Dailymed

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

No annotations are provided, so the description fully bears the burden. It discloses that the tool routes across 300+ sources, picks the right tool, and fills arguments. Lacks mention of failure modes, rate limits, or output format but is otherwise 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?

The description is three concise sentences plus examples, with no redundant information. Every sentence adds value: purpose, usage, examples.

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 (300+ sources), the description covers purpose, usage, and examples adequately. However, it omits the output format (e.g., text or JSON) and any potential limitations or errors.

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 single parameter 'question'. The description adds significant value by showing example questions and explaining how the tool interprets them, far beyond the schema's brief 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 answers natural-language questions by automatically routing to the right data source. It lists examples of question types and explicitly distinguishes itself from sibling tools like get_drug and search_drugs.

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 with example queries and explains that it avoids needing to figure out which tool to call. However, it does not specify when not to use this tool (e.g., for known specific direct calls).

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, openWorldHint=true, destructiveHint=false, and the description adds behavioral details beyond this: it resolves the market, classifies the bet, fans out to specific packs, and returns a comparison. No mention of auth or rate limits, but the core behavior is well disclosed.

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 about 80 words, front-loading the core purpose and then adding details. It avoids redundancy and every sentence contributes useful information.

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

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

Given no output schema, the description adequately describes the output as an evidence packet and comparison. It covers input types, process (resolve, classify, fan out), and output. For a tool of moderate complexity, this is fairly complete.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds meaning by explaining that 'market' accepts slug, URL, or question text, and hints that 'depth' controls thoroughness. It also explains how the tool uses the market parameter to fan out to packs, enhancing understanding beyond the schema.

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

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

The description explicitly states it researches Polymarket bets by pulling Pipeworx data in one call. It lists input types (slug, URL, question text) and outputs (evidence packet, comparison). This clearly distinguishes it from siblings like ask_pipeworx or entity_profile, which serve different purposes.

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

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

The description provides explicit use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It also notes it's the core demo product. While it doesn't state when not to use it, the context is clear enough to guide appropriate 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?

With no annotations, the description carries the full burden. It discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) and the return format ('paired data + pipeworx:// citation URIs'), but omits behavioral traits such as read-only status, authentication requirements, rate limits, or error handling. The description provides moderate transparency but lacks details on side effects or operational constraints.

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, with one paragraph of 4-5 sentences. It front-loads the primary purpose and includes only essential information: usage triggers, parameter details, data sources, and efficiency gain. Every sentence serves a clear purpose without redundancy.

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

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

Given the lack of annotations and output schema, the description adequately explains inputs, data sources, metrics per type, and output format. It also implicitly differentiates from sibling tools by emphasizing multi-entity comparison. However, it could be more complete by mentioning expected error cases or response structure for edge cases.

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

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

The input schema already provides 100% description coverage for both parameters. The description adds value by giving real-world examples (e.g., ['AAPL','MSFT'], ['ozempic','mounjaro']) and clarifying the expected format for each type, which enhances understanding beyond the schema alone.

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

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

The description clearly states the action ('compare'), the resources ('companies or drugs'), and the format ('side by side in one call'). It provides specific examples of user queries that trigger this tool, making the purpose unambiguous and easily understood.

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 identifies when to use the tool by listing trigger phrases ('compare X and Y', 'X vs Y', etc.) and notes it replaces 8–15 sequential calls, implying efficiency. However, it does not explicitly state when not to use it or mention sibling tools like entity_profile or get_drug as alternatives for single-entity queries.

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

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

No annotations are provided, but the description discloses that it returns the top-N most relevant tools with names and descriptions. It lacks details on ordering or whether results are deterministic, but this is sufficient for a discovery 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 with useful information, though listing many domains makes it slightly verbose. Every sentence is valuable and it is front-loaded with the 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?

No output schema exists, but the description explains the return format (names + descriptions). For a simple discovery tool, this is adequately complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context about returning top-N relevant tools and complements the limit and query parameters with natural language usage.

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

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

The description clearly states it finds tools by describing data or task, listing many domains, and explicitly differentiates itself by advising to call this first when many tools are available.

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

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

The description explicitly states when to use it ('Use when you need to browse, search, look up, or discover what tools exist for...') and when to prioritize it ('Call this FIRST...').

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

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

No annotations are provided, so the description carries the full burden. It describes the tool as a read operation and lists return items with citation URIs. It does not mention error behavior or empty results, which would be helpful for a complete picture.

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 purpose and usage, then details contents and input format. Every sentence adds value without redundancy.

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

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

Given the complexity of aggregating multiple data sources and no output schema, the description adequately explains what is returned (SEC filings, fundamentals, patents, news, LEI). It misses potential limitations like pagination or error handling, but overall provides sufficient context 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%, but the description adds significant value: it explains the value parameter accepts ticker or zero-padded CIK, explicitly advises not to use names, and recommends resolve_entity for name resolution. This goes well beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: to get everything about a company in one call. It lists specific data types returned and distinguishes from siblings by mentioning it aggregates multiple sources and suggesting resolve_entity for name inputs.

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 usage scenarios (e.g., 'tell me about X') and a when-not-to-use case (names not supported, use resolve_entity first). This helps the agent select the correct 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?

No annotations are provided, so the description carries full burden. It states 'Delete' which implies irreversible action, but lacks details on potential side effects or permissions. For a simple key-delete, this is fairly transparent, but could be more explicit about irreversibility.

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

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

Three sentences, front-loaded with the action. Every sentence adds essential information without redundancy or waste.

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 low complexity (single parameter, no output schema), the description provides complete contextual information: purpose, usage scenarios, and relationship 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 description for 'key'. The description adds value by clarifying that the key refers to a 'previously stored memory', giving 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 action: 'Delete a previously stored memory by key.' It distinguishes from sibling tools like 'remember' (store) and 'recall' (retrieve), making the purpose unambiguous.

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

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

The description provides explicit when-to-use guidance: 'when context is stale, the task is done, or you want to clear sensitive data.' It also suggests pairing with siblings, effectively directing the agent on alternative 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 are empty, so description carries full burden. Only states what it returns ('Full SPL metadata + sections'), but does not disclose behavior like read-only nature, rate limits, or authentication requirements. Minimal transparency.

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

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

One short sentence, no wasted words. Could be slightly more informative but highly 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?

No output schema, so description should hint at return structure. 'Full SPL metadata + sections' is vague but gives a general idea. Lacks mention of error handling or specific section names. Adequate for a tool with a single input.

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 a single parameter with description 'DailyMed setId UUID'. The description mentions 'by set_id' but adds no new 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?

Clearly states verb (get), resource (SPL metadata + sections), and identifier (by set_id). Distinguishes from sibling tools like search_drugs which search by name.

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

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

No guidance on when to use this tool versus alternatives like search_drugs or list_labels_for_drug_name. Does not specify that it requires a known set_id.

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 are empty; the description does not disclose behavioral traits such as read-only nature, authentication needs, or rate limits. Minimal transparency beyond the basic purpose.

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 sentence, concise and front-loaded with key information. It is efficient but could be more structured or informative.

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, so the description should explain return values or behavior; it does not. It also omits details about pagination or filtering, leaving the tool under-specified 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 the schema already documents both parameters. The description adds no extra meaning beyond what the schema provides, earning baseline score.

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

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

The description states it is a pharmacologic/drug-class reference for types EPC, MoA, PE, CS. It implies listing or referencing classes but lacks a clear verb like 'list' or 'retrieve'. It distinguishes from siblings by being a dedicated class reference, but does not explicitly differentiate.

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 like search_drugs or get_drug. The description only states what it is, not context or exclusions.

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

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

No behavioral details are provided. There are no annotations, and the description does not disclose pagination behavior, result limits, or any side effects. The tool's input parameters (page, pagesize) suggest pagination, but the description does not clarify this.

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

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

The description is extremely short (one sentence), which is concise but at the expense of informativeness. It does not earn its place by providing valuable context beyond the tool name.

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

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

Given the lack of output schema and the presence of pagination parameters, the description is incomplete. It does not explain what the returned labels contain, how pagination works, or how this tool differs from similar ones.

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 no descriptions for parameters, and the description does not explain the meaning or usage of 'page', 'pagesize', or even 'drug_name' beyond the obvious. With 0% schema description coverage, the description fails to compensate.

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

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

The description 'All labels mentioning a drug name' provides a general sense of the tool's function but is somewhat vague and borders on a tautology with the tool name. It does not specify what kind of labels (e.g., drug labels, regulatory labels) or the exact action (listing vs. searching).

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 offers no guidance on when to use this tool versus its siblings like 'search_drugs' or 'list_classes'. There is no mention of context, prerequisites, or 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?

With no annotations provided, the description fully covers behavioral traits: it discloses rate limits (5 per identifier per day), that it doesn't count against tool-call quota, and that feedback is read daily and influences the roadmap. 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 paragraph of ~5 sentences, front-loaded with purpose and usage, then adding behavioral notes. No unnecessary words; every sentence provides useful 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 (a feedback form) and complete schema, the description is fully complete. It covers all input, behavioral, and contextual aspects without 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?

Input schema has 100% description coverage, meeting baseline. The description adds value by elaborating on the 'type' enum with usage guidance, and on 'message' with length and specificity advice. While schema covers basics, the description enhances parameter understanding.

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

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

The description clearly states the tool is for telling the Pipeworx team about bugs, missing features, or praise. It uses specific verbs ('tell') and resource ('team'), and distinguishes itself from sibling tools by focusing on feedback rather than queries or actions.

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

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

The description explicitly lists when to use the tool (bug, feature/data_gap, praise) and what to avoid (pasting end-user prompts). It also provides context about the team's review process and roadmap impact, and mentions rate limits and that it's free.

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

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

Annotations already declare the tool as read-only (readOnlyHint), non-destructive (destructiveHint false), and open-world (openWorldHint). The description adds context about monotonicity checking, return format (ranked opportunities with direction+reasoning), and mode behavior. 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?

Description is well-structured: starts with purpose, then explains two modes with examples. It is somewhat long but every sentence adds value. Could be slightly tighter, but effectively communicates key information without redundancy.

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

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

For a tool with no output schema, the description adequately explains returns (ranked opportunities with direction+reasoning). It does not detail error handling or edge cases, but given the complexity of arbitrage detection, the description is fairly complete.

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

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

Schema coverage is 100%, so parameters are documented in schema. The description adds meaningful context: for 'event' it explains single-event mode with slug/URL; for 'topic' it explains cross-event mode with search and grouping. This goes beyond schema descriptions.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities via monotonicity violations on Polymarket. It explicitly describes two modes (event and topic), distinguishing it from sibling tools like polymarket_edges which likely focus on graph traversal rather than arbitrage detection.

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?

Usage guidelines are explicit: the description explains when to use each mode, provides examples (e.g., event slug 'when-will-bitcoin-hit-150k', topic 'Strait of Hormuz traffic returns to normal'), and clarifies why cross-event mode is needed for cases where Polymarket splits cutoffs into separate events.

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, non-destructive behavior. The description adds rich context: scanning top markets, grouping by asset, caching price history once, computing model probability, and ranking by edge magnitude. This significantly extends the behavioral understanding 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 somewhat lengthy but front-loaded with the core purpose and each sentence adds value—covering model, data sources, and output. Minor redundancy ('highest-volume' then 'top markets') prevents a perfect score.

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

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

Despite no output schema, the description fully explains the return value: top N ranked by edge magnitude with suggested trade direction. It covers scope (crypto-price bets, V1), data sources, and internal logic, making the tool self-contained 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?

Input schema coverage is 100% with clear descriptions for all three parameters. The description reinforces defaults (e.g., limit refers to 'Top N edges') but does not add new semantic meaning beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description explicitly states it scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, specifying the model and data sources. It also presents itself as answering 'what should I bet on today', which distinguishes it from sibling tools 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 clearly frames the tool for discovering betting opportunities with edge, targeting the 'what should I bet on today' use case. However, it does not explicitly state when NOT to use it or directly compare against sibling tools, leaving some ambiguity.

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

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

No annotations provided, so description carries burden. Discloses scoping by user identifier and behavior when key omitted. Lacks details on error handling (e.g., missing key) but adequate for a read operation.

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

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

Three sentences, front-loaded with purpose, then usage context, then scoping and pairing. Every sentence adds value with no fluff.

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

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

For a simple retrieval tool (1 optional param, no output schema), the description is fairly complete: purpose, usage, scoping, relations. Lacks error behavior but sufficient for agent decision-making.

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?

One parameter with full schema coverage (100%). Description reinforces schema's behavior but adds no new semantic meaning beyond what schema already provides. Baseline 3 applies.

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

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

The description clearly states the tool retrieves a saved value or lists keys, specifying verb (retrieve/list), resource (saved values/keys), and explicitly differentiates 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?

Provides clear context: 'Use to look up context the agent stored earlier' and mentions alternatives (remember, forget). Lacks explicit when-not statements but usage context is well-defined.

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

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

With no annotations provided, the description carries the full burden. It discloses the parallel fan-out to three data sources, the return format (structured changes, total_changes count, Pipeworx URIs), and the parameter format for 'since'. Although it doesn't explicitly state that the operation is read-only or discuss rate limits, the behavioral details are sufficient for an agent to understand what the tool does.

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

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

The description is moderately concise (about 100 words) and front-loaded with the purpose and examples. It wastes no sentences, though it could be slightly more compact by combining the parameter format explanation with the schema. The structure flows naturally from purpose to usage to behavior to return format.

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 3 required parameters, no nested objects, and no output schema, the description provides adequate completeness: it explains what the tool returns and the data sources used. However, it lacks details on pagination, error handling, or the exact structure of 'structured changes'. With an output schema absent, the description could be more explicit about the return object's shape.

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 already has 100% description coverage for all three parameters, but the description adds significant value: it explains the accepted formats for 'since' (ISO date and relative shorthand with examples), details that 'value' accepts ticker or CIK, and notes that 'type' is currently limited to 'company'. This goes beyond the schema's descriptions and helps the agent construct correct inputs.

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

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

The description starts with a clear question ('What's new with a company...'), provides concrete user query examples, and names the data sources (SEC, GDELT, USPTO). This makes the tool's purpose immediately obvious and distinct from sibling tools like 'recent_updates' (which likely covers broader entities or different timeframes). The verb 'fan out' and the return structure further solidify the purpose.

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

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

The description explicitly lists when to use this tool ('when a user asks...' and the example queries). It gives clear context for typical monitoring ('Use "30d" or "1m" for typical monitoring'). However, it does not explicitly state when NOT to use it or mention alternative sibling tools (e.g., 'recent_updates'), missing a full usage guideline.

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

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

No annotations exist, and the description fails to disclose behavioral details such as ordering, pagination, or data scope. Minimal transparency beyond the implied retrieval.

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 short (three words) but not overly verbose. Lacks structure; could be improved with a sentence.

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

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

For a tool with no output schema and no annotations, the description is incomplete. It does not specify what 'labels' refers to, the return format, or any contextual information needed for correct usage.

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

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

The input schema covers the single parameter 'limit' with clear constraints and default, achieving 100% coverage. The description adds no additional parameter meaning, but the schema is sufficient.

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

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

The description is a phrase 'Recently updated labels' which vaguely indicates the resource but lacks an explicit verb or context. It does not differentiate from sibling 'recent_changes'.

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 'recent_changes' or other list tools. No usage context provided.

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

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

Despite no annotations, the description discloses key behaviors: scoped by identifier, persistent vs. 24-hour retention. Lacks mention of any rate limits or error conditions, but adequate for a write operation.

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

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

Four sentences are concise and well-structured, front-loading the purpose and adding necessary details without fluff.

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

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

Given the simple key-value store, no output schema is needed. The description covers scope, persistence, and usage context fully.

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 naming conventions and examples but doesn't significantly extend beyond schema information.

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

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

The description clearly states 'Save data the agent will need to reuse later' with a specific verb and resource. It distinguishes from siblings like 'recall' and 'forget' by implication.

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

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

Explicitly instructs when to use ('when you discover something worth carrying forward') and provides examples. Also pairs with 'recall' and 'forget' for retrieval and deletion.

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

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

No annotations provided, so description carries full burden. It mentions returns IDs and citation URIs, and efficiency gains, but does not disclose error handling, ambiguity resolution, or rate limits. Adequate for a safe lookup 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?

Paragraph is moderately sized, front-loads purpose, then adds details. No wasted sentences, but 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 no annotations and no output schema, the description covers purpose, usage timing, examples, and return format. It is complete for a lookup tool, though ambiguity handling could be 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%, baseline 3. Description adds meaningful examples and explains the value parameter format (ticker, CIK, name, brand/generic) beyond the schema's short 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 it looks up canonical/official identifiers for companies or drugs, specifies the types of identifiers (CIK, ticker, RxCUI, LEI), and distinguishes itself from siblings by noting it replaces 2-3 lookup calls and should be used before other tools.

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

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

Explicitly tells when to use ('when a user mentions a name and you need the CIK...') and provides guidance to use it BEFORE other tools that need official identifiers.

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 are empty, so description must carry behavioral disclosure. It fails to mention that the tool is read-only, output format, pagination behavior, or any prerequisites.

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 sentence, no fluff, efficient and to the point.

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?

Tool has 7 parameters, no annotations, and no output schema. Description is too short, lacking info on return values, combination logic, and pagination details.

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

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

Schema has 100% coverage with descriptions for all 7 parameters. Description adds little beyond listing some parameters; 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?

Description clearly states verb 'Search', resource 'Structured Product Labels', and search criteria (name, ANDA/NDA, NDC, RxCUI), distinguishing it from siblings like 'get_drug' and 'list_labels_for_drug_name'.

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

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

No guidance on when to use this tool vs alternatives, no exclusions or when-not advice provided.

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

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

No annotations provided, so description carries full burden. It details the internal process (replaces 4–6 sequential calls) and lists return values (verdict, structured form, actual value with citation, percent delta). No mention of destructive side effects, which is appropriate.

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 packs essential information: purpose, when to use, domain, return values. Slightly dense, but front-loaded with main purpose. Could be more structured (e.g., bullet points for returns) but remains 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 no output schema, the description fully covers return values and process. Input, usage, and domain are clearly explained. No gaps remain for a tool with one parameter and straightforward behavior.

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 100% schema coverage. The description adds examples of valid claims and emphasizes natural-language input, providing context beyond the schema's simple 'description' field.

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: fact-checking natural-language claims against authoritative sources. It provides specific verb phrases (Fact-check, verify, validate, confirm/refute) and distinguishes from sibling tools by specifying the domain (company-financial claims via SEC EDGAR+XBRL).

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

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

Explicitly states when to use ('when an agent needs to check whether something a user said is true') with example phrasings. Also scopes the tool to company-financial claims for US public companies, implying limitations. Does not explicitly state what not to use for, but the domain restriction serves that purpose.

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