Passive Aggression

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

The description discloses that the tool automatically picks the right tool and fills arguments, which is helpful. However, it does not mention any limitations, error handling, or what happens when no data source is suitable. Since no annotations are provided, the description could do more to clarify behavioral traits.

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

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

The description is concise at three sentences, with the purpose stated upfront. It includes examples which are valuable, though slightly longer than necessary. Every sentence adds value, so it earns a high score.

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

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

Given the tool's simplicity (one parameter, no output schema), the description is mostly complete. It explains the core functionality and provides examples. However, it could be more complete by mentioning potential limitations or how the tool selects data sources.

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 already describes the 'question' parameter as 'Your question or request in natural language', and the description adds context with examples but does not provide additional semantic detail beyond what the schema offers. With 100% schema coverage, the baseline is 3, and the description does not exceed that.

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

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

Description clearly states the tool's purpose: answering natural language questions by automatically selecting the right data source. It provides concrete examples to illustrate its functionality, making it easy to understand what the tool does and distinguishing it from other tools.

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

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

The description explains when to use this tool: when you want to ask a question in plain English and get an answer without browsing tools or learning schemas. It does not explicitly state when not to use it or mention alternatives, but the examples give clear guidance on appropriate use cases.

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

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

Annotations indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds behavior beyond annotations: it resolves the market, classifies the bet type, fans out to different packs based on classification, and performs a comparison. No contradictions.

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

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

The description is three sentences, front-loaded with the main purpose. It is reasonably concise but includes some marketing phrasing ('core demo product') that could be trimmed. Overall, it's well-structured and easy to parse.

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

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

Despite no output schema, the description explains the return format (evidence packet + market-vs-model comparison). It covers input flexibility, internal logic, and use cases. For a complex multi-step tool, this is highly complete.

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

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

Schema coverage is 100%, and description explains the 'market' parameter accepts slug, URL, or question text. The 'depth' parameter is covered by schema with enum. Description adds minimal extra meaning beyond 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 researches a Polymarket bet, pulls Pipeworx data, resolves the market, classifies the bet, fans out to appropriate data packs, and returns an evidence packet with a market-vs-model comparison. It uses a specific verb ('research') and resource ('Polymarket bet'), and differentiates from siblings like validate_claim or compare_entities.

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

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

Explicit use cases are given: 'should I bet on X?', 'what does the data say...', 'is there edge in this bet?'. It also notes this is the core demo product, implying when to prefer it over manual discovery. No explicit exclusions or alternatives, but context is clear enough.

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

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

With no annotations, the description carries the burden. It explains the return includes 'paired data + pipeworx:// resource URIs' and lists data categories per type, but does not disclose side effects, auth needs, or rate limits.

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

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

Three concise sentences, front-loaded with the main action. No redundant information.

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

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

Given no output schema, the description adequately explains return content per type. Could mention response format more explicitly, but overall complete for a comparison 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 covers both parameters with descriptions (100% coverage). The description adds value by detailing what data is returned for each type, going 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 'Compare 2-5 entities side by side' and differentiates between company and drug types with specific data points. Sibling tools are unrelated, so no confusion.

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 notes it 'Replaces 8-15 sequential agent calls,' implying efficiency. However, no explicit when-not or alternative tools are mentioned, though siblings are unrelated.

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

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

Since no annotations are provided, the description must disclose behavior. It states the tool is a search and returns tool names and descriptions, but does not mention if it modifies state, requires authentication, or has rate limits. However, given the nature of a search tool, these omissions are minor, and the description adequately conveys its read-only 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 three sentences, all essential: what the tool does, what it returns, and when to use it. No wasted words. The key instruction is front-loaded.

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 low complexity (2 parameters, no nested objects, no output schema), the description fully covers the tool's purpose, usage guidance, and parameter semantics. There is no missing information needed for an agent to invoke it correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the 'query' parameter expects natural language descriptions (with examples), which goes beyond the schema's simple description. It also clarifies the 'limit' default and max, which are not in the schema. This extra context earns a 4.

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 by natural language description, returns relevant tools, and should be called first when many tools are available. The verb 'search' and resource 'tool catalog' are specific, and it distinguishes itself from siblings by being the discovery 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 says 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task,' providing clear when-to-use guidance. It does not need to mention when not to use because it is the primary discovery 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?

No annotations provided, but the description clearly lists what data is returned and mentions citation URIs. Does not cover side effects, authentication, or rate limits, but is adequate for a read-only aggregation 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 sentences with dense, valuable information. First sentence lists returns, second provides a key exclusion. No fluff.

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

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

Despite no output schema, description sufficiently explains return format (pipeworx:// URIs), covers alternate workflows (resolve_entity, usa_recipient_profile), and sets expectations for the tool's scope.

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 beyond schema: explains 'type' only supports 'company', 'value' accepts ticker or CIK, and explicitly warns that names are not supported (use resolve_entity).

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

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

The description clearly states it returns a full profile of an entity with specific data points for type='company', and explicitly compares to sibling tools like compare_entities and resolve_entity, establishing distinct purpose.

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

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

Explicitly tells when to use (comprehensive profile) and when not to (federal contracts should use usa_recipient_profile), and implies using resolve_entity if only a name is available.

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

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

Since no annotations are provided, the description carries full burden. It indicates a destructive action (delete), but does not disclose side effects, such as whether deletion is permanent or reversible, or if confirmation is required. The behavior is partially transparent 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 a single, concise sentence that front-loads the action and resource. No unnecessary words or details, achieving maximum efficiency.

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 (1 param, no output schema, no nested objects), the description is adequately complete. It covers purpose and key identification. However, it could briefly mention that the operation is permanent if applicable, but not a critical omission.

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 single parameter 'key', which is also described in the schema. The description adds no additional semantic meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.

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

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

The description clearly states the action ('Delete'), the resource ('a stored memory'), and the method of identification ('by key'). It effectively differentiates from sibling tools like 'recall' and 'remember' by specifying deletion rather than retrieval or storage.

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 specific memory needs to be removed, but it does not provide explicit guidance on when to use this tool versus alternatives like 'remember' or 'recall'. No mention of prerequisites or when not to use it.

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

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

With no annotations, the description carries full burden. It accurately describes the tool as a read-only analysis tool that returns structured output (severity, phrases, translation, responses). It does not mention rate limits or auth, but for a text analysis 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 two sentences, front-loaded with the purpose, and contains no unnecessary 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?

The description covers the tool's purpose, output, and usage context. Since an output schema exists (not shown but indicated), the description does not need to detail return values further. It is 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 the description does not need to repeat parameter info. It adds value by explaining the output but does not elaborate on parameters beyond what the schema provides. 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 explicitly states 'Analyze text for passive-aggressive language' and lists specific outputs (severity score, flagged phrases, translation, suggested responses). It clearly distinguishes from other tools like ask_pipeworx, which are unrelated.

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

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

The description ends with 'Use when reviewing emails, messages, or conversations to identify underlying hostility,' providing clear context. While it does not explicitly mention when not to use it or alternatives, the sibling tools are unrelated, so this is sufficient.

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 rate limit (5 messages per identifier per day) and notes it is 'Free.' It does not detail what happens after submission (e.g., no guarantee of reply) but for a feedback tool this is acceptable.

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

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

The description is three concise sentences, front-loading the purpose, then usage guidance and constraints. No filler or redundancy.

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

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

Given the tool's moderate complexity and no output schema, the description covers the essentials: purpose, usage, parameter guidance, and constraints. It could mention that feedback is sent to the team and that responses are not guaranteed, but it is largely 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 100% description coverage, but the description adds value by specifying how to write the message (focus on Pipeworx tools/data, avoid end-user prompts) and the rate limit. This goes 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's purpose: 'Send feedback to the Pipeworx team' with specific use cases (bug reports, feature requests, missing data, praise). This distinguishes it from sibling tools like 'ask_pipeworx' (for questions) and 'forget'/'remember' (for memory operations).

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 use cases and instructions on how to write the message ('Describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim') and mentions a rate limit. However, it does not explicitly state when not to use this tool (e.g., for general inquiries, prefer ask_pipeworx), which would make it a 5.

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

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

Annotations already mark the tool as read-only and non-destructive. The description adds specific behavioral details: walks child markets, extracts dates/thresholds, sorts, and reports violations. No contradictions, and it adds value beyond annotations.

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

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

The description is relatively concise for the complexity, with clear explanations and no redundant phrases. It could be slightly shorter but is well-structured and informative.

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

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

Given the tool has one parameter and no output schema, the description fully covers what the tool does, how it works, and what it returns. It is complete for an agent to correctly invoke and interpret results.

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 that the parameter accepts slug or URL, which matches the schema description. No additional parameter details are needed beyond what the schema provides.

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

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

The description clearly states the tool finds arbitrage opportunities in Polymarket events by checking monotonicity violations. It explains the core logic and distinguishes itself from siblings like 'polymarket_edges' by focusing on arbitrage detection.

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 (when an event has multiple date-threshold markets) and how to use (pass event slug or URL). It does not explicitly state when not to use, but the context is clear.

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

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

Beyond annotations (readOnly, non-destructive), the description reveals internal behavior: data sources (FRED, coinpaprika), lognormal model, caching pattern (fetches price history once), ranking by |edge|, and output includes trade direction. This full disclosure exceeds what annotations provide.

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

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

The description is dense but efficient, with the main action in the first sentence followed by explanatory details. Slight verbosity in internal method description could be trimmed, but overall it is well-structured and front-loaded.

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

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

Despite no output schema, the description explains what is returned (top N ranked by edge magnitude with trade direction) and covers data sources, model, and caching. It is sufficiently complete for agents to understand purpose and output, though additional details on output format would raise it to 5.

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 all three parameters. The tool description adds context about defaults and constraints (e.g., window enum values) but doesn't significantly enhance understanding beyond the 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?

Description clearly states it scans Polymarket markets to find where Pipeworz data disagrees most with market price, targeting crypto-price bets. It explains the model, grouping, and ranking, and explicitly distinguishes from paging through markets manually. This precision and differentiation from sibling tools like polymarket_arbitrage earns top score.

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 includes the use case 'what should I bet on today,' implying a specific context, but it does not explicitly state when not to use this tool or mention alternative sibling tools. More explicit guidance on when to prefer bet_research or polymarket_arbitrage would raise score.

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 behavioral burden. It explains the tool is a read operation (retrieve/list). However, it does not disclose whether listing all memories has performance implications, if the memories are persistent across sessions, or if there are size limits. 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, no wasted words. Front-loaded with the core function. 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?

The tool has 1 optional parameter, no output schema, and no annotations. The description explains the two use cases but lacks details on return format, error behavior (e.g., key not found), or whether listing is limited. It is minimally sufficient but not complete for all scenarios.

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% (only one parameter with description). The description adds context that omitting the key lists all memories, which is already implied by the parameter being optional. Minimal added 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 retrieves a stored memory by key, or lists all memories when key is omitted. It specifies the verb 'retrieve' and resource 'memory', and distinguishes the two modes of operation.

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

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

The description explains when to use it: 'retrieve context you saved earlier in the session or in previous sessions.' It implies when to omit the key (to list all). It does not explicitly mention when not to use it or alternative tools, but the context is clear enough.

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

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

With no annotations provided, the description carries the full burden. It discloses key behaviors: parallel fan-out across data sources, supported since formats (ISO date and relative), and return value types (structured changes, total_changes count, pipeworx:// URIs). It does not cover error handling or rate limits, but for a read-only tool, this is sufficient.

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, efficiently packing all necessary information without fluff. It could be slightly more structured (e.g., using bullet points for the data sources), but it remains clear and easy to read. No redundancy with schema.

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

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

Given the tool's moderate complexity (multiple data sources, since parameter options), the description adequately covers what the tool does, its inputs, and its outputs. No output schema exists, so the description of return values (structured changes, URIs) is helpful. It might lack details on pagination or limits, but it is sufficient for typical use.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds practical examples (e.g., 'AAPL' for ticker, '7d' for since) and clarifies the since format, but most of this information is already present in the schema descriptions. The tool name and context add minimal extra semantic 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: 'What's new about an entity since a given point in time.' It specifies the entity type ('company'), details the data sources (SEC EDGAR, GDELT, USPTO), and explains the parallel fan-out. This provides a very specific verb+resource combination that uniquely defines the tool's function.

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

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

The description gives explicit usage context: 'Use for "brief me on what happened with X" or change-monitoring workflows.' This clearly identifies when the tool should be used. However, it does not explicitly state when not to use it or mention alternative sibling tools, so it lacks full differentiation 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 provided, the description carries full burden. It clearly discloses behavioral traits: persistence depends on authentication (authenticated users get persistent memory; anonymous sessions last 24 hours). This adds value beyond the schema.

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

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

The description is three sentences, front-loading the primary purpose ('Store a key-value pair'), then usage guidance, then persistence details. 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 the tool's simplicity (2 required string params, no output schema), the description is complete: purpose, usage context, and persistence behavior are covered. It could optionally mention that values are overwritten on same key, but not necessary.

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% and both parameters ('key' and 'value') have descriptive examples and explanations in the schema. The description does not add further parameter-level 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 stores a key-value pair in session memory, specifying the verb 'store' and the resource 'key-value pair in session memory'. It distinguishes itself from siblings like 'recall' (which retrieves) and 'forget' (which deletes) by describing the saving 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?

The description explicitly suggests when to use this tool: 'save intermediate findings, user preferences, or context across tool calls'. It implies usage for temporary data storage and differentiates persistence levels between authenticated and anonymous users, but does not explicitly mention when not to use or name alternatives like 'recall' or 'forget'.

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 discloses what inputs are accepted (ticker, CIK, name), what outputs are returned (ticker, CIK, company name, resource URIs), and the benefit of a single call. Since no annotations are provided, the description carries full burden, and it covers the main behavioral aspects adequately.

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 long, each with a clear purpose: first sentence states the core functionality, second gives specifics, third highlights benefit. It is front-loaded and contains no unnecessary 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?

For a simple lookup tool with two parameters and no output schema, the description covers the essentials: purpose, input formats, output details, and efficiency gain. It could mention potential error handling or case sensitivity, but it is largely complete for its complexity.

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

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

The input schema already has descriptions for both parameters, giving 100% coverage. The description adds value by providing concrete examples and clarifying the formats for the 'value' parameter, which enhances understanding beyond the schema alone.

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

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

The description clearly states the tool resolves an entity to canonical IDs, specifies the supported entity type (company), and provides concrete examples. It distinguishes itself from siblings by claiming to replace 2–3 lookup calls, making the purpose very 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?

The description explains when to use this tool: when you need to resolve an entity in a single call instead of multiple lookups. It gives examples of inputs and outputs. However, it does not explicitly mention alternatives or when not to use it, so it falls slightly short of a 5.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns a verdict, structured form, actual value with citation, and percent delta, and notes it replaces 4-6 sequential calls. This provides good insight into 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 concise (few sentences), front-loaded with the core action, and includes specific examples and output details without 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 simplicity of the tool (one parameter, no output schema), the description adequately covers input, supported claims, and output details. It lacks error handling or limitations but is sufficient for the tool's complexity.

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

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

The sole parameter 'claim' has 100% schema description coverage. The tool description adds clear examples and context (e.g., 'Apple's FY2024 revenue was $400 billion') beyond the schema, enhancing understanding.

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 fact-checks natural-language claims, specifies supported domain (company-financial claims for public US companies), and mentions it replaces sequential agent calls. It distinguishes itself from siblings by describing its unique functionality.

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 indicates when to use the tool (for financial claims about public US companies) and implies its scope via 'v1 supports'. However, it does not explicitly state when not to use it or provide direct alternatives among sibling tools.

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