Clinicaltables

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

Annotations already indicate readOnly, openWorld, non-destructive. The description adds useful context: it routes to 2,520 tools, fills arguments, and returns structured data with stable citation URIs. This goes beyond annotations by explaining the mechanism and output format, though it could mention potential failure 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 well-structured with a clear imperative, list of domains, mechanism, and examples. It is somewhat lengthy but every sentence adds value. It could be slightly more concise, but overall it is efficient for the complexity of the tool.

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

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

Given the tool's complexity (meta-routing) and lack of output schema, the description provides a solid overview of inputs, mechanism, and outputs. It could be improved by explaining limitations or error handling, but it sufficiently covers what an agent needs to know for typical use.

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

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

Schema coverage is 100% with a clear description of the 'question' parameter. The description adds no additional semantics about the parameter beyond what the schema provides; the examples are more about usage than parameter definition, so a baseline of 3 is appropriate.

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

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

The description clearly states the tool answers factual questions by routing to thousands of sub-tools across many domains, distinguishing it from siblings like bet_research which are more specialized. It uses specific verbs like 'routes' and 'returns structured answer with citations', 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 explicitly says to prefer this tool over web search for factual queries and provides many examples. It implies when to use it versus siblings (siblings are more specific), but does not explicitly list alternatives or when not to use it, leaving some room for interpretation.

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

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

Annotations indicate read-only, open-world, non-destructive. Description adds detail: resolves market, classifies bet, fans out to packs, returns evidence packet and comparison. Provides behavioral insight beyond annotations.

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

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

Three sentences, front-loaded with main action. Each sentence adds value. Could be slightly more concise but effective.

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

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

Given no output schema, description thoroughly explains outputs and behavior (fan-out based on bet category). Complete for a research tool.

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

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

Schema coverage 100% with descriptions for both parameters. Description adds minimal extra meaning beyond schema (e.g., default thorough). Baseline score 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 researches a Polymarket bet by pulling relevant Pipeworx data. It specifies input types (slug, URL, question text) and outputs (evidence packet, comparison). It distinguishes itself from siblings by being the core demo product for betting 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?

Explicitly lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. Clear context but no explicit when-not-to-use 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?

Annotations indicate read-only and non-destructive behavior. The description adds context on data sources (SEC EDGAR/XBRL, FAERS) and confirms it returns paired data with citation URIs, enhancing behavioral understanding.

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 reasonably concise (around 100 words) and front-loaded with the core purpose. Slight redundancy in listing use-case phrases could be tightened, but all sentences are 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?

Despite lacking an output schema, the description sufficiently explains the return data (paired statistics and citation URIs) for both entity types, covering the tool's full functionality and usage context.

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

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

The input schema has 100% coverage, but the description adds significant value by explaining the effect of each enum ('type') and providing format examples for 'values' (tickers vs. drug names), which guides correct parameter 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 uses a specific verb 'compare' and resource 'entities (companies or drugs)', and distinguishes from siblings by emphasizing side-by-side comparisons and batching multiple queries into one call.

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

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

The description explicitly lists user phrases that trigger this tool ('compare X and Y', 'X vs Y', etc.) and implies when not to use it (single entity queries). It could be improved by directly naming alternative tools for individual lookups.

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 no additional behavioral context beyond what is provided by 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?

Extremely brief but not informative. Conciseness is achieved at the cost of missing critical details.

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 4 parameters and no output schema, the description is far too sparse. It does not cover usage, expected output, or parameter behaviors, leaving agents with significant ambiguity.

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%, but description provides no explanation for any of the four parameters (df, ef, count, terms). Agents must infer their meaning from names alone, which is insufficient.

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 'Search NLM conditions table' with a specific verb and resource. It distinguishes itself from sibling tools like 'disease_names' or 'procedures' but does not explicitly differentiate its scope.

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

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

No guidance on when to use this tool versus alternatives like 'drugs' or 'procedures'. No context on prerequisites or when not to use it.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, which the description complements by stating it returns top-N relevant tools with names and descriptions. It adds behavioral context beyond annotations without contradiction.

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

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

The description is well-structured and front-loaded with key information. While slightly verbose with the list of domains, each part earns its place. Could be slightly more concise but still effective.

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

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

Without an output schema, the description fully explains the return format (top-N tools with names and descriptions). It provides examples, usage guidance, and covers all necessary context for a simple 2-parameter tool.

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

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

Schema coverage is 100% with clear descriptions for both parameters (query and limit). The tool's description reinforces parameter usage (e.g., 'top-N' implies limit) but does not add significant new semantics beyond the schema.

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

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

The description clearly states the tool's purpose as finding tools by describing data or task. It lists specific domains (SEC filings, financials, etc.) and distinguishes it from siblings by advising to call it first when unsure which tool to use.

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

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

The description explicitly advises using this tool when browsing, searching, or discovering what tools exist, and to call it first when many tools are available. It does not explicitly exclude alternatives but provides clear context for use.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds no behavioral details, such as result format, pagination, or API limits, beyond what annotations provide.

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

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

The description is very concise (5 words) but at the cost of completeness. While brevity is valued, this description omits critical information, making it under-specified rather than efficiently concise.

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

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

Given the absence of an output schema, no parameter descriptions, and no usage guidance, the description is insufficient for an agent to correctly invoke the tool. It fails to provide necessary context for a 4-parameter search tool.

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

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

With 0% schema description coverage, the description carries the full burden of explaining parameters. It mentions none of the four parameters (df, ef, count, terms), leaving the agent without guidance on how to use them.

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 (Search) and the resource (NLM disease_names table). However, it does not differentiate this tool from sibling search tools like conditions, drugs, or procedures, which likely search different tables.

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

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

The description provides no guidance on when to use this tool versus alternatives, nor any prerequisites or context for appropriate 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 already indicate readOnlyHint=true and destructiveHint=false. The description adds no behavioral context beyond a generic 'Search'. It does not disclose traits like result limits, pagination, or data sources beyond the NLM table name.

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 single-sentence description is concise but lacks structure and critical information. It is under-specified rather than efficiently composed, as it omits parameter details and behavioral guidance.

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

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

Without an output schema, the description should explain return values or behavior. It does not. Given four parameters and no parameter guidance, the description is incomplete for effective tool 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?

The input schema has four parameters with 0% description coverage. The description adds no explanation for any parameter (df, ef, count, terms), failing to provide meaning beyond what the schema structure implies. Required parameter 'terms' is not clarified.

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

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

Description clearly states the action ('Search') and the specific resource ('NLM drugs table'), which effectively conveys the tool's purpose. However, it does not differentiate from sibling search tools like 'conditions' or 'disease_names', so it falls short of a 5.

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 is provided on when to use this tool versus alternatives. The description does not mention contexts, prerequisites, or exclusions, leaving the agent without direction for selecting it among siblings.

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

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

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true. Description adds behavioral context by detailing the return data types (SEC filings, financials, patents, news, LEI) and citation URIs, going 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?

Single paragraph is concise and well-structured, starting with purpose, then use cases, return contents, and input format. Could be slightly more structured (e.g., bullet points) but remains efficient.

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

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

Despite no output schema, description adequately explains return values (filings, financials, patents, news, LEI) and includes input constraints. Sibling tool context (like resolve_entity) is accounted for, making it complete for a profile tool.

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

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

Schema coverage is 100%, baseline 3. Description adds significant value by explaining that type is only 'company' (with future plans), value accepts ticker or zero-padded CIK, and clarifying that names are unsupported and to use resolve_entity first.

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

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

Description clearly states verb 'Get everything about a company' and resource 'company profile'. It distinguishes from sibling tools by noting it aggregates data from multiple sources (SEC, USPTO, news, GLEIF) rather than calling individual pack tools.

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

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

Explicitly lists when to use (queries like 'tell me about X', 'profile', 'research') and when not to (if only have a name, use resolve_entity first). Implicitly suggests alternatives but could be more explicit about not using for single-aspect queries.

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

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

Annotations show destructiveHint=true, description adds context about clearing sensitive data. No contradiction.

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

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

Two sentences, no fluff, front-loaded with action then guidelines.

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?

Sufficient for a simple tool with one required parameter and no output schema. Minor gap: could explicitly state permanence, but destructive hint covers it.

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 100% and parameter documented. Description doesn't add extra beyond schema.

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

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

States 'Delete a previously stored memory by key.' Clear verb+resource, distinguishes from siblings 'remember' and 'recall'.

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 when to use (stale context, task done, clear sensitive data) and mentions pairing with remember/recall. No when-not, but clear enough.

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, and destructiveHint=false, so the agent can infer safe, read-only behavior. The description adds no additional behavioral context beyond the annotations, which is adequate but not enhanced.

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

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

While the description is short, it is under-specified rather than concise. At only four words, it fails to convey sufficient information about the tool's parameters, behavior, or usage. Effective conciseness should include all essential details without 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 tool has 4 parameters, no output schema, and no parameter descriptions, the brief description is far from complete. It lacks information on parameter semantics, return format, and any constraints, leaving the agent unable to use the tool correctly.

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

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

Schema description coverage is 0%, and the description does not explain any of the four parameters (df, ef, count, terms). The agent has no insight into what these parameters mean or how to use them, making this dimension severely lacking.

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 'Search NLM icd10cm table' clearly identifies the action (Search) and the resource (NLM icd10cm table). It distinguishes from siblings like icd9cm and loinc by specifying a particular medical coding system. However, it could be improved by noting that it searches for codes or descriptions.

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 usage guidelines are provided. With siblings like icd9cm and loinc, the description should indicate when to use this tool versus alternatives. The current text gives no context on the appropriate use case.

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, and destructiveHint=false, but the description adds no additional behavioral context (e.g., rate limits, pagination, or result format).

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, which is concise but lacks substance. It provides minimal information, making it insufficiently 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?

Given the 4 parameters with no schema descriptions and no output schema, the description entirely fails to provide a complete picture. It does not cover return values, parameter usage, or behavioral nuances.

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 fails to explain any of the 4 parameters (df, ef, count, terms). The agent receives no guidance on how to use them.

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 a specific verb ('Search') and resource ('NLM icd9cm table'), making the purpose clear. The name distinguishes it from the sibling tool 'icd10cm' without explicit differentiation in the description.

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 'icd10cm'. There is no mention of 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?

Description adds no behavioral context beyond what annotations already declare. Annotations confirm read-only and non-destructive behavior, but the description fails to mention response nature, pagination, or any 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 extremely short (one sentence), which is concise but lacks structure. It does not waste words but also omits essential details needed for correct usage.

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

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

Given 0% schema coverage, no output schema, and minimal description, the tool definition is severely incomplete. An agent cannot reliably use this tool without additional knowledge about parameter semantics and return format.

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

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

Schema description coverage is 0%, and the description provides no explanation for parameters (df, ef, count, terms). The agent cannot infer the meaning or expected format of these 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?

Description clearly states 'Search NLM loinc table,' specifying the action (search) and resource (NLM loinc table). The tool name 'loinc' further identifies it as related to LOINC codes, but does not explicitly distinguish from other NLM search tools among 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?

No guidance is provided on when to use this tool versus alternatives like icd10cm or drugs. There is no indication of prerequisites or when it should not be used.

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, and destructiveHint=false. The description 'Search' aligns with these, but adds no extra behavioral context beyond what annotations already convey.

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 too short and lacks necessary detail. Conciseness should not sacrifice completeness; here it results in under-specification.

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 and four undocumented parameters, the description fails to provide sufficient context for correct tool invocation.

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

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

Schema description coverage is 0%, and the description does not explain any of the four parameters (terms, df, ef, count). Agents cannot infer parameter meanings from the text.

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 'Search NLM npi_individual table', which clearly indicates the tool searches a specific database table. However, it does not differentiate from the sibling 'npi_organization' tool.

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

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

No guidance is provided on when to use this tool vs alternatives. There are no prerequisites, use cases, or exclusions mentioned.

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, openWorldHint=true, and destructiveHint=false. The description adds no behavioral detail beyond 'Search', which is consistent with annotations. However, it does not clarify pagination, rate limits, or other behavior beyond the annotation cues.

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—a single sentence. It is front-loaded and contains no fluff, but it may be too minimal for effective use.

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

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

Given the tool has four parameters with no schema descriptions, no output schema, and a very brief description, the context is insufficient. An agent would lack understanding of search behavior, result format, and parameter purposes.

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 of the four parameters ('df', 'ef', 'count', 'terms'). The agent has no semantic understanding of these inputs from the description or 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 ('Search') and the resource ('NLM npi_organization table'), but it does not differentiate from sibling tools like 'npi_individual', which likely perform similar searches on different tables.

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 is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, limitations, or scenarios where it 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 (readOnlyHint=false, destructiveHint=false) are consistent. Description adds transparency by stating rate limits (5 per identifier per day), free usage, and that it doesn't count against tool-call quota. 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?

Description is a single paragraph of 5 sentences, front-loading the purpose. Each sentence adds value. Could be slightly more compact but is effective and well-organized.

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 schema covers all parameters, and there is no output schema, the description adequately explains usage, rate limits, and guidance. It covers all necessary context for an AI agent to use the tool correctly.

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

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

Input schema has 100% description coverage, so the baseline is 3. The description reinforces the schema but does not add new meaning beyond 'describe in terms of Pipeworx tools/packs' and 'don't paste user prompt,' which is marginal improvement.

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

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

Description clearly states the tool is for sending feedback (bug, feature, data_gap, praise) and distinguishes itself from sibling tools by specifying use cases like 'when a tool returns wrong/stale data' or 'when a tool you wish existed isn't in the catalog'.

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

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

Explicitly tells when to use each feedback type (bug, feature, etc.), instructs to describe in terms of tools/packs, and mentions rate limits and quota. Provides complete guidance for correct invocation.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is clear. The description adds behavioral context about the two modes and the monotonicity check, which goes beyond annotations. No contradictions are present, and the description does not discuss auth or rate limits, which are not critical given the read-only nature.

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

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

The description is well-structured and front-loaded with the main purpose. It uses a clear bullet-like format for the two modes, and every sentence adds value (e.g., examples, mode rationale). There is no unnecessary repetition or fluff.

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

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

Given the lack of an output schema, the description adequately outlines the return format ('ranked opportunities with suggested trade direction + reasoning'). It covers the two modes and their use cases, but could be slightly more explicit about mutual exclusivity of event and topic parameters. Overall, it is sufficiently complete for an agent to use effectively.

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

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

Schema description coverage is 100%, with each parameter having a concise description. The tool description adds extra context beyond the schema, such as examples of slugs and topics, and explains the cross-event mode's rationale, enhancing the agent's understanding of parameter 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 the tool finds arbitrage opportunities via monotonicity violations on Polymarket, detailing two distinct modes ('event' and 'topic') with examples. It differentiates itself from sibling tools like 'polymarket_edges' and 'bet_research' by specifying its unique purpose: cross-market monotonicity checking for arbitrage.

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

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

The description provides clear guidance on when to use each mode: 'event' for a single event slug and 'topic' for cross-event searches, explaining why the topic mode is necessary for capturing orders across separate events. It does not explicitly mention when not to use the tool or alternatives, but the context is sufficient for an agent to choose appropriately.

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 and non-destructive, and the description reinforces that with detailed model info (lognormal from FRED, coinpaprika), process (groups by asset, fetches price once), and output (ranked edges with direction). No contradictions; adds significant context beyond annotations.

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

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

Well-structured with five sentences covering purpose, model, process, output, and use case. Every sentence adds value, but slightly verbose; could be trimmed without losing clarity.

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

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

Covers the algorithm (V1, crypto-price bets, model), process, and output (ranked edges with direction). No output schema, but description adequately explains return. Minor gap: does not specify return fields or data format beyond ranking and direction.

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% with clear parameter descriptions. The description only reiterates top N, volume window, and minimum edge without adding new meaning. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the verb 'scan' and resource 'Polymarket markets', and specifies the unique purpose: finding where Pipeworx data disagrees with market price. It distinguishes from siblings like polymarket_arbitrage by focusing on edge discovery from model vs market, not arbitrage.

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

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

Explicitly says it's built for 'what should I bet on today' and saves users from manual paging. Provides clear context for when to use, but does not explicitly list alternatives or when not to use among siblings like bet_research.

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

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

Annotations already indicate readOnlyHint, openWorldHint, and destructiveHint, so the safety profile is clear. However, the description adds no behavioral context beyond 'Search', missing details like result format, pagination, or any 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?

Extremely short (4 words), but this is under-specification rather than concise helpfulness. It sacrifices necessary detail for brevity.

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 (4 parameters, no output schema, no parameter descriptions), the description is severely incomplete. It lacks parameter details, usage examples, and behavioral notes, leaving the agent with minimal 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 0%, and the description does not explain any parameters (e.g., 'terms' is required, 'df' and 'ef' likely denote date filters, 'count' limits results). The description provides no help in understanding parameter semantics.

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

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

The description specifies 'Search NLM procedures table', which is a clear verb+resource combination. It distinguishes from sibling search tools like 'conditions' or 'drugs' by naming the specific table, but could be improved by indicating what kind of procedures are included.

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 'conditions' or 'diseases'. No exclusions or context about search behavior (e.g., whether it's full-text or exact match).

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 behavioral context: scoped to identifier, non-destructive retrieval, and the ability to list keys. No contradictions and adds 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?

Two well-structured sentences, front-loaded with the primary action. Minor length in first sentence, but no extraneous information.

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

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

For a simple retrieval tool with one optional parameter and annotations covering safety, the description fully explains behavior, scope, and companion tools. No output schema needed as return values are implied.

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 covers 100% of parameters with a clear description for 'key'. The description reinforces the omission behavior and adds scoping context, going beyond the schema's 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 explicitly states the tool retrieves a value saved via remember or lists all keys when omitted. It distinguishes from siblings by mentioning remember and forget, providing clear purpose and resource identification.

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

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

The description explains when to use the tool (look up stored context, list all keys) and mentions pairing with remember/forget. While not providing explicit when-not alternatives, the guidance is clear and implies exclusion of other tools.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that the tool fans out to SEC EDGAR, GDELT, and USPTO in parallel, and returns structured changes with citation URIs. This provides useful context beyond the annotations.

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

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

The description is a single, well-structured paragraph that front-loads the purpose and provides concrete examples, usage context, and return format without wasted words. Every sentence adds value.

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

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

For a 3-param tool with 100% schema coverage and no output schema, the description adequately describes the return format (structured changes, total_changes count, pipeworx:// URIs). It could mention error behavior or rate limits, but the openWorldHint annotation suggests the agent should handle openness. Overall sufficiently 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 meaning by explaining the 'since' parameter format (ISO date or relative shorthand like '7d', '30d', '3m', '1y') and clarifying that 'value' can be a ticker or CIK. It also notes that 'type' currently only supports 'company'.

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

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

The description uses a specific verb ('what's new with a company') and clearly identifies the resource (company changes) and time window. It provides multiple example query patterns, distinguishing it from sibling tools like 'entity_profile' or 'compare_entities' 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 gives explicit example queries and contexts ('what's happening with X?', 'brief me on what happened with Microsoft this quarter'), and explains the fan-out to multiple data sources. While it doesn't explicitly list exclusions, the examples clearly indicate when to use this 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?

Annotations already indicate a write operation (readOnlyHint=false). The description adds valuable behavioral context: key-value scoping by identifier, persistence differences between authenticated and anonymous sessions (24 hours). 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 sentences), front-loaded with purpose, then usage guidance, then behavioral details. 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?

For a simple tool with two string params and no output schema, the description covers all needed context: purpose, when to use, data persistence behavior, and relation to sibling tools. It is fully complete.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds meaning beyond schema by explaining the key-value pair concept, scoping by identifier, and giving usage examples (e.g., 'subject_property', 'target_ticker').

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: 'Save data the agent will need to reuse later'. It uses specific verbs like 'save', 'reuse', and 'carry forward', and distinguishes itself from siblings by mentioning '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?

The description provides explicit usage guidance: 'Use when you discover something worth carrying forward' with concrete examples (resolved ticker, target address, etc.). It also mentions pairings with recall and forget, though it doesn't explicitly state when not to use it.

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

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

With annotations already indicating read-only and non-destructive behavior, the description adds that the tool returns IDs and pipeworx:// citation URIs. It explains the lookup process and replacement of multiple calls, enhancing transparency without contradiction.

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

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

The description is concise, with four sentences that front-load the main action. Every sentence adds unique value—purpose, use case, examples, and behavioral note—without redundancy or fluff.

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

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

Given the tool has no output schema, the description adequately notes that returns include IDs and citation URIs. It covers entity types, required parameters, and usage context, making it sufficiently complete for a simple lookup tool.

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

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

Schema covers 100% of parameters. The description adds meaning by explaining the value parameter format for company (ticker, CIK, or name) and drug (brand or generic name), and the type enum, helping agents construct valid 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 clearly states the tool's purpose: looking up canonical/official identifiers for companies or drugs. It specifies the types of identifiers (CIK, ticker, RxCUI, LEI) and provides concrete examples, making it distinct from sibling tools like entity_profile or conditions.

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

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

The description explicitly advises using this tool before others that need official identifiers, and when a user mentions a name requiring an ID. It lacks explicit alternative names but implies it replaces multiple lookups, providing clear context.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the description adds minimal value. It only states 'Search', which is consistent with read-only behavior, but does not disclose details like return format or search behavior (e.g., exact match, fuzzy). With annotations covering the safety profile, a score of 3 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 short sentence, which is concise but at the cost of being under-specifying. It does not front-load necessary information like parameter usage or results, making it inefficient for an AI agent to use reliably.

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, zero parameter descriptions in both schema and description, and no usage context, the tool is severely under-documented. The agent lacks essential information to correctly invoke and interpret results from this tool.

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

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

The input schema has 4 parameters (df, ef, count, terms) with no descriptions in the schema (coverage 0%). The description does not explain any parameter meanings. Only 'terms' is required but its role is unclear. This is a severe gap for proper tool invocation.

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 'Search NLM ucum table' which gives a verb and resource, but it does not explain what 'ucum' stands for or how it differs from sibling tools like icd10cm or loinc. The purpose is somewhat clear but lacks specificity to distinguish it from other medical code lookups.

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

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention any context or constraints, leaving the agent without criteria to decide between ucum and other search tools on the server.

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

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

Annotations already indicate read-only, non-destructive behavior. The description adds behavioral context: it is a composite tool replacing 4-6 sequential calls, uses specific data sources (SEC EDGAR + XBRL), and returns a structured verdict with citations. This goes beyond 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 one compact paragraph that front-loads the core purpose. Some repetition ('fact-check, verify, validate') could be tightened, but it remains efficient and focused.

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 1 parameter and no output schema, the description covers the use case, supported domain, output format (verdict types), and efficiency benefit. It lacks edge-case handling but is sufficiently complete for an agent to use it correctly.

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

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

The only parameter 'claim' has 100% schema coverage, but the description enriches it with concrete examples (e.g., Apple revenue, Microsoft profit) and states the supported claim domain. This adds meaning beyond the schema's type definition.

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 factual claims against authoritative sources. It specifies the supported domain (company-financial claims for US companies via SEC EDGAR + XBRL) and identifies the verb (fact-check, verify, validate). This is specific enough to distinguish from sibling tools.

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

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

The description provides explicit usage scenarios (e.g., 'Is it true that…?', 'Verify the claim that…') and indicates the supported claim type. It does not directly compare to alternatives among sibling tools but implies a specific niche. This is clear guidance, though lacking outright exclusions.

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