Usajobs

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

No annotations are provided, so the description must convey behavioral traits. It mentions automatic source selection and argument filling but lacks details on whether the tool is read-only, error handling, latency, or authentication requirements. This is insufficient for a tool with no annotations.

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

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

The description is front-loaded with the main purpose and is well-structured. It includes a list of example questions and data sources. While every sentence is informative, it could be slightly more concise by removing redundant phrasing. Still, it is 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 the simplicity of the input (one string parameter) and the absence of output schema, the description provides a solid understanding of the tool's functionality, usage, and scope. It could mention potential limitations or what happens if the question is ambiguous, but overall it is fairly complete.

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

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

The input schema has only one parameter with a basic description. The description adds significant value by explaining the kinds of questions that can be asked and providing multiple examples. This goes beyond the schema, which only says 'Your question or request in natural language.'

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

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

The description clearly states that the tool answers natural-language questions by automatically selecting the appropriate data source. It uses specific verbs ('Answer') and resources ('natural-language question'), and distinguishes itself from sibling tools by covering a broad range of 300+ sources, making it a general-purpose query 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?

The description explicitly specifies when to use the tool (when a user asks natural-language questions and you don't want to pick a specific tool) and provides concrete examples. It does not explicitly mention when not to use it, but the context of sibling tools suggests that structured queries might be better handled elsewhere.

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 adds significant behavioral details beyond the readOnly, openWorld, and non-destructive annotations: it resolves the market, classifies the bet, fans out to relevant packs (e.g., crypto+fred+gdelt), and returns an evidence packet and comparison. No contradictions with annotations.

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

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

The description is a single paragraph of about 5 sentences, front-loading the purpose. It is informative without being excessively long. Minor redundancy could be trimmed, but overall 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?

Without an output schema, the description adequately explains the tool returns an 'evidence packet plus a simple market-vs-model comparison'. It covers the core functionality for a demo product. Could mention whether the evidence packet structure is predictable, but sufficient for 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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining the 'market' parameter can be a slug, URL, or question text, and 'depth' defaults to thorough, with 'quick' implying 2-3 sources. It gives examples of fan-out for different bet types, enhancing the schema's enum and string 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 researches a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and the output (evidence packet and comparison). It distinguishes itself from sibling tools by focusing specifically on Polymarket bets.

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 use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It does not explicitly state when not to use it or mention alternative tools, but the context is clear enough for the agent to decide.

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 the description carries full burden. It discloses data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs) and mentions return format (paired data + citation URIs). It does not explicitly state read-only nature, but the context implies no destructive actions.

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 approximately 100 words, front-loaded with the core purpose, followed by usage examples and data sources. Every sentence adds value, and the structure is logical and 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?

The description covers input, output format, and data sources. While no output schema exists, the explanation of return type (paired data + URIs) is sufficient. Missing are error scenarios or rate limits, but for a read-heavy tool with few parameters, 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 description coverage is 100% for both parameters. The description adds value beyond the schema by explaining how to format values for company vs drug and giving examples, which helps the agent construct correct inputs.

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

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

The description clearly states the tool compares 2–5 companies or drugs side by side, with specific data sources for each type. It differentiates from sibling tools like entity_profile (single entity) and search, making its purpose distinct.

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 scenarios ("compare X and Y", "X vs Y", tables/rankings) and notes it replaces 8–15 sequential calls. However, it does not explicitly state when not to use this tool or mention alternatives beyond the implied single-entity 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?

With no annotations, the description fully explains behavior: returns top-N most relevant tools with names and descriptions. It mentions the limit parameter's default and max, providing transparency about what the user receives.

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 relatively long but front-loaded with the key action. 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?

Given no output schema, the description sufficiently explains the return value (top-N tools with names and descriptions). It covers usage guidance, parameters, and purpose, making it complete for a discovery 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?

Input schema has 100% coverage, but the description adds valuable context: query expects natural language and limit has default 20 and max 50. This enhances understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It provides specific examples and distinguishes from siblings by emphasizing discovery over direct data retrieval.

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

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

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

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 returned data types and citation URIs. The tool is clearly read-only, though not explicitly stated; no destructive or side effects mentioned. Minor omission could be noting it only reads public data.

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, front-loaded with purpose. While concise, the many use cases and output list make it slightly dense. Could benefit from bullet points or clearer separation, but still 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?

No output schema, yet description thoroughly enumerates all categories of returned data (SEC filings, fundamentals, patents, news, LEI) and mentions citations. For a complex multi-source tool, this provides sufficient completeness for an agent to understand its capabilities.

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. Description adds context: explains the enum constraint for type (only 'company' currently) and gives examples for value. Also provides alternative path for name inputs via resolve_entity, 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?

The description clearly states 'Get everything about a company in one call' and enumerates specific outputs (SEC filings, fundamentals, patents, news, LEI). It distinguishes from sibling tools like resolve_entity and compare_entities by highlighting its comprehensive nature.

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 triggers: user queries like 'tell me about X', 'give me a profile', etc. Also specifies when not to use it: when only a name is available, recommends resolve_entity first. Provides input format examples (ticker or CIK) and warns against unsupported name inputs.

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?

Clearly identifies delete/destructive behavior. No annotations provided, so description carries full burden. Lacks mention of irreversibility or confirmation, but sufficient for a simple key deletion 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?

Two efficient sentences with no fluff. Action stated first, then usage context. 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 low-complexity tool (one param, no output schema), the description covers purpose, usage, and basic behavior adequately. Could mention return value or confirmation, but not critical.

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

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

Schema coverage is 100% with a single parameter 'key' described as 'Memory key to delete'. Description adds no extra meaning beyond what schema provides, meeting baseline.

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

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

The description clearly states the action 'Delete a previously stored memory by key' with specific verb and resource. It distinguishes from sibling tools 'remember' and 'recall' by mentioning pairing.

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

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

Explicitly states when to use: when context is stale, task done, or clearing sensitive data. Also hints at alternatives by mentioning pairing with remember and recall.

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

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

Annotations are empty, so the description must carry the burden. 'Fetch' implies a read operation but does not disclose any behavioral traits such as error handling, authentication needs, or what the response contains. Minimal value added.

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?

A single, concise sentence with no wasted words. Efficiently communicates the tool's 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?

For a simple fetch tool with one parameter and no output schema, the description is somewhat adequate but does not explain return format or error cases. Could improve by stating it returns the full announcement details or what happens on not found.

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 parameter already described as a USAJOBS announcement number with an example. The tool description repeats this but adds no further meaning, so baseline 3 is appropriate.

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

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

The description clearly states the tool fetches a single announcement by announcement number, using the specific verb 'Fetch' and specifying the resource 'announcement'. It distinguishes itself from siblings like 'search' which likely handles multiple results.

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 other list tools. The description only states what it does, with no when-not or contextual cues.

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, and description gives no behavioral details such as whether the tool is read-only, requires authentication, or has rate limits. 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?

Extremely concise with one phrase, front-loaded. However, it may be too brief to be fully helpful; every word earns its place but could include more context.

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 parameters or output schema, the description covers the data type but does not specify format or usage details. Adequate but not 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?

No parameters exist, so baseline is 4. Description adds meaning by indicating the returned data is 'Federal agency reference codes', which clarifies output content.

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 'Federal agency reference codes' vaguely indicates purpose. It implies listing codes but lacks a verb like 'list' or 'get', making it less explicit. Differentiates from siblings by name, but purpose is not clearly stated as an action.

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. Description does not mention context, prerequisites, or exclusions, leaving the agent without direction.

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 provides no behavioral details such as whether the list is complete, paginated, or requires authentication. It is a bare noun phrase without any operational context.

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 no wasted words, perfectly sized for a simple no-parameter 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 no parameters and a simple purpose, the description is adequate but could be improved by hinting at the output format (e.g., array of objects with code and title). It leaves ambiguity about what exactly 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?

No parameters exist (baseline 4), so the description does not need to add meaning. The schema has 100% coverage by having no properties.

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 that the tool returns occupational series codes with an example, distinguishing it from sibling tools like list_agencies or list_pay_grades. However, it does not explicitly use a verb like 'list' but the name and context make it clear.

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 (e.g., search, validate_claim) or any prerequisites. The description implies it's for retrieving all codes, but no explicit usage context.

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

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

No annotations are provided, so the description must carry the full burden. It does not disclose whether the tool is read-only, any side effects, or other behavioral traits beyond listing codes.

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?

A single concise sentence that is front-loaded with the core purpose. However, it may be slightly too brief for a complete description.

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 parameters, no output schema, and no annotations, the description is minimal. It lacks context on how the list is ordered or what the codes are used for, but is enough for a simple enumeration 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?

Input schema has 0 parameters, so schema description coverage is 100% (vacuous). The description does not need to explain parameters; baseline for 0 params is 4. It correctly states the output content.

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 lists pay grade codes with examples (GS, GG, ES). It is a specific verb ('list') and resource ('pay grades'), distinguishing it from sibling tools like list_agencies or list_occupational_series.

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. Sibling tools like list_agencies and list_occupational_series are related but no differentiation is provided.

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

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

With no annotations, the description fully discloses behavioral traits: it is rate-limited (5 per identifier per day), free, and does not count against tool-call quota. It also explains how feedback is used (digests read daily, affects roadmap). No contradictions.

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

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

The description is 5 sentences, each providing essential information: purpose, usage scenarios, content guidelines, impact, and constraints. It is well-organized and front-loaded with 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?

Given the tool's simplicity (feedback submission) and no output schema, the description is quite complete. It covers when to use, what to include, behavioral constraints, and impact. Could optionally include an example, but not missing crucial details.

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

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

Schema description coverage is 100%, but the description adds meaningful guidance: it repeats enum explanations with helpful examples (e.g., 'bug = something broke or returned wrong data'), advises on message specificity and length, and instructs not to paste end-user prompts. This adds value beyond the schema.

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

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

The description uses specific action words like 'tell the Pipeworx team something is broken, missing, or needs to exist' and clearly distinguishes the tool's purpose from sibling tools, which are all data retrieval or memory 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 lists scenarios (bug, feature, data_gap, praise, other) and provides guidance on how to describe issues (in terms of tools/packs, not end-user prompt). It also mentions rate limit and that it's free, but doesn't explicitly exclude cases where feedback is inappropriate.

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 explains the underlying logic (monotonicity rule) and the return structure (list of market pairs with gap and suggested trade). This adds value beyond annotations which already indicate read-only, 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 explanation, though slightly verbose. Each sentence contributes to understanding. Could be tightened slightly, but overall good.

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 one-parameter tool with no output schema, the description is comprehensive: it explains the input, the algorithm, the expected result, and the underlying economic principle. No gaps remain.

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 'event' is described in the schema as a slug or URL. The tool description reinforces this and provides example. Schema coverage is 100%, so the description adds marginal but helpful context.

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

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

The description clearly states the tool's purpose: finding arbitrage opportunities via monotonicity violations. It explains the concept, when it applies (same event with multiple date/threshold markets), and what results to expect, distinguishing it 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 clear guidance on when to use the tool (detecting arbitrage in Polymarket events) and what inputs are needed (event slug or URL). It doesn't explicitly list alternatives but context implies this tool is specialized for monotonicity checks.

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, so agent knows it's safe. Description adds behavioral context: scans top markets groups by asset, fetches price history once for efficiency, ranks by edge magnitude with suggested direction. This goes beyond annotations, though it could disclose dependency on external data freshness.

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 front-loaded with main purpose and remains informative throughout. Every sentence adds value: main purpose, technical approach, output format, use case. Could be slightly more concise but very 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 no output schema, description explains the output (top N ranked with suggested direction) and process. However, it does not specify exact fields or structure of returned edges, which would help an agent parse results. Completeness is adequate but not comprehensive.

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 does not add meaning beyond schema; parameter details like defaults and max are already in schema. Thus, no extra value beyond baseline.

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

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

The description clearly states it scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price, using specific verb 'scan' and resource 'Polymarket markets'. It distinguishes from siblings like polymarket_arbitrage and bet_research by focusing on edge discovery for crypto-price bets, making 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 targets the 'what should I bet on today' use case, indicating when to use the tool. While it does not explicitly mention when not to use or list alternatives, the context implies it's for opportunity discovery, which is sufficient guidance.

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

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

With no annotations, the description carries the full burden. It discloses scoping (identifier-based isolation) and the ability to list all keys. It does not explicitly state read-only behavior, but the action 'retrieve' implies it, and the pairing with forget suggests state changes are separate.

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 four sentences, front-loaded with the primary action and purpose. It is concise but could be slightly tighter; however, 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 one optional parameter and no output schema, the description is complete: it explains retrieval, listing, scoping, and integration with sibling tools. No gaps remain.

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 the single parameter 100%, and the description adds valuable context: omitting the key lists all saved keys. This clarifies behavior beyond the schema.

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

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

The description clearly states the tool retrieves a value saved via remember or lists all keys if key is omitted. It uses specific verbs and resource references, and distinguishes from siblings like remember and forget.

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

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

The description explains when to use the tool (to look up stored context) and pairs it with remember and forget for a complete workflow. It lacks explicit 'when not to use' guidance but is otherwise 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?

With no annotations, the description carries the full burden. It discloses parallel fan-out to three sources, return structure (structured changes, count, citations), and accepted date formats. It could mention error handling or authorization, but the provided details are robust.

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 (~100 words), front-loaded with the core purpose, and eliminates unnecessary fluff. Every sentence adds value without repetition.

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 explains return format. It covers data sources, parameter options, and typical usage. Could mention error behavior or result limits, but it remains sufficient for a tool with three parameters.

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: for 'since' it explains ISO vs relative shorthand and recommends '30d' or '1m'; for 'value' it gives examples; for 'type' it notes only 'company' supported.

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

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

The description clearly states the tool's purpose: retrieving recent changes for a company from multiple sources. It provides specific verb phrases ('What's new', 'any updates') and distinguishes itself from sibling tools like search or 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 gives concrete examples of when to use the tool ('what's happening with X?', 'monitoring for changes'). It does not explicitly state when not to use it or mention alternatives, but the examples effectively guide 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 provided, so description fully discloses storage scope, persistence behavior, and pairing with recall/forget. Adds value beyond schema by explaining session versus persistent memory.

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

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

Four sentences with no waste: first sentence states purpose, second when to use, third how stored, fourth pairing. Efficiently front-loads core 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?

Covers purpose, usage, storage, and persistence. Lacks mention of return value or idempotency (e.g., overwrite behavior), but is sufficient for a simple save tool. No output schema, so return value not expected to be detailed.

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

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

Schema coverage is 100% with clear descriptions for both key and value. Description adds usage examples but does not significantly enhance understanding beyond schema. 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?

Clearly states the tool saves data for later reuse across conversations/sessions, with specific verb 'Save data' and resource 'key-value pair'. Distinguishes from sibling tools recall and forget by explicitly pairing with 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?

Explicitly advises using it when discovering something worth carrying forward, with clear context on persistence (authenticated persistent, anonymous 24h). Does not explicitly state when not to use, but provides sufficient guidance for typical 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?

With no annotations provided, the description carries full burden for behavioral transparency. It discloses that the tool returns IDs plus citation URIs and gives examples of output. However, it does not mention read-only nature, authentication requirements, rate limits, or side effects. The description is adequate but not exhaustive.

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 (~100 words) and front-loaded with the main purpose. Every sentence adds value: purpose, specific IDs, examples, workflow guidance, efficiency claim. No redundant or wasted words, making it easy to parse quickly.

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 (2 parameters, no output schema), the description is fairly complete. It explains what the tool does, what it returns (IDs + citation URIs), and how to use it. However, it could mention error handling or limitations (e.g., if the input is ambiguous). Without an output schema, describing the return format is important, and it does so adequately.

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 significant value beyond the schema by providing concrete examples for each parameter (e.g., 'AAPL', '0000320193' for company; 'ozempic', 'metformin' for drug). It clarifies that the value parameter accepts tickers, CIKs, or names for companies, and brand or generic names for drugs, adding meaning that the schema's generic description lacks.

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 lists specific IDs (CIK, ticker, RxCUI, LEI) and provides concrete examples, making the purpose unambiguous. It also distinguishes from sibling tools by explicitly stating it replaces 2–3 lookup calls and should be used before other tools that need official identifiers.

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 tells when to use the tool: 'when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' It implies but does not explicitly state when not to use it, such as when an identifier is already known. The context is clear, but lacks explicit exclusions or alternative tool references.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It only states that the tool searches and returns certain fields, without mentioning side effects, pagination behavior, or whether it is read-only. This is minimal in the absence of 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 extremely concise—a single sentence that states the tool's purpose and output fields. It is well front-loaded and avoids 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 9 parameters, 0 required parameters, no output schema, and no usage examples, the description is incomplete. It does not explain how to construct a search query effectively or interpret results, which is critical for a search tool with many optional filters.

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 covers 89% of parameters with descriptions, so the description does not need to add much. However, the description provides no additional semantic context beyond the schema, such as how parameters interact or typical usage patterns.

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

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

The description clearly states that the tool searches USAJOBS announcements and specifies the returned fields (title, agency, location, etc.), making the purpose immediately clear. It distinguishes from siblings like get_job which fetches a single job details.

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 over alternatives (e.g., get_job for single job, list_agencies for agencies). It does not mention when not to use it or any prerequisites, leaving the agent to infer context from the tool name alone.

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 the return type (verdicts, actual value, citation, delta) and mentions the underlying data sources (SEC EDGAR + XBRL). It does not mention side effects, authentication, or rate limits, but for a read-only validation tool, this is adequate.

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 (4 sentences) and front-loaded: first sentence states the tool's action and purpose. Every sentence adds value—usage guidance, domain scope, return format, and efficiency benefit.

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 single-parameter tool with no output schema, the description provides sufficient context: what it does, when to use, supported domain, verdict categories, and return structure. It is slightly lacking in handling unsupported claims or error cases, but still fairly complete.

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

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

Schema description coverage is 100% with the 'claim' parameter already described. The description reinforces by giving examples ('Apple's FY2024 revenue was $400 billion'), but does not add additional 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 'validate' (verb) for 'natural-language factual claim' (resource) and lists specific domain (company-financial claims via SEC EDGAR). This distinguishes it from siblings like 'ask_pipeworx' 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 explicitly says 'Use when an agent needs to check whether something a user said is true' and provides example queries, establishing clear context. However, it does not explicitly state when not to use it (e.g., claims outside the financial domain), but the domain is specified.

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