Trademarks

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 picks the right tool and fills arguments, implying autonomy and potential side effects, but does not disclose specific behaviors like what tools it uses or if it modifies state.

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) with a clear front-loaded purpose. It includes examples that add value without being verbose.

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

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

Given the simple input (one natural language parameter) and no output schema, the description is sufficiently complete for an agent to understand how to invoke the tool. The examples cover typical use cases.

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

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

Schema description coverage is 100%, so the parameter 'question' is already described as 'Your question or request in natural language.' The description adds examples but not additional semantic details 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 accepts a plain English question and returns an answer from the best data source. It uses a specific verb ('ask') and resource ('question'), and differentiates from sibling tools by emphasizing natural language interaction.

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 (when you have a plain English question) and implies not to use other tools by saying 'No need to browse tools or learn schemas.' It provides example questions for 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?

Annotations already mark the tool as read-only and non-destructive. The description adds substantial behavioral context: it resolves the market, classifies bet type, fans out to appropriate packs, and returns a market-vs-model comparison. It explains the tool's core behavior beyond the annotations, providing transparency about the aggregation and analysis process.

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

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

The description is moderately long but front-loads the core purpose. Every sentence adds value, though some phrasing could be tightened. It remains efficient and well-structured for an AI agent 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?

For a complex tool with no output schema, the description adequately covers return values (evidence packet, comparison) and behavior (classification, fan-out). It lacks details on error handling or limitations, but overall provides sufficient context given the richness of the description.

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 meaning by clarifying that 'market' can be a slug, URL, or question text, and explains the 'depth' parameter's effect (quick vs thorough fan-out). This aids selection beyond the schema's basic types and enums.

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: research a Polymarket bet by pulling relevant Pipeworx data. It specifies the verb 'Research' and the resource 'Polymarket bet', and details actions like resolving the market, classifying the bet, fanning out to packs, and returning an evidence packet with comparison. This distinguishes it from sibling tools by emphasizing it aggregates multiple sources in one call.

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

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

Explicitly lists use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It also contrasts with alternatives by stating that agents using this tool convert better than those discovering packs themselves, providing clear when-to-use 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?

No annotations provided; description discloses data sources (SEC EDGAR, FDA) and output format (paired data, URIs), but lacks details on side effects, rate limits, or authentication requirements.

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

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

Three sentences, front-loaded with core functionality, no wasted words, and efficient structure.

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?

Description adequately explains purpose, entity types, data returned, and efficiency, though lacks detailed return format (no 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 coverage is 100%; description adds context by explaining entity-specific value formats (tickers/CIKs vs drug names) and URIs, but does not significantly enhance beyond schema descriptions.

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

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

Description clearly states it compares 2-5 entities side by side, specifies two entity types (company, drug) with distinct data, and mentions efficiency by replacing multiple sequential calls.

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

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

Description explains when to use (compare entities side by side) and implies efficiency over sequential calls, but does not explicitly state contraindications or alternatives among siblings.

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

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

Annotations are not provided, so the description must fully cover behavioral aspects. It states the tool returns the most relevant tools with names and descriptions, but does not disclose whether the search is purely semantic, any rate limits, or if it has side effects. No contradictions, but lacks depth.

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

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

The description is three sentences, each adding value: what it does, what it returns, and when to use it. No redundancy, well front-loaded with key action.

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 params, no output schema, no nested objects) and the presence of 7 sibling tools, the description is nearly complete. It lacks detail on how results are ranked or whether it indexes all tool properties, but the guidance is sufficient for selection.

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

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

Schema coverage is 100% and both parameters have descriptions in the schema. The description adds minimal extra meaning beyond the schema, mentioning 'natural language' for query and default/max for limit. 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 searches a tool catalog using natural language and returns relevant tools. It specifies the resource ('Pipeworx tool catalog'), the action ('search'), and distinguishes itself from siblings by being the discovery tool to call first.

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 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear context on when to use the tool, implying it should be used before other tools like get_trademark_* or ask_pipeworx.

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?

Although no annotations are provided, the description fully compensates by disclosing: the tool aggregates 10-15 calls into one, returns pipeworx:// URIs, only supports 'company' type, and warns that bundling federal contracts is too slow. This level of detail is excellent for agent decision-making.

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 elegantly concise with a bullet-style list of data sources, using one sentence for the core functionality and another for the alternative tool. Every word is purposeful, and the structure is 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?

Given only 2 parameters (both well-described) and no output schema, the description fully explains the return format (pipeworx:// URIs) and covers all critical context: supported type, input formats, and when to use alternatives. It leaves no gaps for agent comprehension.

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

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

Schema coverage is 100%, and the description adds significant value: it clarifies that only 'company' is supported (enum limited), explains that 'value' accepts ticker or CIK, and explicitly tells agents to use resolve_entity for names. This goes well beyond the schema's basic 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 returns a full entity profile across multiple packs, listing specific data sources for type='company'. It distinguishes itself from siblings like resolve_entity (name resolution) and compare_entities, with a specific verb 'profile' and resource 'entity'.

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

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

The description explicitly advises when to use this tool vs alternatives: 'For federal contracts call usa_recipient_profile directly (too slow to bundle)' and notes that names are not supported, recommending resolve_entity first. This provides clear usage context and exclusions.

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

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

With no annotations, the description carries full burden. It clearly states the action is destructive ('Delete'), which implies mutability, but does not disclose whether deletion is reversible, requires confirmation, or affects other data. Minimal but acceptable for a simple deletion.

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, clear sentence with no filler. It is front-loaded with the action and resource, and every word is necessary.

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 required parameter, no output schema, and straightforward purpose, the description is complete enough. It explains what the tool does and what parameter is needed. No return value explanation is required given no 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?

The schema has 100% coverage with a single required parameter 'key', and its description is 'Memory key to delete'. The description adds no further semantic detail (e.g., format, length, or example). Baseline 3 is appropriate since schema already explains the parameter.

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

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

The description clearly states the action ('Delete') and the resource ('a stored memory by key'). It distinguishes the tool from siblings like 'remember' (create) and 'recall' (retrieve), though the phrase 'by key' is slightly ambiguous without specifying that the key identifies the memory.

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 when a memory needs to be removed, but provides no guidance on when not to use it (e.g., if memory is shared) or alternatives (e.g., 'recall' to check before deleting). No exclusion or comparison with siblings is given.

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

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

No annotations provided, so description carries full burden. It mentions requires API key (a behavioral trait) but does not disclose rate limits, data freshness, or potential errors. Adequate but not detailed.

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

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

Two sentences, front-loaded with purpose, then details and requirements. Efficient and clear, though could be slightly more concise.

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

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

Tool has only 2 params with full schema coverage, no output schema. Description covers purpose, return fields, and auth requirement. Sufficient for this complexity.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds no extra meaning beyond what the schema already provides for the two parameters.

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 specifies 'Look up a US trademark by registration number' with clear verb ('look up') and resource ('trademark'), and lists returned fields (status, owner, etc.). It distinguishes from siblings like get_trademark_by_serial by focusing on registration number.

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?

Description explicitly states 'Requires USPTO API key', guiding the agent to provide that parameter. No exclusion or alternative mentioned, but the context of sibling tools (e.g., get_trademark_by_serial) provides implicit differentiation.

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 correctly notes that an external API key is required, which is a critical behavioral detail not obvious from the schema alone. No contradiction 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?

Two sentences, front-loaded with purpose, then key fields, then prerequisite. No wasted words.

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

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

Given simple tool with 2 well-described params, no output schema, and no nested objects, description covers purpose, parameters, and a critical prerequisite (API key). Complete for this complexity level.

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. Description adds value by explaining the purpose of the api_key parameter (requirement for USPTO) and serial_number (e.g., format example), going beyond the schema's own 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?

Description specifies exact resource (US trademark by serial number) and action (look up), and lists key returned fields (status, owner, dates, goods/services, classification), clearly distinguishing it from siblings like get_trademark_by_registration.

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?

States requirement for USPTO API key and where to obtain it, but does not explicitly mention when to use this tool versus alternative tools (e.g., when to use serial vs registration number).

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 explicitly states that the tool requires a USPTO API key, which is a critical behavioral detail not captured in annotations (which are empty). It also clarifies the nature of the data (prosecution history: office actions, responses, etc.). However, no information about rate limits, pagination, or error handling is provided.

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

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

The description is a single sentence that efficiently conveys the tool's purpose and a key requirement. Every word serves a purpose, and there is no extraneous information.

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

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

Given the tool has only two parameters, no output schema, and no annotations, the description adequately covers the purpose and the api_key requirement. However, it could mention what the tool returns (e.g., format or structure) to improve completeness for an agent without 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 provides clear descriptions for both parameters. The description adds context by indicating that api_key is a requirement and serial_number identifies the trademark, but these are already implied by the schema. No additional semantics beyond the schema are provided.

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 prosecution history for a trademark by serial number, using the verb 'get' and specifying the resource (prosecution history) and identifier (serial number). It also notes a requirement (USPTO API key), and distinguishes from sibling tools like get_trademark_by_serial which likely retrieves basic data.

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 this tool (to retrieve prosecution history) and mentions a prerequisite (USPTO API key). It does not explicitly state when not to use it, but the sibling tools (e.g., get_trademark_by_serial) imply alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses the rate limit, the fact that it is free, and the type of content to include. However, it does not mention whether feedback is reviewed or if there are acknowledgements, which would be useful for an agent to set expectations.

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

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

The description is three sentences with no redundant information. It front-loads the purpose, then provides usage guidance, and ends with constraints. Every sentence earns its place.

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

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

For a simple feedback tool with three parameters (one nested) and no output schema, the description covers purpose, usage, rate limit, cost, and content guidelines. It could mention whether feedback is reviewed or if there is a response, but it is largely complete for the tool's simplicity.

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 agents to describe what they tried in terms of Pipeworx tools/data and to avoid including the end-user's prompt verbatim, which supplements the message parameter's schema description.

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

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

The description clearly states 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, missing data, or praise). It distinguishes itself from sibling tools which are all queries, comparisons, discovery, and memory tools, making the purpose unambiguous.

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

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

The description explicitly states when to use the tool and provides a negative guideline ('do not include the end-user's prompt verbatim'). It also specifies a rate limit (5 messages per identifier per day) and the cost ('Free'), giving clear usage boundaries.

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

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

Annotations already indicate read-only, open-world, non-destructive; description adds detailed behavioral context: two scanning modes, monotonicity logic, and output format (ranked opportunities with reasoning), fully aligning 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?

Slightly verbose but each sentence adds value; front-loaded with purpose, then efficient breakdown of modes and why cross-event matters. A minor trim could tighten it but highly 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?

No output schema but description specifies returns 'ranked opportunities with suggested trade direction + reasoning', sufficient for agent. Could hint at data recency or limitations, but adequate.

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

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

Schema covers both parameters with 100% description; description adds significant meaning by defining modes, providing examples, and explaining cross-event search, far exceeding schema basics.

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 it finds arbitrage opportunities via monotonicity violations, distinguishes two modes (event/topic), and differentiates from siblings like bet_research or polymarket_edges.

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

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

Explicitly explains when to use each mode, including the rationale for cross-event mode (Polymarket listing each cutoff as separate event), providing clear when-to/why 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?

Annotations declare readOnlyHint=true, so safety is clear. The description goes beyond by detailing the process: scans top markets, groups by asset, fetches price history once, computes model probability using FRED and coinpaprika, ranks by edge. This adds useful behavioral context not covered by annotations.

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

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

The description is appropriately sized, front-loaded with the main purpose in the first sentence, and every sentence adds value. No wasted words, clear 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?

Given no output schema, the description explains return values (ranked by edge magnitude with suggested direction). It covers the model and data sources, providing enough context for an agent to understand the tool's function. Lacks error handling details, but overall 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?

Input schema has 100% description coverage, so the schema already documents each parameter. The description does not add new semantic information beyond what is in the schema (e.g., defaults and limits are repeated). 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 scans high-volume Polymarket markets to find where Pipeworx data disagrees with market price, returns ranked edges with trade direction. It is specific about the verb and resource, and the mention of 'V1 covers crypto-price bets' distinguishes it from potential siblings like polymarket_arbitrage.

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

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

The description provides context ('what should I bet on today') suggesting when to use, but does not explicitly state when not to use or mention alternatives. It lacks exclusions or conditions for use, relying on implied usage.

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

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

The description does not disclose any behavioral traits beyond what is obvious from the tool name and schema. No annotations are provided, so the description carries the full burden, but it lacks details on side effects, persistence guarantees, or session scope.

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 two sentences that efficiently convey the purpose and usage. No unnecessary words, but it could be slightly more 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 simple tool (single optional parameter, no output schema, no annotations), the description is adequate but could mention return format or whether memories persist across sessions. It does not fully compensate for missing annotations.

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

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

Schema coverage is 100%, so the description adds minimal extra meaning. It explains that omitting key lists all memories, which aligns with the schema's 'omit to list all keys' description.

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

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

The description clearly states the verb 'retrieve' and resource 'memory by key', and distinguishes between retrieving a specific key and listing all memories. It differentiates from sibling tools like 'remember' and 'forget' by specifying this is for reading stored context.

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

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

The description says to use this for retrieving context saved earlier, implying when to use it. It does not explicitly state when not to use it or provide alternatives, but the context is clear for a memory retrieval tool.

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

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

With no annotations, the description carries full burden. It discloses the parallel fan-out, accepted date formats, and return structure (structured changes, total_changes count, URIs). It does not explicitly state if the operation is read-only or has side effects, but the behavior is well-described.

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

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

The description is a single, well-organized paragraph that front-loads the purpose and then details behavior and output. Every sentence earns its place with no wasted words. It is concise yet comprehensive.

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 (multiple data sources) and no output schema, the description adequately explains return values: structured changes, total_changes count, and URIs. It covers the key aspects, though a bit more detail on the structure of 'structured changes' could improve completeness.

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 beyond schema: it explains 'type' is limited to 'company', gives examples for 'since' (ISO date or relative, with recommended values), and clarifies 'value' as ticker or CIK. This aids correct parameter usage.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the data sources (SEC EDGAR, GDELT, USPTO) and the return format, making it distinct and actionable. The verb 'what's new' and resource 'entity' are specific.

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

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

The description explicitly recommends use cases: 'Use for "brief me on what happened with X" or change-monitoring workflows.' This provides clear context. However, it does not mention when NOT to use or suggest alternative tools, which would improve 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 persistence behavior: 'Authenticated users get persistent memory; anonymous sessions last 24 hours'. This adds valuable context beyond the schema about data lifespan.

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 with front-loaded purpose. The first sentence states the core function, the second provides usage scenarios, the third adds behavioral notes. Every sentence adds value, but could be slightly more concise.

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

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

Given the simple key-value store operation, no output schema, and no nested objects, the description is complete. It covers purpose, usage, and behavioral traits 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 description coverage is 100% with examples for 'key' and description for 'value'. The description adds minimal extra meaning beyond the schema, 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 stores a key-value pair in session memory, specifying the verb 'Store' and resource 'key-value pair in your session memory'. It distinguishes itself from siblings like 'forget' and 'recall' by focusing on saving data.

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

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

The description provides explicit use cases: 'save intermediate findings, user preferences, or context across tool calls'. It implies when to use (for persisting data) but doesn't explicitly contrast with 'forget' or 'recall' for when not to use.

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

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

No annotations exist, so the description carries the burden. It discloses output fields and versioning notes, but does not mention failure modes, rate limits, or state changes. For a simple read operation, this is adequate but not comprehensive.

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, no fluff, front-loaded with the primary action. Every sentence adds value: purpose, version/type details, and efficiency claim. Excellent conciseness.

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

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

Given the tool's simplicity (2 params, no output schema), the description covers purpose, input formats, and output fields. It lacks error handling details, but overall it is sufficiently complete for an agent to use the tool 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 coverage is 100%, but the description enriches the 'value' parameter with concrete examples (ticker, CIK, name) and clarifies the 'type' enum's scope. This adds significant practical guidance 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 resolves an entity to canonical IDs, specifies supported type (company), provides concrete input examples, and distinguishes from siblings by claiming it replaces 2–3 lookup calls. This makes the purpose highly specific and actionable.

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 says 'Replaces 2–3 lookup calls,' which implies when to use it. However, it does not explicitly state when not to use it or name alternatives among siblings. Still, the context is clear enough for an 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?

Despite having no annotations, the description discloses the tool's return values (verdict types, structured form, actual value with citation, percent delta) and its data sources. It does not explicitly state that it is read-only, but the fact-checking context implies no side effects. Behavioral transparency is good but could be improved by noting any rate limits or authentication requirements.

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 (4 sentences) and information-dense, with key information front-loaded. Every sentence provides value: purpose, scope, return value, and efficiency claim. No redundant or vague language.

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

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

Given the single parameter and no output schema, the description fully explains the tool's function, supported domain, and return details. The agent can understand what to expect and when to use it without needing additional context.

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

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

The input schema has 100% description coverage for the sole parameter, 'claim', with clear examples and explanation. The description also elaborates on how the claim is processed, adding 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 clearly states the tool's purpose: fact-checking a natural-language claim against authoritative sources, specifically for company-financial claims. It specifies the sources (SEC EDGAR + XBRL) and the types of claims supported, distinguishing it from sibling tools which appear 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 explicitly indicates when to use the tool (for company-financial claims on public US companies) and highlights its efficiency benefit over sequential agent calls. However, it does not mention when NOT to use it or provide direct alternatives among siblings, though the domain specificity implicitly guides usage.

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