fuiwanted

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool selects data sources and fills arguments automatically, and it handles natural language questions. However, it lacks details on limitations (e.g., data scope, accuracy), error handling, or response format, which are important for a tool with no output schema.

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

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

The description is front-loaded with the core purpose, followed by operational details and examples. Every sentence adds value: the first explains the tool's function, the second describes how it works, and the third provides concrete examples. It is efficiently structured without wasted words.

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

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

Given the tool's complexity (natural language processing with automatic tool selection) and lack of annotations/output schema, the description does well by explaining the mechanism and providing examples. However, it could be more complete by mentioning potential limitations or the types of data sources available, which would help set expectations for the agent.

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

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

The schema description coverage is 100%, with the parameter 'question' well-documented. The description adds value by emphasizing 'plain English' and 'natural language,' clarifying the expected input style beyond the schema's 'question' definition. With only one parameter, this extra context is sufficient for a high score.

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

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

The description clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes itself from siblings by emphasizing natural language input versus browsing specific 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 explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives by implication (use other tools for browsing or schema-based queries) and includes specific examples like 'What is the US trade deficit with China?' to illustrate appropriate use cases.

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 full burden. It discloses the operation (comparison), data sources (SEC EDGAR, FDA), and return format (paired data + resource URIs). It does not mention permissions or side effects, which is acceptable for a read-heavy tool. A 5 would require explicit mention of limitations or error handling.

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) and front-loaded with the main purpose. Every sentence adds value: comparison scope, type-specific details, and efficiency note. No extraneous text.

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

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

Given the tool's complexity (two entity types, multiple data points), the description provides sufficient context. It explains what data is compared and mentions return format. Lacking an output schema, it still gives a good picture. A 5 would require explicit mention of error handling or data freshness.

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 listing specific data fields for each type and mentioning resource URIs in the output, which aids the agent in understanding the return beyond the schema's parameter 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 compares 2-5 entities side by side, specifies the data returned for each type (company: revenue, net income, etc.; drug: adverse-event reports, etc.), and distinguishes itself as a batch comparison tool replacing sequential calls. This differentiates it from siblings like resolve_entity or search_wanted.

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 this tool: to compare multiple entities in one call, with specific entity types and data points. It notes efficiency gains ('Replaces 8–15 sequential agent calls'). However, it does not explicitly state when not to use it or mention alternatives, which would make it a 5.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool searches a catalog, returns relevant tools, and should be called first in high-tool contexts. However, it lacks details on rate limits, authentication needs, or error handling, which are important for a search tool with no output schema.

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 appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines without redundancy. Every sentence adds value, making it concise and well-structured for quick understanding.

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 (search functionality with 2 parameters) and lack of annotations or output schema, the description is mostly complete. It covers purpose, usage context, and behavioral aspects, but could improve by mentioning response format or limitations. It adequately compensates for the missing output schema by specifying what is returned.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('query' and 'limit') thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips for the query parameter. Baseline 3 is appropriate as the schema handles 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 tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resources ('tool catalog'), and distinguishes it from siblings by emphasizing its role in finding tools among 500+ options. It explicitly mentions returning 'most relevant tools with names and descriptions,' making the outcome transparent.

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

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

The description provides explicit guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a specific condition (500+ tools) and priority (FIRST). It implicitly distinguishes from siblings by focusing on tool discovery rather than direct data searches like 'get_wanted_person' or 'search_wanted'.

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, description carries full burden. It states the tool returns pipeworx:// URIs and lists data sources. It mentions speed limitation for federal contracts but does not detail potential rate limits or output format limitations.

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 concise (~80 words), front-loaded with purpose, and structured logically: summary, data list, output note, and alternative guidance. Every sentence adds value.

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

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

Given moderate complexity and no output schema, the description provides sufficient context for input and usage. It lacks details on the structure of the returned profile, but the note about citation URIs and bundled data compensates somewhat.

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 workflow context by noting that type only supports 'company' and that value requires ticker/CIK, with a pointer to resolve_entity for names. This goes beyond the schema to prevent misuse.

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

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

Description clearly states it retrieves a full entity profile across multiple packs with specific data sources listed for type=company. It distinguishes from siblings by naming usa_recipient_profile and resolve_entity as alternatives.

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 this tool (for comprehensive entity profile instead of 10-15 sequential calls) and when to use alternatives (usa_recipient_profile for federal contracts, resolve_entity if only a name is available).

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

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

With no annotations, the description carries full burden. It discloses the destructive nature ('Delete'), but lacks critical behavioral details: whether deletion is permanent or reversible, what happens if the key doesn't exist (error vs. silent failure), permission requirements, or rate limits. This is inadequate for a mutation 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, efficient sentence with zero wasted words. It's front-loaded with the core action ('Delete'), making it immediately clear. Every word 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 destructive tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'stored memory', the deletion's permanence, error handling, or return values. Given the complexity of a delete operation, 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 description coverage is 100%, with the single parameter 'key' documented as 'Memory key to delete'. The description adds no additional meaning beyond this, merely restating 'by key'. Baseline 3 is appropriate when 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 action ('Delete') and resource ('a stored memory by key'), making the purpose unambiguous. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' strongly implies a destructive operation distinct from retrieval or creation.

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 doesn't mention prerequisites (e.g., needing an existing memory key), exclusions, or relationships with siblings like 'recall' (which likely retrieves memories) or 'remember' (which likely creates them).

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

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

No annotations are provided, so the description carries the full burden. It states the tool retrieves 'full details', which implies a read-only operation, but does not disclose behavioral traits like error handling, rate limits, or authentication needs. The description is minimal but not contradictory.

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, efficient sentence that front-loads the purpose without unnecessary words. Every part of the sentence contributes to understanding the tool's function.

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

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

Given the tool's low complexity (one parameter, no output schema), the description is complete enough for basic use. However, without annotations or output schema, it lacks details on return values or potential errors, which could be helpful for an agent.

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

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

The schema description coverage is 100%, with the parameter 'uid' fully documented in the schema. The description adds no additional meaning beyond the schema, such as format examples or constraints, so it meets the baseline for high schema coverage.

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

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

The description clearly states the verb ('Get') and resource ('full details for a specific FBI Wanted person'), specifying the exact operation. It distinguishes from the sibling tool 'search_wanted' by focusing on retrieval of a single person by UID rather than searching.

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

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

The description implies usage context by specifying 'by their UID', indicating this tool is for retrieving details when the UID is known. However, it does not explicitly state when not to use it or name alternatives, though the sibling tool name suggests a search function.

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

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

With no annotations provided, the description carries the full burden. It discloses the rate limit (5 per identifier per day) and a privacy guideline (omit end-user prompt). The behavioral impact is minimal (sending feedback), and these disclosures are sufficient for safe invocation.

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: three sentences cover purpose, usage categories, a privacy instruction, and rate limit. Every sentence serves a distinct purpose 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 feedback tool, the description covers the essential aspects: what it does, when to use, rate limiting, and content guidelines. No output schema exists, but the return value is likely trivial. The description is complete enough to enable confident 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%, so baseline is 3. The description adds value by instructing the agent to describe what was tried in terms of Pipeworx tools/data, which enhances the message parameter beyond the schema's length constraint. It also reinforces the enum meanings without repeating them verbatim.

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 'Send feedback' and the resource 'Pipeworx team'. It lists specific use cases (bug reports, feature requests, missing data, praise), effectively distinguishing it from sibling tools like ask_pipeworx or discover_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 contexts for use ('bug reports, feature requests, missing data, or praise') and a critical constraint ('do not include the end-user's prompt verbatim'). It also mentions the rate limit (5 per day). While it doesn't directly name alternatives, the scope is clear enough that an agent can infer when to use this tool versus others.

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 full burden. It discloses key behavioral traits: the tool can retrieve by key or list all memories, works across sessions, and accesses 'previously stored' data. However, it doesn't mention error handling (e.g., what happens if key doesn't exist), performance characteristics, or whether listing all memories has pagination/limits.

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 perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality, the second provides usage context. No wasted words, front-loaded with the most important information.

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

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

Given no annotations and no output schema, the description provides adequate but incomplete context. It covers the basic functionality and usage scenario, but doesn't describe return values, error conditions, or performance limits. For a memory retrieval tool that might return complex data, more output information would be helpful.

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

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

The schema has 100% description coverage, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the dual functionality (retrieve vs list) and the semantic meaning of omitting the key parameter. It clarifies that 'omit key' triggers listing behavior, which isn't obvious from the schema alone.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes between retrieval by key and listing all memories, but doesn't explicitly differentiate from sibling tools like 'remember' or 'forget' beyond mentioning 'context you saved earlier'.

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

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

The description provides clear context for when to use the tool ('to retrieve context you saved earlier in the session or in previous sessions') and includes operational guidance ('omit key to list all keys'). However, it doesn't explicitly state when NOT to use this tool or mention alternatives among the sibling tools.

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

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

Despite no annotations, the description discloses key behaviors: parallel fan-out to multiple sources, supported date formats (ISO and relative), and return fields (structured changes, count, URIs). It also notes the limitation to 'company' type. Minor omission: no mention of error handling or rate limits, but overall good 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?

The description is concise, with no wasted words. The first sentence states the core purpose, followed by details on fan-out, date formats, and output. It is well-structured and easy to parse.

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

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

The description covers input, process, and output sufficiently. It mentions return fields and URI links, compensating for the missing output schema. Could include more on pagination or error states, but given the tool's design, it is reasonably 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 value by explaining date format examples ('7d', '30d', '3m', '1y'), which the schema describes abstractly, and clarifies that 'value' can be ticker or CIK. This aids parameter understanding beyond the schema alone.

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

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

The description clearly states the tool's purpose: finding what's new about an entity since a given time. It specifies the entity type ('company'), lists the data sources (SEC EDGAR, GDELT, USPTO), and describes the output, making it distinct from siblings like 'entity_profile' or 'ask_pipeworx'.

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 usage guidance is provided: 'Use for "brief me on what happened with X" or change-monitoring workflows.' This tells the agent exactly when to invoke this tool, and the description implies it's specialized for temporal changes, not general 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool performs a write operation (storing data), specifies persistence characteristics (authenticated users get persistent memory, anonymous sessions last 24 hours), and mentions the cross-tool context capability. However, it doesn't cover potential limitations like storage size or rate limits.

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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, and the second sentence adds crucial behavioral context about persistence differences. There's zero waste or redundancy in the text.

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 write operation tool with no annotations and no output schema, the description provides good contextual completeness. It covers the tool's purpose, usage scenarios, and key behavioral traits (persistence characteristics). However, it doesn't specify what happens on success/failure or return values, which would be helpful given the lack of output schema.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema - it mentions 'key-value pair' but doesn't provide further context about parameter usage, format requirements, or constraints. Baseline 3 is appropriate when 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 tool's purpose with specific verb ('Store') and resource ('key-value pair in your session memory'), and distinguishes it from siblings by mentioning persistence across tool calls. It goes beyond the name 'remember' by explaining what kind of data can be stored and the memory scope.

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

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

The description provides explicit guidance on when to use this tool ('save intermediate findings, user preferences, or context across tool calls') and distinguishes it from alternatives by specifying persistence differences for authenticated vs. anonymous users. It gives clear context for application without misleading 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?

With no annotations provided, the description carries the full burden. It discloses that the tool is a single call, lists exact return fields (ticker, CIK, company name, resource URIs), and implies a read-only operation through the verb 'resolve'. No side effects, destructive actions, or rate limits are mentioned, but the simplicity of the operation makes this acceptable.

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, consisting of two short sentences. The first sentence defines the core purpose, and the second provides version-specific details and examples. Every word contributes value with zero 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 no output schema, the description adequately covers return values. It lists all returned fields and mentions the resource URIs for stable citation. For a simple entity resolution tool with two parameters, this is nearly complete. Minor gaps like error handling or no-match behavior are not critical for a straightforward lookup.

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

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

Schema coverage is 100%, but the description adds value beyond the schema by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and clarifying that the type enum currently only supports 'company'. This helps agents understand valid inputs better than the schema alone.

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

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

The description clearly states the tool's purpose: resolving an entity to canonical IDs in a single call. It specifies the supported type (company), acceptable input formats (ticker, CIK, name), and return values (ticker, CIK, company name, resource URIs). This makes it distinct from sibling tools which are unrelated.

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

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

The description provides clear context for usage by noting it replaces 2–3 lookup calls, implying efficiency gains. It explicitly states v1 only supports 'company' type, but does not give explicit when-not-to-use scenarios or alternatives. However, sibling tools are sufficiently different that confusion is unlikely.

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

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

No annotations are provided, so the description carries the full burden. It mentions pagination and optional filtering, which adds some behavioral context, but it doesn't disclose critical traits like rate limits, authentication needs, error handling, or the format of results (e.g., list structure, fields returned). For a search tool with no annotation coverage, this is a significant gap.

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

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

The description is two sentences, front-loaded with the core purpose and efficiently covering optional features without waste. Every sentence adds value, making it appropriately sized and well-structured.

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

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

Given the tool's moderate complexity (search with filtering/pagination), no annotations, and no output schema, the description is minimally adequate. It covers the purpose and parameters but lacks details on behavioral traits and result format, leaving gaps that could hinder an agent's ability to use it 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%, so the schema already documents both parameters ('page' and 'query') with clear descriptions. The description adds marginal value by reinforcing that 'query' can filter by 'name, crime type, etc.' and that omitting it lists all, but this doesn't go beyond what the schema provides. Baseline 3 is appropriate when 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 ('Search') and resource ('FBI Most Wanted list'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from the sibling tool 'get_wanted_person', which might be for retrieving a specific person versus searching/filtering a list.

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 implies usage by mentioning optional filtering and pagination, suggesting when to use it for broader searches. However, it lacks explicit guidance on when to use this tool versus the sibling 'get_wanted_person' (e.g., for list vs. detail views) or any exclusions, leaving some ambiguity.

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

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

No annotations provided, so description carries the burden. It discloses the scope (v1, limited to financial claims) and return fields (verdict, structured form, etc.), but lacks details on error handling, rate limits, or required permissions. The transparency 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?

Three sentences cover purpose, scope, and return value. No redundancy, front-loaded with the core action. Every sentence adds important information.

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

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

Given no output schema, the description explains the return structure (verdict, extracted form, citation, delta). It also notes the tool replaces multiple agent calls, providing context. Slightly lacking in details about verdict interpretation or edge cases, but adequate for a simple 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 sole parameter 'claim' already has a schema description (100% coverage). The tool description adds value with concrete examples ('Apple's FY2024 revenue was $400 billion'), which clarifies usage beyond the schema.

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

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

The description clearly states the tool's purpose: 'Fact-check a natural-language claim against authoritative sources.' It specifies the supported domain (company-financial claims for public US companies) and mentions the verdict types returned, distinguishing it from other tools on the server.

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 defines when to use the tool ('v1 supports company-financial claims (revenue / net income / cash for public US companies)'), providing clear domain restrictions. It doesn't explicitly exclude other claims but implies scope. It also highlights efficiency by noting it replaces multiple sequential calls.

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