Gtex

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

Annotations already indicate readOnlyHint, openWorldHint, and destructiveHint=false. The description adds valuable context: it routes to many tools, returns citations, and handles natural language. No contradictions with annotations.

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

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

The description is front-loaded with 'PREFER OVER WEB SEARCH' and each sentence adds value. It could be slightly more concise, but it is well-structured and not 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 simple input schema (1 param) and no output schema, the description is largely complete. It explains the tool's scope, behavior, and output format (citations). 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?

Only one parameter (question) with schema description 'Your question or request in natural language'. Schema coverage is 100%, so baseline 3. The description adds no further parameter-specific details but the overall tool description clarifies how the question is processed.

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

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

The description explicitly states the tool routes factual questions to appropriate sources among 1,423+ tools, with clear examples of domains (SEC filings, FDA data, etc.). It distinguishes itself from web search, making the purpose 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 provides explicit when-to-use guidance (prefer over web search for structured data, factual questions) and gives concrete examples ('current US unemployment rate', 'Apple's latest 10-K'). It also implies when not to use (non-factual queries) by omission.

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 provide safety (readOnlyHint, openWorldHint, destructiveHint false). The description adds behavioral details: it resolves markets, classifies bets, fans out to packs, and returns evidence packets with comparison. 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 that is front-loaded with purpose. All sentences are relevant and contribute to understanding, though it could be slightly restructured for easier parsing.

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 explains the return (evidence packet plus comparison) and covers market resolution, classification types, and fan-out behavior. It is fairly complete for a research tool, though error handling is not mentioned.

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 providing examples for market (slug, URL, question) and detailing depth options (quick=2-3 sources, thorough=full fan-out), enhancing the schema's explanations.

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 markets, classifying bets, and returning evidence packets. It uses specific verbs and resources, and distinguishes from siblings like polymarket_arbitrage and 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?

Explicit use cases are given: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. However, it does not explicitly state when not to use or list alternatives, missing the full 'when-not' criterion.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: data sources (SEC EDGAR/XBRL, FAERS, FDA), output includes citation URIs, and it is a read-only aggregation tool.

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

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

The description is a single paragraph but efficient: opens with core purpose, then usage triggers, then type-specific details, and ends with benefit. Every sentence adds value, though slightly 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?

No output schema exists, but description adequately explains returns (paired data + citation URIs). Input is fully covered, and the tool's role compared to siblings is clear. Could mention pagination or limitations, but fine given scope.

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 meaning beyond schema: for 'type' it details what fields each entity type returns, and for 'values' it provides examples and constraints (max 5).

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

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

The description clearly states it compares 2-5 companies or drugs side by side, specifying the data sources and output format. It distinguishes itself from sibling tools like entity_profile by emphasizing multi-entity comparison.

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

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

The description explicitly lists user intents that trigger this tool (e.g., 'compare X and Y', 'X vs Y', 'stack up', etc.) and notes it replaces 8-15 sequential calls, providing clear context for when 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 declare readOnlyHint=true and destructiveHint=false, so the read-only nature is clear. The description adds that it returns 'top-N most relevant tools with names + descriptions,' aligning with no mutations. No contradictions.

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

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

The description is concise (3-4 sentences) and front-loaded: purpose, usage guidance, result format, and when to use. Every sentence adds value; no fluff.

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

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

No output schema, but description explains return type (top-N tools with names+descriptions). With only 2 parameters (one required), this is sufficient. Minor improvement could mention example output but not necessary.

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

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

Schema covers both parameters with descriptions (query and limit). Description does not add beyond schema. With 100% schema coverage, baseline 3 is appropriate.

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

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

The description states 'Find tools by describing the data or task' and lists many specific domains (SEC filings, financials, FDA drugs, etc.). It clearly distinguishes from sibling tools (e.g., compare_entities, entity_profile) by being a meta-tool for discovery.

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

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

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and advises 'Call this FIRST when you have many tools available and want to see the option set' – clear when to use and context.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds context on return data (SEC filings, financials, patents, news, LEI) and citation URIs. Does not contradict annotations. Lacks mention of potential staleness or rate limits, but overall transparent.

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

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

Description is longer but each sentence adds value. Front-loaded with clear purpose. Could trim some example phrases, but overall efficient for the complexity.

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

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

No output schema, yet description fully explains what is returned (SEC filings, fundamentals, patents, news, LEI). Covers complex multi-source aggregation and mentions citation URIs, leaving no major gaps 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?

Schema coverage is 100%. Description adds meaning beyond schema: explains 'type' is only 'company', 'value' can be ticker or CIK, and explicitly states that names are not supported, linking to resolve_entity for name resolution.

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

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

Description starts with 'Get everything about a company in one call' and provides numerous concrete query examples (e.g., 'tell me about X', 'research Microsoft'). Clearly distinguishes from siblings by noting it replaces calling 10+ pack tools across multiple data sources.

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 (user asks for company profile) and when not to (names not supported; directs to resolve_entity first). Provides alternative tool name and clear input constraints.

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 aligns with annotations (destructiveHint: true) and adds behavioral context such as 'clear sensitive data' and removing stale context, which goes 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?

Two sentences with no extraneous content: first sentence states the primary action, second provides usage guidance and sibling pairing. Efficiently 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?

For a simple tool with one parameter and no output schema, the description covers purpose, usage context, and sibling relationships thoroughly.

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

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

The input schema has 100% coverage with a clear description for the 'key' parameter. The tool description does not add additional meaning beyond the schema, meeting the baseline.

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

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

The description clearly states the verb 'Delete' and the resource 'a previously stored memory by key'. It distinguishes itself from sibling tools 'remember' and 'recall' which handle storage and 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 provides explicit usage contexts: when context is stale, task is done, or clearing sensitive data. It also advises pairing with 'remember' and 'recall', though it does not explicitly state when not to use.

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

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

Annotations already indicate the tool is read-only and non-destructive. The description adds a constraint on input (by Gencode id or symbol) but does not disclose output details or behavior edge cases.

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

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

The description is extremely concise, one sentence with no fluff. It is front-loaded but could benefit from additional structure to guide usage.

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

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

With no output schema, the description should describe the return format or metadata fields. It does not, leaving the agent uncertain about what to expect. Also, the single parameter is described minimally.

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

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

The schema has 0% parameter description coverage. The description adds meaning by stating the parameter is for a Gencode id or symbol, but lacks format details (e.g., prefix expectations) or validation rules.

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 gene metadata using a Gencode id or symbol. It is specific about the resource and the lookup key, but does not differentiate from siblings like entity_profile that may also serve gene data.

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

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

No guidance on when to use this tool versus alternatives. The description does not mention any context or prerequisites, leaving the agent to infer usage.

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

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

Annotations provide safety profile (readOnly, non-destructive). Description adds tissue aggregation detail but omits return format. Adequate given annotations.

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

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

Single efficient sentence. Slightly more structure (e.g., listing return shape) could improve without losing conciseness.

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

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

Adequate for a simple single-parameter tool, but missing return format details (output schema absent).

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 parameter description fully (100%). Description restates gene relevance but adds no new 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?

Description clearly states verb ('median expression'), resource ('across tissues for a gene'), and units ('TPM'), distinguishing it from siblings like single_tissue_eqtls.

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

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

No guidance on when to use this tool vs alternatives. Lacks context for selection.

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

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

Annotations provide readOnlyHint=false, destructiveHint=false, openWorldHint=false. The description adds valuable context: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota. 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 concise and well-structured, front-loading the purpose, then providing usage guidelines, formatting instructions, and rate limits. Each 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?

The description covers purpose, usage, constraints, and behavioral traits. Given the absence of an output schema, it does not need to describe return values. It could mention anonymity, but overall it is sufficiently 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 detailed descriptions for each parameter. The description adds some usage context (e.g., 'describe in terms of Pipeworx tools/packs') but does not significantly enhance parameter meaning beyond the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: sending feedback about broken, missing, or needed features to the Pipeworx team. It specifies the resource (Pipeworx tools/packs) and distinguishes itself from siblings by being the dedicated feedback mechanism, with explicit types (bug, feature, etc.).

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

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

The description provides explicit when-to-use scenarios: bug, feature/data_gap, praise. It also gives instructions on how to describe the issue (avoid pasting end-user prompts, be specific). It lacks explicit when-not-to-use conditions, but the guidance is clear and actionable.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds behavioral details: walks child markets, searches across events, groups them, checks monotonicity, and returns ranked opportunities with suggested trade direction and reasoning. No contradictions.

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

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

The description is efficiently structured with a clear first sentence stating purpose, followed by well-organized explanations of the two modes. Every sentence adds value with no redundancy.

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

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

Given the tool's complexity (two modes, cross-event logic), the description covers all essential aspects: mode selection, behavior, and return value description. Without an output schema, it sufficiently describes outputs as ranked opportunities with reasoning.

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 substantial meaning by explaining each mode's behavior with examples (event slug vs. topic string), clarifying usage 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 finds arbitrage opportunities on Polymarket by checking monotonicity violations across related markets. It distinguishes two modes (event and topic), making the purpose specific and differentiating from siblings.

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

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

The description explicitly explains when to use each mode: event mode for a single event slug, topic mode for cross-event searches. It provides reasoning for the topic mode (catches cases missed by single-event mode), offering clear context on selection.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: explains the model (lognormal from FRED + coinpaprika price), process (scans top markets, groups by asset, fetches price history once, computes probability, ranks by edge), and scope (V1 covers crypto-price bets). 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 concise (~4 sentences), front-loaded with purpose, and every sentence adds value. It efficiently communicates the tool's function, process, and intended use without unnecessary 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?

The tool has no output schema, so the description should clarify return values. It states 'Returns top N ranked by edge magnitude with suggested trade direction,' which covers the main outputs. However, it could be more explicit about the fields (e.g., market name, edge value, direction). Given the tool's complexity is moderate, this is fairly complete but not fully detailed.

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

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

Input schema has 3 parameters with 100% description coverage, so baseline is 3. The description does not add meaning beyond schema descriptions for each parameter, but it does contextualize the overall ranking logic which relates to the parameters. No additional semantic value beyond what schema provides.

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

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

The description clearly states the tool scans the highest-volume Polymarket markets and returns those where Pipeworx data disagrees most with the market price, specifying it covers crypto-price bets and outputs ranked edges with trade direction. This distinct purpose separates it from siblings like 'polymarket_arbitrage' and 'bet_research'.

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 the use case: 'Built for the what should I bet on today question — agents/users discover opportunities without paging through hundreds of markets.' It implies when to use (opportunity discovery) but does not explicitly state when not to use or mention alternatives, though the context of siblings is present.

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 about scoping to an anonymous identifier and the pairing with remember/forget, which is useful 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 concise (3 sentences) with a clear front-loading of the core function. Every sentence adds value: function, use case, and additional context. 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?

While the description is thorough for a simple tool, it does not specify the return format (e.g., string or list) or any error scenarios. Given no output schema, this is a minor gap.

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 good parameter description. The description reinforces the behavior of omitting the key and provides examples, adding 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 retrieves a value saved via remember or lists all saved keys if omitted. It provides concrete examples (ticker, address, notes) and distinguishes itself from siblings 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?

It explains when to use (to look up stored context) and suggests pairing with remember and forget. However, it does not explicitly state when not to use or provide alternatives, though siblings are clear.

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

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

Annotations already indicate read-only, open-world, non-destructive behavior. The description adds valuable context: it fans out to SEC EDGAR, GDELT, and USPTO in parallel, and mentions the output includes structured changes, count, and URIs. No contradictions with annotations.

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

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

The description is concise, well-structured, and front-loaded with the core purpose. Every sentence adds value: use-case examples, backend data sources, and parameter format details. 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?

The description covers the tool's operation, data sources, parameter formats, and high-level output structure. Without an output schema, it adequately describes return values. It could detail the exact structure of 'changes', but for typical use 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?

Schema coverage is 100% with descriptions for all parameters. The description adds examples for the 'since' parameter (ISO date or relative shorthand like '7d', '30d'), clarifies that 'type' only supports 'company', and gives examples for 'value' (ticker or CIK).

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

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

The description clearly states the tool's purpose: retrieving recent changes for a company. It provides specific use-case examples ('what's happening', 'any updates') and distinguishes from sibling tools like entity_profile or compare_entities by focusing on temporal updates.

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

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

The description gives explicit query patterns for when to use the tool (e.g., 'what's happening with X?'). It does not explicitly list when not to use or name alternative tools, but the context signals and sibling tool names provide implicit 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?

Describes persistence (authenticated persistent, anonymous 24h) and scoping by identifier, adding value beyond annotations.

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

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

Four concise sentences, front-loaded with purpose, 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?

Covers main aspects but does not specify overwrite behavior on duplicate keys, a minor gap.

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 covers both params with descriptions; tool description adds use-case context but no additional param-specific details.

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

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

Clearly states it saves data as key-value pairs for reuse. Explicitly distinguishes from siblings recall and forget.

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

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

Specifies when to use: when discovering info worth carrying forward, with examples. Advises pairing with recall and forget.

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 valuable transparency by detailing exactly what outputs to expect (IDs plus pipeworx:// citation URIs) and that it consolidates multiple lookups. This goes beyond the annotations without contradicting 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 concise at four sentences, each serving a purpose: defining the action, specifying use cases, providing examples, and offering strategic advice. It is front-loaded with the key purpose and free of 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 simplicity (two required parameters, no nested objects) and the absence of an output schema, the description fully covers what the tool does and what it returns. There are no gaps in understanding; an agent can confidently invoke 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?

The input schema covers both parameters with descriptions, achieving 100% coverage. The description's examples (e.g., 'Apple → AAPL') add some semantic richness but do not significantly expand on the schema. The baseline of 3 is appropriate since the schema already defines the parameters well.

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 return canonical/official identifiers (CIK, ticker, RxCUI, LEI) for companies or drugs. It explicitly distinguishes this from sibling tools by positioning it as a prerequisite: 'Use this BEFORE calling other tools that need official identifiers.' The verb 'look up' and the specific ID systems make 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 guidance on when to use the tool: 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' It also gives concrete examples and notes that it 'Replaces 2–3 lookup calls,' helping an agent decide when this tool is appropriate.

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, and destructiveHint, so the safety profile is clear. However, the description adds no behavioral context (e.g., data source, filtering logic, or return format), though with annotations the burden is lower.

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

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

The description is a single concise sentence, front-loaded with the core purpose. It wastes no words, but is somewhat under-specified given the tool's capabilities.

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 0% schema coverage, no output schema, and minimal description, the tool is incomplete for an agent. Missing details include valid tissue values, significance threshold, and output format. Annotations provide some context but not enough.

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

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

Schema description coverage is 0%, and the description does not explain any parameters. While 'gencode_id' is somewhat self-explanatory, 'tissue' is ambiguous (free text? controlled list?). The description adds no value beyond the schema's field names and types.

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 retrieves significant single-tissue eQTLs for a gene, using specific verbs and resources. It distinguishes from sibling tools like 'gene' or 'median_expression' by focusing on eQTLs, but could explicitly mention that it queries by gene 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?

No guidance on when or when not to use the tool, or how it compares to alternatives like 'gene' or 'top_expressed_genes'. The description lacks any usage context or prerequisites.

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, and open world. The description adds 'List', which implies retrieval, but provides no additional behavioral insight (e.g., return format, pagination, or data source). With annotations covering the safety profile, the description adds minimal value.

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

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

The description is three words, front-loaded, and contains no extraneous information. Every word is earned.

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

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

For a simple list tool with no parameters and clear annotations, the description is nearly complete. Including a brief note on purpose (e.g., 'available tissues for filtering other queries') would improve completeness, but current version 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?

No parameters exist, so schema coverage is 100%. Baseline score of 4 applies because the description does not need to compensate. The description adds nothing about parameters, which is acceptable given there are none.

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 'List of tissues' concisely states the tool returns a list of tissues. It is clear in purpose, but could specify which tissues (e.g., from a particular ontology), especially since sibling tools like 'single_tissue_eqtls' imply tissue-specific 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?

No guidance on when to use this tool versus siblings like 'gene', 'median_expression', or 'top_expressed_genes'. The description does not clarify that this tool likely provides a controlled vocabulary for other tools, leaving the agent to infer usage context.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds no behavioral details beyond that, such as pagination or data scope.

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

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

Extremely concise at 6 words, but appropriately sized given the tool's simplicity. Every word is necessary, though it could be slightly expanded for 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?

The description lacks completeness for a tool with 4 parameters. It does not explain the meaning of 'top expressed', the data source (GTEx), or any limitations. Output schema is absent, increasing the need for descriptive guidance.

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 description is not required to add parameter details. However, it does not explain what 'top expressed' means or how 'num' affects results, providing no extra 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 retrieves top expressed genes for a given tissue, using a specific verb and resource. However, it does not differentiate from sibling tools like 'gene' or 'median_expression', which could cause 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?

No guidance on when to use this tool versus alternatives like 'median_expression' or 'gene'. No scenarios or exclusions 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?

Disclosures beyond annotations: returns verdict, structured form, actual value with citation, percent delta; also clarifies v1 scope (company-financial claims) and data sources. 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 concise paragraphs, front-loaded with purpose, no filler. Every sentence serves a clear function.

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 one parameter and no output schema, the description fully informs about input format, output structure, supported domains, and the tool's efficiency benefit. 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 a description of the 'claim' parameter. The description adds value by providing examples and clarifying the natural-language format, as well as the supported claim domain, justifying one point 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 validates factual claims against authoritative sources, with specific verbs and examples, distinguishing it from sibling tools like bet_research 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 describes when to use (checking truth of user statements) and gives example phrasings. Does not list alternatives or when-not-to-use, but the context is clear from sibling names.

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