Npi Registry

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

No annotations are provided, so the description carries full burden. It mentions that Pipeworx picks the right tool and returns the result, but does not disclose behavioral traits like speed, limits, error handling, or ambiguity handling. Adequate for a read-only question-answering 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 of about 5 sentences, front-loaded with purpose and usage. It includes examples and a list of sources without being verbose. Well-structured and concise.

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

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

Given the tool has one parameter, no output schema, and no annotations, the description explains what it does and when to use it. It provides enough context for an AI agent to select and invoke the tool correctly, though it doesn't detail 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 coverage is 100% for the single 'question' parameter, so baseline is 3. The description adds value by providing concrete examples and hinting at the scope of questions, improving beyond the schema's minimal 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 selecting the correct data source. It provides specific verbs like 'answer' and lists many sources and examples, distinguishing it from sibling tools that are more specific.

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 the tool: 'Use when a user asks... and you don't want to figure out which Pipeworx pack/tool to call.' It implies alternatives exist but doesn't explicitly exclude cases, though the guidance is clear.

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

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

Description adds significant behavioral context beyond annotations (readOnlyHint, openWorldHint, destructiveHint): resolves market, classifies bet, fans out to packs, returns evidence packet and comparison. No contradictions.

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

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

Description is dense and front-loaded with main action, but a single paragraph could be slightly more concise. Still efficient with no wasted words.

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

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

Despite no output schema, description fully explains output: evidence packet plus market-vs-model comparison. Covers all necessary details for agent selection and use.

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

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

Schema covers 100% of parameters with descriptions. Description adds context for 'market' (slug, URL, text) but no additional meaning beyond schema. 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 the tool researches Polymarket bets by pulling Pipeworx data. It specifies input types (slug, URL, question text), resolves market, classifies bet, and fans out to packs, distinguishing it as the core demo product that outperforms separate tool discovery.

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

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

Explicitly lists use cases like 'should I bet on X?' and 'is there edge in this bet?', and implies superiority over discovering packs separately. Lacks explicit when-not-to-use alternatives, but sibling tools like ask_pipeworx are present.

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

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

No annotations provided, but the description fully compensates by disclosing data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), return format (paired data + citation URIs), and efficiency (replaces 8-15 calls). No destructive behavior is implied.

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 slightly lengthy but efficiently front-loads purpose and triggers. Every sentence adds value, though it could be slightly more concise 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?

Without an output schema, the description covers return values (paired data, citation URIs) and both entity modes thoroughly. It also mentions replacing sequential calls, providing full context for 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 descriptions, but the tool description adds substantial value: explains each 'type' enum in detail (company vs drug, what data pulled) and gives examples for 'values' (tickers/CIKs for company, drug names).

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 compares 2-5 companies or drugs side by side, with specific verbs like 'compare', 'vs', 'stack up', and lists use cases. It distinguishes from siblings like 'entity_profile' (single entity) and 'search' (general).

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 when-to-use triggers: user phrases like 'compare X and Y', 'X vs Y', 'which is bigger', or requests for tables/rankings. It implicitly excludes single-entity queries via sibling 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?

With no annotations, the description explains the return: 'Returns the top-N most relevant tools with names + descriptions'. It also hints at its role as a discovery step. However, it doesn't detail ordering or caching behavior, which is acceptable for a search 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 front-loaded and structured into clear sentences. It could be slightly more concise, but the list of domains and usage guidance justify the length. No extra 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's simplicity, the description comprehensively covers its behavior (returns top-N relevant tools with names+descriptions), when to use it (discovery step), and what to query. No output schema is needed as the return is straightforward.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for the query parameter ('analyze housing market trends', 'look up FDA drug approvals') and clarifies the limit's effect on output size.

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 tools by describing the data or task. It lists many example domains and explicitly says to call this first when many tools are available, distinguishing it from siblings like 'search' and 'entity_profile'.

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

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

The description provides explicit usage guidance: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set'. It clearly indicates the context and initial step.

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 discloses all returned data types (SEC filings, financials, patents, news, LEI, citation URIs) and limitations (only company type, no names). However, it does not explicitly state read-only nature or authentication requirements, which are implied but not stated.

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?

Dense single paragraph with front-loaded purpose. Could be slightly more structured (e.g., bullet list of returns), but remains concise and clear with no wasted words.

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

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

No output schema, so description covers all returned information. Also addresses limitations (type, value format) and usage context (replacing multiple tool calls). Fully sufficient for agent invocation.

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

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

Schema coverage is 100%, but description adds critical context: type is limited to 'company', value supports ticker or CIK with examples, and clarifies that names require resolve_entity.

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 'Get everything about a company in one call', specifying the verb and resource. It distinguishes from siblings by listing the integrated data sources (SEC, USPTO, news, GLEIF) and noting it replaces 10+ pack tool calls.

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

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

Provides explicit example queries ('tell me about X', 'give me a profile of Acme') and specifies when not to use (if only a name, use resolve_entity first). Clearly contrasts with alternatives like search or resolve_entity.

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 must cover behavioral traits. It mentions deletion but does not disclose side effects (e.g., error handling if key missing, irreversibility, authentication needs). Minimal behavioral info beyond the action.

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

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

Two sentences with clear front-loading. First sentence states purpose, second provides guidelines. No unnecessary words, but could be slightly expanded without losing conciseness.

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

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

Simple tool with 1 param, no output schema. Description pairs with siblings and covers use cases, but lacks error behavior or return value info. Adequate but not rich.

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 description for 'key' ('Memory key to delete'). Description adds 'by key' but no extra meaning beyond schema. 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 'Delete a previously stored memory by key', specifying verb and resource. Siblings include 'remember' and 'recall', and the description explicitly names them, distinguishing this tool as a deletion operation.

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

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

Provides explicit when-to-use scenarios: 'context is stale, task is done, or you want to clear sensitive data'. Lacks explicit when-not-to-use or alternative tools, but pairing with remember/recall gives 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 are absent, so description must carry behavioral context. Only states 'fetch', but does not disclose whether the operation is read-only, any side effects, authentication requirements, or error behavior. 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?

Single sentence that is front-loaded and to the point. Every word is necessary and there is 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 lookup tool, the description is very brief. It does not describe return format, what happens if NPI not found, or any pagination/error info. Given no output schema, more context is needed.

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

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

Schema coverage is 100% (parameter 'npi' described as '10-digit NPI'). The description repeats this same info, adding no new semantics. Baseline 3 for high coverage.

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

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

The description 'Fetch a provider by 10-digit NPI' clearly states the verb (fetch), resource (provider), and unique identifier (10-digit NPI). This distinguishes it from sibling tools like search or entity_profile which are broader.

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 or entity_profile. The description implies usage when you have an NPI, but does not explicitly state exclusions or 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?

Discloses rate limit (5 per identifier per day), free usage, and that feedback is read daily and affects roadmap. No annotations provided, so description carries the burden; it does well but could mention if feedback is anonymous or if there's a response.

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?

Five sentences that are front-loaded with purpose and usage. No redundant or vague statements; every sentence earns its place.

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

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

For a feedback tool with structured input (schema covers all parameters), the description provides complete guidance on when and how to use, including rate limits and what to include/exclude. No output schema needed.

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

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

Schema description coverage is 100% with good parameter descriptions. The description adds general usage context but no additional parameter-specific details beyond the schema, so baseline 3.

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: reporting bugs, feature gaps, or praise to the Pipeworx team. The verb 'Tell' and resource 'Pipeworx team' are specific, and the types (bug, feature, etc.) differentiate it from siblings like search or ask.

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

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

Provides explicit when-to-use scenarios (bug, feature/data_gap, praise, other) and what to avoid (pasting end-user prompt). Also mentions rate limits and quota, guiding 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?

The description thoroughly explains the behavioral logic (monotonicity checking), the steps taken (walking child markets, extracting dates/thresholds, sorting, reporting violations), and the return format. This goes well beyond the annotations, which already signal read-only and non-destructive behavior.

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

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

The description is well-structured with a clear topic sentence, logical explanation of the arbitrage concept, and a concise summary of the return value. Every sentence serves a purpose 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 single parameter, no output schema, and helpful annotations, the description provides all necessary context: what the tool does, how it works, what it returns, and when it applies. It is complete for its complexity.

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

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

The input schema has 100% coverage with a clear description of the 'event' parameter. The tool description adds context by explaining how the parameter is used (to walk child markets, extract dates, etc.), providing extra meaning beyond the schema.

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

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

The description clearly states the tool's purpose: find arbitrage opportunities by checking monotonicity violations within a Polymarket event. It explains the underlying concept with examples, making it highly specific and distinct from sibling tools like polymarket_edges.

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

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

The description explains how to use the tool (pass an event slug or URL) and describes the internal logic, but it does not explicitly mention when not to use it or provide comparative guidance against alternatives. However, sibling tools are not obvious substitutes, so the lack of exclusions is acceptable.

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, openWorldHint=true, and destructiveHint=false, clearly indicating a safe, read-only operation. The description adds value by explaining the underlying model (lognormal from FRED + coinpaprika), the grouping and ranking logic, and that it is V1 covering crypto-price bets. This enriches transparency beyond the structured 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 concise single paragraph of three sentences, front-loaded with the core purpose, then explains the model and process, and ends with output and purpose. There is no fluff, and every sentence earns its place.

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

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

Given no output schema, the description explains the return value (top N ranked by edge magnitude with suggested trade direction). It covers model details and data sources. However, it does not specify the exact fields in the returned list, but it is sufficient for an agent. It is fairly complete for a ranking tool with full schema and annotations.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add extra meaning beyond what the schema provides for each parameter. It mentions 'top N' aligning with limit, and 'volume window' is in schema, but no further detail is added.

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

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

The description clearly states the tool scans high-volume Polymarket markets, compares market price with Pipeworx model probability, and returns top opportunities ranked by edge. It specifies the scope (crypto-price bets, V1) and output (top N with trade direction). It is not a tautology and distinguishes from siblings like polymarket_arbitrage by focusing on edge discovery.

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

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

The description explicitly says it is built for the 'what should I bet on today' question, implying when to use it for discovering opportunities. However, it does not provide explicit exclusions or alternatives to sibling tools like polymarket_arbitrage or bet_research, though the context of 'edge' vs 'arbitrage' is implicit.

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

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

Discloses scoping by identifier (anonymous IP, hash, account ID) and behavior of omitting key (lists all). Since no annotations exist, the description adequately covers this read operation's traits.

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 filler. First sentence states core function, second provides usage examples and relationships. Every sentence earns its place.

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

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

For a simple retrieval tool with one optional parameter and no output schema, the description explains the behavior, scoping, and sibling links. Minor omission of error handling or return format, but sufficient.

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

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

Schema description coverage is 100%, so baseline is 3. The description reinforces schema without adding new detail beyond examples. No added value for the parameter.

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

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

Clearly states the verb (retrieve/list) and resource (values/keys saved via remember). Distinguishes from siblings by explicitly naming 'remember' and 'forget' and specifying that it retrieves stored context.

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 examples of when to use (look up target ticker, address, notes) and pairs with sibling tools. Lacks explicit 'when not to use' but implicitly contrasts with re-deriving information.

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 fan-out to SEC EDGAR, GDELT, USPTO; explains date formats; mentions return structure (structured changes, total_changes, URIs). Lacks mention of auth or rate limits, but these are not critical for read-only 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?

Description is compact yet comprehensive, front-loaded with purpose, and every sentence adds necessary detail 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?

For a tool with no output schema, description adequately covers return structure and key behaviors. Lacks pagination or limit details, but overall sufficient for agent to understand 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?

Schema coverage is 100%, but description adds value by explaining 'since' format with examples, clarifying 'type' enum limitation, and describing 'value' as ticker or CIK.

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

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

Description clearly states verb ('What's new') and resource ('company'), with specific examples of user queries. Distinguishes from siblings by mentioning parallel fan-out to multiple data sources.

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

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

Explicitly says when to use (user asks about updates/changes) and provides example queries. However, does not mention when not to use or suggest alternatives 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?

With no annotations, the description fully discloses key behaviors: key-value storage, scoping by identifier, persistence differences between authenticated users and anonymous sessions (24 hours), and pairing with recall/forget.

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

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

Two concise sentences front-load the purpose and usage, with no 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 simple tool with no output schema, the description covers all necessary aspects: purpose, usage context, behavioral details (scoping, persistence), and relationships with 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% and already describes key and value parameters with examples. The description reiterates these examples but adds limited additional meaning beyond the schema.

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

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

The description clearly states the tool saves data for reuse across conversations or sessions, with specific verb and resource. It distinguishes itself from siblings recall and forget by explicitly mentioning them.

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

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

Provides clear when-to-use guidance with examples of what to store (resolved ticker, target address, etc.). Mentioning pairing with recall and forget implies alternatives, but lacks explicit when-not-to-use.

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

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

Since no annotations, the description carries full burden. It explains the return of IDs and pipeworx:// URIs, but does not explicitly state read-only nature or any side effects. Still fairly transparent for a 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?

The description is reasonably concise with 4 sentences, each adding value. Slightly verbose but well-organized and front-loaded with key purpose and 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?

Despite no output schema, the description gives concrete return examples and explains the tool's role in the workflow. It could mention more sibling differentiation, but is complete enough for a 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?

With 100% schema coverage, baseline is 3. The description adds value by giving examples of valid values (ticker, CIK, name) and explaining the purpose of the output identifiers, exceeding minimal 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 resolves entities to canonical identifiers (CIK, ticker, RxCUI, LEI) with examples. It distinguishes itself from siblings like entity_profile by focusing on identifier lookup required by 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 says 'Use when...' and 'Use this BEFORE calling other tools that need official identifiers.' Also notes it replaces 2-3 lookup calls, guiding efficient 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?

No annotations are provided, so the description carries full burden. It mentions the external API (NPI Registry), which adds context, but does not disclose behavioral details such as rate limits, pagination behavior, or error handling. The description is adequate but not thorough.

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 with two sentences, no redundant information, and front-loads the core 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?

Despite high complexity (14 parameters, no required, external dependency, no output schema), the description is minimal. It fails to explain return format, error behavior, or how to combine parameters effectively. Additional context would be needed for reliable agent 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?

Schema description coverage is 71%, so many parameters are already documented. The description adds value by specifying required filter categories, but does not enhance individual parameter meaning beyond what schema descriptions provide. Baseline score 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Search providers by any combination of fields.' It identifies the resource (providers) and verb (search), and distinguishes from sibling 'get_provider' which likely retrieves a single provider.

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 provides a when-to-use guideline: 'NPI Registry requires at least one filter — supply at least name/organization/taxonomy/postal_code/state.' This tells the agent that the tool should not be used without filters, implying an alternative like get_provider for known NPIs.

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 that the tool returns a verdict (with five possible outcomes), extracted structured form, actual value with citation, and percent delta. It also mentions it replaces multiple sequential calls, giving insight into its efficiency. It does not discuss rate limits or authentication, but for a verification tool, the behavior is well-covered.

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

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

The description is concise and well-structured: it starts with action verbs, provides usage guidance, specifies scope, and lists return values. 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 tool's simplicity (one parameter, no output schema), the description is quite complete. It covers purpose, usage, scope, return format, and benefit. It does not address error handling or behavior for unsupported claims, but overall it is sufficient for an agent to understand and use the tool correctly.

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

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

Schema coverage is 100% with one parameter 'claim' having a clear description and examples. The description adds context about the supported claim types (company-financial for v1), which adds value beyond the schema. Thus, it earns a score above the baseline of 3.

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 fact-check natural-language claims against authoritative sources, specifically for company-financial claims in v1. It uses specific verbs (fact-check, verify, validate, confirm/refute) and distinguishes from siblings by focusing on claim verification, while siblings like 'ask_pipeworx' or 'compare_entities' serve different functions.

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

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

The description explicitly says 'Use when an agent needs to check whether something a user said is true' and gives examples of user prompts. It also notes the current scope limitation to company-financial claims. However, it does not explicitly mention when not to use it or provide direct alternatives among the listed siblings.

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