Biostudies

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

Annotations already declare readOnlyHint and idempotentHint. The description adds transparency about cost implications (BYO key for Anthropic), default model selection, and the structure of per-model returns (score, confidence, signals, raw_response). 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 highly concise: three sentences covering core action, model options, return structure, and use cases. No redundant words; front-loaded with the key verb and resource.

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 absence of an output schema, the description adequately explains the return format (per-model fields and combined view). All parameters are covered in schema and description, and the context signals (high coverage, no enums) indicate minimal additional explanation needed.

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

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

Schema coverage is 100%, so the description adds limited new info. It mentions that the default model is Workers AI and that _apiKey is needed for Anthropic, which aligns with but slightly expands on the schema descriptions.

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

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

The description clearly states the tool's purpose: to probe LLMs about an entity and score visibility from 0-100. The verb 'probe' and resource 'LLMs' are specific, and the description distinguishes it from siblings like 'scan_competitor_ai_presence' 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) and explains when to use the free default model vs. requiring a paid API key for Anthropic. However, it does not explicitly contrast with 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 indicate readOnly, openWorld, idempotent, non-destructive. The description adds important behavioral context: it routes questions to one of 3,599 tools across 839 sources, fills arguments automatically, and returns structured answers with stable citation URIs. This goes well beyond the annotations.

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

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

The description is well-structured and front-loaded with the key instruction to prefer over web search. It then lists relevant domains, usage guidance, and examples. Every sentence adds value; no redundancy. The length is justified by the breadth of information needed for correct tool selection.

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

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

Given the tool's complexity (routing to many sources) and that there is no output schema, the description is remarkably complete. It covers purpose, when to use, what it does internally, and provides examples. An agent can confidently use this tool based on the description alone.

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 described as aliases for 'question'. The description adds meaning by explaining that the parameter is a natural language question that will be routed to appropriate sources, and provides examples. This adds context beyond the schema's basic type 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 that the tool is for asking factual questions about current or historical data from authoritative structured sources. It specifies the verb 'ask' and the resource 'Pipeworx', and distinguishes itself from web search by recommending preference over it.

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 to prefer this tool over web search for factual queries, lists numerous example domains (SEC filings, FDA data, etc.), and provides usage triggers like 'what is', 'look up', 'find'. It also gives concrete examples, making it easy for an agent to decide when to invoke this tool over alternatives.

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

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

Annotations declare readOnlyHint, etc., but the description adds valuable behavioral details: it costs an extra LLM call, returns explicit refusal reasons, and extracts answers only from tool results. 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 concise yet comprehensive, with front-loaded purpose, clear step-by-step behavior, and explicit usage guidance. Every sentence adds value without redundancy.

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

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

Despite lacking an output schema, the description fully specifies the return format (answer, evidence, confidence, etc.) and possible refusal reasons, covering all necessary contextual information for a complex, high-stakes tool.

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

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

Schema coverage is 100% with adequate descriptions for all 6 parameters. The description does not add further parameter-level detail beyond the schema, but given high coverage, a 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 function as a 'hallucination-resistant answer mode for high-stakes reads,' outlines its routing and extraction process, and distinguishes it from the sibling 'ask_pipeworx' by noting the extra cost and use case (e.g., when answers will be quoted or acted on). This provides a specific verb+resource scope.

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

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

The description explicitly advises when to use this tool ('high-stakes reads' where facts must not be invented) and when not to ('prefer ask_pipeworx for casual lookups'), providing clear context and an alternative.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds extensive behavioral context: confidence resolution, parent event extraction, news fallback, safety for low-confidence matches, closed markets, wide spreads. It describes error handling and blocking conditions comprehensively, going well 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 long but well-structured with logical sections (classifiers, examples, response shapes, resolver contract, etc.). Every sentence adds value. It could benefit from bullet points for readability, but the density is justified given the tool's complexity.

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

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

The description covers all aspects: input formats, classifiers, fan-out examples, response structure, resolver contract with confidence levels, parent event extraction, news fallback, safety mechanisms, closed markets, and illiquid spreads. No output schema exists, but response shapes are detailed. Extremely complete for a complex tool.

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

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

Schema coverage is 100% with detailed descriptions. The description adds significant context: three formats for market input, examples of quick vs thorough, and explanation of include_raw behavior (summarized vs full payload, size ranges). This enriches the schema significantly.

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 researches a Polymarket bet by pulling Pipeworx data, specifies inputs (slug, URL, question text), and outlines the process (resolve, classify, fan-out, return evidence packet + comparison). It distinguishes from sibling tools like polymarket_edges by being the comprehensive research tool, with detailed classifiers and examples.

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

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

Provides explicit use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and explains the depth parameter (quick vs thorough). However, does not explicitly differentiate from sibling tools or state when not to use this tool over alternatives like polymarket_edges.

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 read-only, idempotent, etc. Description adds details like correct handling of off-calendar fiscal years, sorting by primary metric, and citation URIs, enhancing transparency without contradiction.

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

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

While each sentence adds value, the description is slightly verbose with multiple example phrases. However, it is well-structured and front-loaded with purpose.

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

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

Given no output schema, the description covers return values (paired data, citation URIs) and all required behaviors. It is complete for the tool's complexity.

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

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

Schema coverage is 100%. Description elaborates on enum values (company vs drug) with specific data sources and constraints on values (e.g., tickers/CIKs for company, names for drug), adding significant meaning beyond the schema.

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

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

The description clearly states the tool compares 2-5 entities side-by-side, with specific verb phrases and resource types. It distinguishes from sibling tools by highlighting efficiency over sequential 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 states 'ALWAYS PREFER over sequential single-pack lookups' and provides context for when to use (comparing entities) and what each type does.

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 and idempotent. The description adds that results include curated examples and full schemas ready to call, no second lookup needed. This goes beyond the annotations by detailing the output behavior.

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

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

The description is a single, well-structured paragraph. It front-loads the main purpose, then covers usage, output, and recommendation. No wasted words; efficient and clear.

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

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

For a simple search/discovery tool, the description covers purpose, usage scenarios, output details (names, descriptions, schemas), and best practice. No output schema needed; the description sufficiently informs the agent of 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 coverage is 100%, so the schema already documents all parameters. The description provides examples and context for 'query' but does not add new parameter-level meaning beyond the schema's detailed 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: 'Find tools by describing the data or task.' It specifies domains like SEC filings, FDA drugs, etc., and highlights that it returns schemas ready to use. This distinguishes it from sibling tools by advising to 'Call this FIRST'.

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

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

The description provides explicit use cases ('browse, search, look up, or discover') and recommends calling it first. It does not explicitly state when not to use it or name alternatives, but the context and domain list offer good 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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it fans out across multiple sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), mentions a soft-fail for patents (USPTO PatentsView API sunset May 2025), and details the return fields. 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 and front-loaded with examples and a preference statement. While moderately long, every sentence adds value (e.g., fan-out details, return fields). No redundant information.

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

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

Given the tool's complexity (multiple data sources, parallel call), the description is complete. It explains the return format (cik, company_name, recent_filings with URIs, fundamentals, patents, news, LEI) without requiring an output schema. It also mentions the soft-fail for patents, which is important 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 coverage is 100% with descriptions. The description adds meaning beyond the schema: it explains the 'type' parameter only supports 'company', and the 'value' parameter accepts ticker or zero-padded CIK, with examples. It clarifies that names are not supported, adding context.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It includes concrete examples ('Tell me about X', 'research Acme') and lists what it returns. It distinguishes itself from sibling tools like resolve_entity by noting that names are not supported.

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: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also states when not to use it: 'names not supported (use resolve_entity first if you only have a name).'

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data and the lifecycle of memories, which provides additional behavioral insight 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. Front-loaded with the main action, then usage guidance. Efficient 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?

For a simple tool with one parameter and no output schema, the description is complete. It explains the action, when to use, and how it relates to sibling tools.

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

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

Schema coverage is 100% with a clear parameter description. The description does not add significant new information about the parameter beyond the schema, so a baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Delete a previously stored memory by key') and distinguishes the tool from siblings 'remember' and 'recall', making its 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?

Explicitly provides when to use ('context is stale, the task is done, or you want to clear sensitive data') and suggests pairing with 'remember' and 'recall', giving clear contextual 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, openWorldHint, idempotentHint, and non-destructive behavior. The description adds behavioral context: fetches the page, extracts title/description/key links, emits standard markdown, and outputs a single text blob. No contradiction with annotations.

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

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

The description is a single, well-structured paragraph. The first sentence clearly states the main purpose, and subsequent sentences add context and use cases without wasted words.

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

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

For a tool with 2 parameters, full schema coverage, and informative annotations, the description provides everything needed: purpose, behavior, output format, and use cases. No output schema is needed as the output is described as 'a single text blob'.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by specifying that 'url' is a full URL (e.g., example.com) and that 'max_links' has default 25 and maximum 50, which are not in the schema.

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

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

The description uses a specific verb 'generate' and resource 'llms.txt file' and clearly states the action: generating a production-ready llms.txt for any URL. It distinguishes itself from sibling tools like 'ai_visibility_check' by focusing on file generation rather than visibility scanning.

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

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

The description explicitly lists three use cases: indexing a client's site, drafting for own project, and auditing a competitor. While it doesn't specify when not to use or mention alternatives, the use cases are clear and practical.

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

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

Annotations already declare readOnly=true and idempotent. Description adds return content details ('title, flattened attributes...') and 'Keyless' access, which supplements 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?

Two sentences, no redundant words, front-loaded with the main purpose. Every sentence adds value.

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

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

Given the simple tool (one parameter, no output schema, rich annotations), the description fully covers what the tool returns and access requirements, leaving 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 already describes the accession parameter with example. Description adds no further semantic meaning beyond what the schema provides, so 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?

Description clearly states verb ('Get the metadata'), resource ('EBI BioStudies study'), and specificity ('by accession') with an example. Distinguishes from sibling 'search_studies' which implies a list operation.

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 accession format and example, and mentions 'Keyless'. However, lacks explicit guidance on when to use this tool versus alternatives like 'search_studies'.

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

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

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds that it returns specific fields and scopes to the caller's subscriptions, but does not mention pagination or limits. Overall, it adds 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?

Two concise sentences: first states purpose and return fields, second gives usage advice. 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 simplicity of the tool, the description covers purpose, return, and usage. It does not mention potential limitations like pagination or filtering, but with such a small parameter set, it is adequate.

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

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

Schema coverage is 100% and the parameter include_inactive is well-described in the schema. The tool description reinforces the default behavior by mentioning 'active subscriptions', adding 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 lists the caller's active subscriptions and specifies the returned fields (id, type, etc.). It distinguishes from sibling tools like subscribe/unsubscribe by focusing on listing existing 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 says 'Use this to review what you're monitoring before adding more or to find an id to cancel,' providing clear when-to-use guidance and referencing sibling actions.

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 (which are neutral), the description discloses rate-limiting (5/day/identifier), non-quota-counting, and daily digest processing. No contradiction with annotations.

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

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

Two sentences for purpose, then usage guidelines, then constraints. No wasted words; front-loaded with key action. Every sentence earns its place.

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

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

For a feedback tool with 3 params (2 required), nested context object, and no output schema, the description fully covers the tool's operation, constraints, and best practices. No missing information needed for correct 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 covers all parameters with descriptions. The description adds value by explaining the typical length of the message (1-2 sentences, 2000 max) and emphasizing specificity, which goes beyond the schema's plain-text description.

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

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

The description clearly states the tool's purpose with specific verb 'Tell', identifies the resource 'Pipeworx team', and covers exactly what kind of feedback (bug, feature, data_gap, praise). It distinguishes itself from sibling tools as the dedicated feedback channel.

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

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

Explicitly tells when to use (bug, missing tool, praise) and provides precise instructions on what to include (refer to tools/packs, avoid pasting prompts). Also mentions rate limit and quota-free nature, setting clear expectations.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds context: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This discloses aggregation method, privacy, and caching behavior, which goes beyond the annotations.

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

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

The description is concise (three sentences plus bullet points for use cases) and front-loaded with the main action. Every sentence serves a purpose: what it does, when to use it, and additional behavioral details. No redundancy.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description adequately specifies return values (top tools, top packs, total call volume) and caching behavior. No missing information that would hinder correct invocation.

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

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

Schema coverage is 100% with one parameter (window) described in the schema. The description adds semantics: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This provides guidance on choosing the window beyond the enum values, adding value over 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 what the tool returns: 'top tools, top packs, and total call volume over a recent window.' It uses specific verbs ('Returns') and a specific resource ('top tools, top packs, and total call volume'), distinguishing it from siblings like 'discover_tools' which focuses on discovery rather than trending.

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

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

The description explicitly lists three use cases: discovering hot data sources, confirming canonical choice, and seeing alignment of use cases with agent needs. It also explains the choice of window parameter ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). This provides clear guidance on when to use the 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?

The description adds extensive behavioral details beyond annotations: no-args failure, parameter requirements, market walking, ordering checks, partition computation, semantic anchor with Jaccard similarity, partition filter, and response structure. 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 relatively long but well-structured with paragraphs and technical details. It front-loads the requirement and each sentence adds value. Slight wordiness is justified by the complexity of the tool.

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

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

Given the complexity (two modes, arbitrage detection logic), the description covers purpose, parameters, behavior, response structure, and constraints comprehensively. No output schema is needed as the response is sufficiently described. All critical aspects are addressed.

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

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

Both parameters have schema descriptions, and the tool description adds significant extra context such as examples ('fed-decision-may-2026'), mode-specific behavior, and how the tool processes the input. This exceeds the baseline of 3 for high schema coverage.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) with specific examples, making the purpose highly specific and actionable.

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 one of 'event' or 'topic' and warns that calling with no args fails. It provides clear guidance on when to use each mode and mentions what cross-event mode catches that single-event misses. However, it does not compare with sibling tools like polymarket_edges or polymarket_kalshi_spread.

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

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

Annotations indicate read-only, idempotent, open-world, non-destructive behavior. The description goes far beyond by detailing model families, edge calculation (kelly_fraction, slippage), caching (1h KV-level), and response fields including diagnostics. Adds rich behavioral context not available from annotations alone.

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 thorough but verbose, with detailed model formulas (e.g., FRED log-returns, per-sport α). While structured with bold subheadings, it could be more concise. Front-loaded with purpose, but subsequent sections are dense.

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 9 parameters, no output schema, and few annotations, the description comprehensively covers all aspects: model segments, response structure (by_segment, fed_candidates, diagnostics), caching, and parameter interactions. No missing critical information 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% so baseline is 3, but the description adds layered meaning: e.g., slippage_pp 'subtracted from raw |edge| before ranking', min_partition_leg_kelly explained as 'applies to per-leg Kelly inside top_legs'. This provides operational guidance 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 states 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price', directly naming verb, resource, and outcome. It distinguishes from sibling tools like polymarket_arbitrage and polymarket_kalshi_spread by focusing on Pipeworx-model disagreement rather than cross-platform arbitrage.

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

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

Explicitly states 'Built for "what should I bet on today"' and explains how it avoids paging hundreds of markets. Guidance on segment categories and tradeable-edge knobs provides context. Missing explicit when-not-to-use or alternatives, but the description is clear enough for an agent to decide.

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

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

Beyond annotations (readOnly, openWorld, idempotent), description explains real signal vs meaningless spreads due to bet shape mismatch, temporal alignment, and compatibility warnings. 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 dense but well-structured with clear sections for modes, response, and safety fields. Front-loaded with purpose. Slightly verbose but every sentence adds necessary detail.

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

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

For a read-only data tool with 3 parameters and no output schema, the description fully covers response structure, safety fields, temporal constraints, and conditions for meaningful spreads. No obvious 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 each parameter. Description adds value by explaining mode behavior (topic vs explicit, override logic) and provides concrete examples, exceeding 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 computes cross-venue spreads between Kalshi and Polymarket. It distinguishes itself from siblings like polymarket_arbitrage by focusing on inter-venue comparisons and explicitly describes two modes (topic shortcuts and explicit pairing) with safety fields.

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 detailed guidance on two usage modes (topic vs explicit), lists valid topic shortcuts, and warns that most pre-mapped topics currently return compatibility warnings. Implicitly differentiates from siblings but lacks explicit 'when not to use' statements.

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

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

Annotations already declare readOnlyHint and idempotentHint; description adds scoping to identifier and listing behavior. No contradictions, adds 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 concise sentences, front-loaded with main action, no extraneous words.

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

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

For a simple retrieval tool with one optional parameter and no output schema, the description covers behavior, scoping, and pairing comprehensively.

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

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

Schema coverage is 100%, and the description largely restates the schema's parameter description. Does not add significant new information beyond the schema.

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

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

Description clearly states the tool retrieves a value saved via remember or lists all keys, specific verb+resource, distinguishes from siblings like remember and forget.

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

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

Explicitly states to use for looking up previously stored context without re-deriving, mentions scoping and pairing with remember/forget. Lacks explicit 'do not use' scenarios but context is sufficient.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context such as mark_read flagging events as read, affecting future calls. No contradiction with annotations.

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

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

The description is a single paragraph that front-loads the main purpose and systematically covers filtering options, returned fields, and alternative access. Each sentence contributes meaning, though it could be slightly more concise.

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

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

Given the tool has 5 optional parameters, no output schema, and full annotations, the description covers: what is returned, filtering by type/time, the mark_read behavior, and an alternative access method. It does not discuss pagination or rate limits but is generally 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%, so the description does not need to repeat parameter definitions. It adds value by giving examples (e.g., type 'sec_8k') and explaining the effect of mark_read, which enhances understanding beyond the schema descriptions.

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

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

The description clearly states the tool pulls fired events from the subscription feed and returns recent alerts with specific fields (source, citation_uri, raw event payload). The verb 'pull' and resource 'subscription feed' are precise, and the tool is distinguishable from sibling tools like list_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 clear context: it mentions polling works fine and points to an alternative HTTP endpoint for scripts/dashboards. However, it does not explicitly state when to use this tool versus alternative tools or when not to use it.

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

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

Annotations already show read-only, idempotent, non-destructive. Description adds: multi-source fan-out, GDELT-to-GNews fallback, USPTO soft-fail, date format flexibility, and return structure. Adds significant 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?

Long but well-organized with examples and clear guidance. Each section adds value, though could be slightly more compact.

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 complexity (multi-source, fallback, date formats, return values), the description is thorough and complete. No output schema but mentions returned fields.

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

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

Schema covers all 3 parameters with descriptions. Description adds usage nuance: '30d' for monitoring, ticker vs CIK for value, and expands on 'since' with ISO and relative examples.

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

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

Description clearly states it's a 'change feed for a company in the last N days/weeks/months' and lists specific sources (SEC, GDELT/GNews, USPTO). It explicitly distinguishes from sibling tool entity_profile.

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 example queries for when to use it and explicitly tells to use entity_profile for static profiles. Includes fallback behavior and soft-fail notes.

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 non-readonly, non-open world, idempotent, non-destructive. Description adds context about key-value storage, scoping, and session persistence, which is valuable beyond annotations. No contradiction.

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

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

Every sentence earns its place: purpose, usage, details on scoping and persistence, pairing. Well-structured and front-loaded.

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

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

Despite no output schema, the description covers usage, parameters, and pairing. The tool's simple return value (success) is implied. Complete for its 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 description coverage is 100%, so baseline is 3. Description adds minimal extra meaning beyond schema, primarily repurposing examples. Adequate but not exceptional.

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 saves data for reuse across conversations/sessions, using a specific verb-resource pair. It distinguishes from sibling tools recall and forget by mentioning pairing, and avoids tautology.

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

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

Explicitly describes when to use ('when you discover something worth carrying forward') and how it pairs with recall and forget. Also mentions scoping by identifier and persistence differences, providing comprehensive 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 declare readOnly, openWorld, idempotent, not destructive. The description adds that each call cascades through several internal endpoints, replacing 2-3 manual lookups, hinting at potential latency and efficiency gains.

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

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

Concise at ~150 words, each sentence is informative. Uses bolded terms and examples for clarity. No redundancy or filler.

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

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

Despite no output schema, the description fully explains return fields for both entity types including citation URIs, which 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?

100% schema coverage but description adds context: examples of acceptable values ('AAPL', '0000320193', 'ozempic'), mentions auto-disambiguation for company, and lists what each type returns (ticker, CIK, RxCUI, 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 uses specific verbs like 'resolve' and 'look up' and clearly states it converts names to canonical IDs. It provides concrete examples ('ticker for…', 'CIK for…', 'RxCUI for…') and distinguishes itself from siblings by positioning as the first step for ID lookup.

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 FIRST whenever you have a name but need an ID' and describes supported types. While it doesn't say when not to use it, the context is clear enough for an agent to decide.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive nature. The description adds useful context by revealing that the tool internally probes with ai_visibility_check and returns aggregated results, which is consistent with annotations.

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

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

The description is concise: 3-4 sentences, front-loaded with the main purpose, and no extraneous information. Every sentence adds meaningful detail.

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

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

Despite lacking an output schema, the description clearly states the return format (ranked list with score, confidence, signal density). It covers all parameters and the internal aggregation process, making it 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 descriptions for all 4 parameters. The description adds value beyond the schema by explaining that the first entity in the array is treated as the 'subject' and that context is shared across probes.

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: 'Compare AI visibility across multiple entities side-by-side.' It specifies the action (probe with ai_visibility_check, rank by score) and differentiates from sibling tools like ai_visibility_check by explicitly noting that it compares multiple 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?

The description provides usage context ('Useful for competitive AI-marketing audits') and an example question. It implicitly distinguishes from single-entity sibling ai_visibility_check but does not explicitly state when not to use or mention other alternatives like compare_entities.

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

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

Annotations already declare readOnlyHint, idempotentHint, etc., but the description adds critical behavioral traits: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. No contradictions.

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

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

The description is a single dense paragraph but well-structured with front-loaded purpose. Every sentence adds value, though slightly verbose. Could be slightly more concise, but overall efficient.

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

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

Despite no output schema, the description lists return elements (summary block, per-advisory detail, links, alternative versions) and covers edge cases (partial failures, sources_failed). Lacks explicit JSON structure, but 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 descriptions for both parameters. The description adds value: notes that scoped packages are accepted for 'package' and explains default behavior for 'version' (latest published when omitted).

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's a composite check for adding an npm package, listing sources (deps.dev, bundlephobia) and what it evaluates (license, advisories, bundle size, etc.). It is distinct from all sibling tools, which are unrelated.

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

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

Explicitly tells when to use: 'Use whenever an agent asks...' with concrete examples. Also specifies scope: NPM only, with fallback for other ecosystems ('deps.dev:version directly'). Provides clear usage boundaries.

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 behavioral context by specifying the return format (list of fields) and the nature of the database (keyless, cross-archive linking). It does not contradict annotations.

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

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

The description is two sentences long, front-loaded with the core purpose, and contains no extraneous information. Every sentence provides essential details: what the tool does, where it searches, and what it returns.

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

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

For a tool with three well-documented parameters and no output schema, the description provides sufficient context: the purpose, input expectations (keyword), and output fields. It covers all necessary aspects for an agent to understand the tool's function and results.

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 (query, page, limit) are already documented. The description adds value by explaining that the query parameter takes keywords and listing the return fields, which clarifies what information the user can expect from the query.

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 searches EBI BioStudies by keyword and lists the specific fields returned (accession, title, authors, type, release date, link/file counts). This distinguishes it from sibling tools like get_study (which retrieves a single study) and search_within (which likely searches within a resource).

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

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

The description provides clear context for use: it is a keyword search tool for biological studies across EBI archives. It implies use for broad discovery but does not explicitly exclude scenarios or name alternatives. However, the purpose is clear enough for an agent to infer when to use it versus other tools.

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

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

Annotations already indicate read-only and idempotent; description adds details: returns top-N passages with offsets and similarity scores, uses BGE embeddings with 500-char windows, truncation at 200K chars with flagging.

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 dense paragraph, each sentence adds value: purpose, use case, technical details. Front-loaded with key info.

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

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

No output schema, but description explains return values (top-N passages with offsets and scores) and truncation behavior. Covers all needed context given parameter count and annotations.

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

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

Schema description coverage is 100%, so baseline 3. Description adds minimal extra context for parameters beyond schema (e.g., max chars for text, limit range).

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

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

Description clearly states the verb 'search' and resource 'inside a fetched record', and distinguishes from siblings by mentioning the use case of avoiding prompt cramming and pairing with 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 says 'Use when the record is too big to cram into the prompt' and suggests an alternative tool (ask_pipeworx_grounded) for grounded reasoning.

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

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

The description adds behavioral context beyond annotations: it explains the requirement for a Pipeworx OAuth account, details on types and their parameters, delivery channels, SMS caps, and phone verification. Annotations are consistent (readOnlyHint=false, destructiveHint=false) and description does not contradict them.

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

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

The description is well-structured with a clear main action followed by requirements, types, and delivery details. Though slightly lengthy, every sentence adds value and the information is front-loaded.

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

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

Given no output schema, the description covers essential aspects: prerequisites, type options, parameter examples, delivery channels, and limits. It lacks error scenarios or idempotency details but is otherwise thorough for a creation tool.

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

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

Schema coverage is 100%, and the description adds substantial meaning with examples, constraints, and usage details for all parameters, including nested fields like delivery and type-specific params. This goes well beyond the schema itself.

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

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

The description clearly states the verb 'Create' and the resource 'proactive monitoring subscription to a live-data event stream', returning the new subscription id. It distinguishes itself 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?

While the description specifies prerequisites (Pipeworx OAuth account) and supported types, it does not explicitly guide when to use this tool versus alternatives like pipeworx_trending or polymarket_edges. Usage context is implied but not explicitly stated.

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

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

The description adds value beyond annotations by explaining that the row is deactivated (not deleted) and that historical events remain available. This aligns with destructiveHint=false and idempotentHint=true (multiple calls safe). 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 highly concise with three sentences: action, constraint, effect. No redundant information, front-loaded with the core purpose. Every sentence earns its place.

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

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

Given the tool's simplicity (one parameter, no output schema), the description adequately covers the behavioral context (ownership, deactivation). However, it does not specify the return value or success/error indicators, which would be helpful for 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% for the single parameter 'id', and the description adds no new semantic details beyond stating 'by id'. The schema already describes it as 'Subscription id (uuid) returned by subscribe.' Description does not enhance parameter 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 action ('Cancel a subscription by id') and distinguishes it from siblings like 'subscribe' and 'list_subscriptions'. It also adds specific constraints (ownership enforcement) and outcome (deactivation, not deletion), 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 implicitly guides usage by mentioning ownership enforcement and soft-deletion, but it does not explicitly state when to use this tool versus alternatives (e.g., 'subscribe', 'list_subscriptions'). There is no direct comparison or when-not 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, openWorldHint, idempotentHint. The description adds context: uses SEC EDGAR + XBRL, returns verdict with citation, v1 with limited scope. This goes beyond annotations but does not contradict them.

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

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

The description is a single paragraph that front-loads purpose, then examples, scope, and output. Every sentence adds value, though slightly verbose.

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

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

Given the tool's complexity (claim verification with one param and no output schema), the description covers expected output (verdict types, citation, delta) and scope limitations (company-financial claims only). Annotations provide safety profile. Complete for the intended 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?

Only one parameter 'claim' with schema description already covering 100%. The description adds real-world examples and clarifies it's natural-language, adding semantic value beyond the schema.

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

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

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources. It provides specific verb-resource pairs (fact check, verify the claim) and distinguishes itself from siblings by mentioning 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?

Explicitly states when to use: 'whenever the agent needs to check whether something a user said is factually correct.' Also specifies scope limitations: 'v1 supports company-financial claims' via SEC EDGAR + XBRL, implicitly excluding non-financial claims.

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