returns for each type, including specific formats and auto-disambiguation for company.

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 resolves user-spoken names to canonical identifiers with specific examples. Distinguishes from siblings by saying 'Use FIRST whenever you have a name but need an ID.'

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 you have a name but need an ID) and implies it replaces manual lookups. Could be more explicit about when not to use, but clear context is provided.

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

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

Annotations already declare readOnly, idempotent, and open world. Description adds that it calls ai_visibility_check, ranks results, and surfaces scores/confidence/signal density. No contradictions.

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

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

Three sentences, front-loaded with core purpose, then mechanism and use case. Every sentence adds value with no fluff.

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

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

Given no output schema, the description adequately describes the return format (ranked list with score, confidence, signal density) and input constraints (2-8 entities). Missing potential error cases but sufficient for typical use.

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

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

Schema coverage is 100%, and the description adds value beyond schema by explaining that the first entity is treated as the 'subject' for narrative and that models like 'anthropic' require _apiKey. This enriches parameter understanding.

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

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

Clearly states the verb (compare, probes, ranks, surfaces) and resource (AI visibility across entities). Distinguishes from sibling tools like ai_visibility_check and compare_entities by explaining it uses the former and compares multiple entities in one call.

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 a clear use case ('competitive AI-marketing audits') and example question. Does not explicitly state when not to use or contrast with alternative tools, but the context is sufficient for an agent to decide.

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

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

Annotations already indicate safe, read-only behavior. The description adds critical behavioral details: partial failures degrade gracefully, bundlephobia first measurement can take 5-30s, and sources_failed will list timeouts. 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?

Description is front-loaded with key purpose and use case. It is slightly verbose but every sentence adds value; only minor trimming possible.

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 (multiple sources, partial failures, ecosystem limitation) and absence of an output schema, the description thoroughly explains return values, performance considerations, and limitations, making it complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining scoped packages are accepted for 'package' and that 'version' defaults to latest when omitted, going beyond schema descriptions.

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

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

The description clearly states the tool's purpose as a composite check for npm packages with a specific verb and resource. It distinguishes itself from sibling tools by specifying its unique functionality (combining deps.dev and bundlephobia data) and ecosystem 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?

Provides explicit guidance on when to use ('is X safe / popular / small' questions) and when not to (other ecosystems point to deps.dev:version directly). This clearly differentiates from alternatives.

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

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

Discloses technical details beyond annotations: uses BGE-base-en embeddings + cosine over 500-char overlapping windows; 200K char limit with truncation flag; returns passages with character offsets and similarity scores. No contradiction with readOnlyHint/idempotentHint.

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

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

Single paragraph of ~100 words, front-loaded with core action, then usage guidelines, then technical details. No redundant sentences. Every sentence adds value.

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

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

Despite no output schema, description fully explains return format (passages with offsets and similarity scores). Covers input format, limits, and internal algorithm, making it complete 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?

Schema has 100% parameter descriptions, so baseline is 3. Description adds extra context: examples for query, default/range for limit, and explains that 'text' should be a fetched record. This justifies moving above baseline.

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

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

The description clearly states the tool performs semantic search inside a fetched record, specifies the resource (text from a source like SEC 10-K), and contrasts with the sibling ask_pipeworx_grounded.

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

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

Explicitly states when to use: 'when the record is too big to cram into the prompt' and provides a paired alternative (ask_pipeworx_grounded) for grounding over passages instead of the whole document. Also notes the 200K char cap.

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 rich behavioral context such as delivery channels, webhook signing, phone verification, and rate limits. However, annotations indicate idempotentHint=true, but the description describes creation which typically is not idempotent, creating a contradiction. The description does not address idempotency or duplicate subscription 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 well-structured with clear sections for subscription types and delivery channels. It is front-loaded with the main purpose. However, it is relatively lengthy and could be slightly more concise by reducing redundancy in delivery explanations.

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

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

Given the tool's complexity (multiple types, nested parameters, delivery channels) and absence of an output schema, the description covers requirements, limitations, and behavioral details well. Missing only explicit output format beyond subscription id and inconsistency with idempotency annotation slightly reduces completeness.

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

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

Schema coverage is 100%, but the description adds significant value by detailing each subscription type's parameter structure with concrete examples, required fields, and constraints like 'sponsor or condition required' for clinical_trial. It also elaborates on delivery channel parameters 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 creates a proactive monitoring subscription to a live-data event stream and returns the new subscription id. It specifies multiple subscription types and distinguishes from sibling tools like list_subscriptions and unsubscribe.

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

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

The description explicitly requires a Pipeworx OAuth account and rules out anonymous/BYO usage. It provides examples per type and explains delivery channels. However, it lacks explicit 'when not to use' guidance or comparison with alternatives like one-off queries.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds valuable behavioral context beyond annotations, such as that the tool returns exact tool calls with arguments and is drawn from a live catalog. 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 verbose, listing all categories and common user queries. While front-loaded with typical questions, it repeats information already present in the schema's parameter description. Could be more concise while retaining essential guidance.

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

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

Given the tool's simplicity (one optional parameter) and the rich annotations, the description covers everything needed: purpose, usage, return format (category-bucketed example questions with exact tool shapes), and when to use. No missing information.

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

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

Schema coverage is 100% with a clear description of the 'topic' parameter including a list of allowed values. The description adds usage guidance ('Call with no arguments for the full spread') and explains the effect of the parameter, which goes beyond the schema's basic description.

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

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

The description clearly identifies the tool as the onboarding entry point that returns category-bucketed example questions. It uses specific verbs ('returns', 'the onboarding entry point') and distinguishes it from siblings like ask_pipeworx by stating it teaches how to call meta-tools.

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

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

The description explicitly states when to use ('Use this FIRST when you do not yet know what Pipeworx can do for you') and how to call it (no args vs topic). It implicitly guides away from alternatives by mentioning meta-tools, but lacks explicit exclusions like 'if you already know your question, use ask_pipeworx directly'.

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 it is not read-only, idempotent, and not destructive. The description adds ownership enforcement and deactivation behavior, enhancing understanding 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?

Two sentences, front-loaded with the action and key details. No unnecessary words.

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

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

Given the simple tool with one parameter and full annotations, the description covers ownership, data lifecycle, and ties to recent_alerts. 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% and describes 'id' as a UUID from subscribe. The description adds 'by id' and links to the return value of subscribe, providing additional context.

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

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

The description clearly states 'Cancel a subscription by id', specifying the verb and resource. It differentiates from siblings like subscribe (creation) and list_subscriptions (listing).

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 (to cancel a subscription) and provides context on ownership and data lifecycle. It implicitly advises against using it for permanent deletion, but does not explicitly state alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about the data source (SEC EDGAR + XBRL), the return structure (verdict, structured form, actual value, citation, percent delta), and the efficiency gain. 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 with examples, domain specification, and return details. It is verbose but each part serves a purpose. Minor trimming possible but overall effective.

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

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

Since there is no output schema, the description fully explains the return format (verdict types, structured form, citation, delta). It also notes limitations (v1 supports only company-financial claims) and the efficiency improvement. Complete for a single-parameter 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% for the single 'claim' parameter. The description enriches meaning with examples and explains how the parameter is processed (natural-language claim verification). This exceeds the baseline of 3 by adding valuable context beyond the schema.

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

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

The description clearly states the tool's purpose: verifying natural-language claims against authoritative sources. It provides example queries ('Is it true that...', 'fact check') and specifies the domain (company-financial claims via SEC EDGAR + XBRL). It distinguishes from siblings by noting it replaces 4-6 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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It specifies the domain (company-financial claims) and contrasts with what it replaces. It lacks explicit when-not-to-use instructions but the narrow domain provides implicit boundaries.

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