nouel

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds valuable context: default model behavior, API key requirement for Anthropic, and the return structure with scores, confidence, signals, and raw responses. This goes beyond annotations without contradicting them.

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 succinct sentences that front-load the purpose, explain key configuration, and list use cases. Every sentence earns its place with no redundancy.

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

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

Given no output schema, the description fully explains return values (per-model and combined view). It covers authentication, default behavior, and use cases. For a probe tool with annotations, this is 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?

All 4 parameters have schema descriptions (100% coverage). The description adds functional context: explaining the default model, the role of '_apiKey', and the helpfulness of 'context' for disambiguation. This enhances understanding beyond the schema.

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

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

The description clearly states the tool's purpose: probing LLMs for knowledge about a business/brand/topic and scoring visibility per model. It uses specific verbs ('probe', 'score') and resources ('LLMs', 'visibility'). It distinguishes itself from siblings like 'scan_competitor_ai_presence' by focusing on visibility scoring across multiple models.

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: default free model, optional Anthropic via BYO key, use cases like AI-marketing audits and pre-launch checks. It does not explicitly state when not to use the tool or compare to alternatives, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that Pipeworx automatically selects tools and fills arguments, which is valuable context. However, it doesn't mention limitations like response time, data source availability, error handling, or authentication requirements. The description doesn't contradict annotations (none exist).

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 efficiently structured: it opens with the core functionality, explains the automation benefit, and provides three diverse examples. Every sentence adds value without redundancy. The length is appropriate for explaining this type of meta-tool.

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

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

Given the tool's complexity (automated tool selection) and lack of annotations/output schema, the description does well to explain the high-level behavior and provide examples. However, it could better address potential limitations or edge cases. The absence of an output schema means the description doesn't need to explain return values, but more behavioral context would help.

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

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

The schema description coverage is 100%, so the schema already documents the single 'question' parameter. The description adds context by emphasizing 'plain English' and 'natural language,' and provides concrete examples that illustrate appropriate question formats. This enhances understanding beyond the basic 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 the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes itself from sibling tools by emphasizing natural language processing rather than structured queries.

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

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

The description provides explicit guidance on when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It contrasts with alternatives by suggesting this is for users who want to avoid manual tool selection. The examples further clarify 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?

The description discloses that it resolves the market, classifies the bet, fans out to appropriate packs, and returns an evidence packet with a market-vs-model comparison. This adds significant context beyond the annotations (readOnlyHint, openWorldHint, non-destructive). 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 detailed but somewhat long. It front-loads the main purpose and provides all necessary information. Could be more concise, but 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?

Despite no output schema, the description explains exactly what is returned (evidence packet + market-vs-model comparison) and the process (classification, fan-out). It is complete for a tool that researches Polymarket bets.

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 schema already documents both parameters. The description adds context: 'market' can be slug, URL, or question text; 'depth' quick vs thorough with default. This reinforces and slightly enriches 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 verb 'Research' and the resource 'Polymarket bet', and explains how it pulls Pipeworx data. It distinguishes from siblings by being specifically for Polymarket betting research, not general data queries.

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 like 'should I bet on X?', 'what does the data say?' and positions itself as the core demo product. It lacks explicit alternatives or when-not-to-use guidance, 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?

No annotations are provided, so the description carries full burden. It discloses return type (paired data + pipeworx:// resource URIs) and per-type metrics, but does not mention safety (read-only? destructive?), rate limits, or authentication requirements. The behavioral traits are partially clear 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?

Two sentences cover the core purpose and key detail. The first sentence is a strong verb-first statement of purpose. Minor improvement could be structuring the type-specific returns more clearly, but overall it's concise 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?

Given no output schema, the description adequately covers return values (paired data, resource URIs). It also notes efficiency benefit (replaces 8–15 calls). However, it doesn't mention error conditions, validation rules, or max/min entity count beyond schema. For a tool with moderate complexity, it is fairly complete but not exhaustive.

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 both parameters fully described. The description adds meaning beyond schema by detailing what each type returns (e.g., 'revenue, net income, cash, long-term debt' for company). This enriches the agent's understanding of the parameter values.

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 'Compare 2–5 entities side by side in one call' with specific metrics per type (company: revenue, net income, etc.; drug: adverse-event count, FDA approval count). This clearly distinguishes it from sibling tools like ask_pipeworx or resolve_entity, which are for different purposes.

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

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

The description explains the use case: comparing 2-5 entities in one call to replace 8-15 sequential calls. It implies efficiency benefits but doesn't explicitly state when not to use (e.g., for single entity queries) or 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the search functionality and return format (tools with names and descriptions), but lacks details on error handling, rate limits, or performance characteristics. It adequately covers basic behavior but misses advanced operational context.

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

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

The description is perfectly concise with two sentences that each serve a distinct purpose: the first explains the core functionality, the second provides critical usage guidance. Every word earns its place with zero redundancy or fluff.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description does well by explaining the purpose, usage context, and basic return format. However, it could benefit from mentioning what happens when no tools match or if there are authentication requirements.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description mentions 'describing what you need' which aligns with the 'query' parameter, but adds no additional semantic context beyond what the schema provides. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('search', 'returns') and resources ('Pipeworx tool catalog', 'most relevant tools with names and descriptions'). It distinguishes from siblings by focusing on tool discovery rather than prize or laureate searches, making the purpose explicit and differentiated.

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

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

The description provides explicit guidance on when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This clearly indicates the primary use case and context, offering strong direction for agent decision-making.

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

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

No annotations provided, so description carries full burden. It discloses the one-call bundling, returns pipeworx:// citation URIs, and explains why usa_recipient_profile is separate. Lacks mention of authentication or read-only nature, but still strong.

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, then alternative. Every sentence adds value, no waste.

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?

Complex multi-source tool with no output schema. Description lists all sources, input constraints, and alternatives. Could mention pagination or limits, but covers essentials well.

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% (baseline 3). Description adds meaning by explaining type supports only 'company', value can be ticker or CIK, and names not supported (recommends resolve_entity). This goes 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?

Description clearly states it returns a full profile of an entity across multiple Pipeworx packs, listing specific data sources (SEC, XBRL, patents, news, LEI) and distinguishing from sibling usa_recipient_profile. It also notes it replaces 10-15 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?

Explicitly says when to use (for comprehensive entity profile) and when not to (use usa_recipient_profile for federal contracts because it's too slow to bundle). Also implies using resolve_entity for name resolution.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool deletes a memory, implying a destructive mutation, but doesn't address critical aspects like whether deletion is permanent/reversible, what permissions are required, error handling for non-existent keys, or side effects. This leaves significant gaps for a mutation tool.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action ('Delete') and resource ('stored memory'), making it immediately understandable. Every word earns its place in conveying the essential purpose.

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

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

For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral traits like permanence, permissions, or error handling, nor does it explain what happens after deletion (e.g., confirmation message, null return). Given the complexity of a delete operation, more context is needed for safe 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?

The schema description coverage is 100%, with the single parameter 'key' fully documented in the schema as 'Memory key to delete'. The description adds no additional semantic context beyond restating this parameter's purpose, so it meets the baseline score of 3 where the schema does the heavy lifting.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'recall' or 'remember' which likely handle memory retrieval/creation, leaving some ambiguity about the tool's unique role in the memory management system.

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

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

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (e.g., needing an existing memory key), exclusions, or relationships to sibling tools like 'recall' (likely for retrieval) or 'remember' (likely for creation). The agent must infer usage context from the tool name alone.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details: it fetches the page, extracts title/description/key links, and emits standard markdown format. This goes beyond annotations, though it could optionally note error handling for unreachable URLs.

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 plus a bullet point list of use cases. Every sentence adds value: first states the main action and benefit, second details the process, third lists use cases. No superfluous words, perfectly 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 tool's low complexity (2 parameters, no output schema), the description covers input purpose, process steps, output format, and use cases. Annotations align, and no additional information is necessary for an agent to invoke the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add new meaning beyond the schema's parameter descriptions; it merely reinforces the context. No extra value is provided for the 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?

The description uses a specific verb 'generate', identifies the exact resource 'llms.txt file', and targets 'any URL'. It clearly distinguishes itself by naming the output format and listing concrete use cases, making its 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 includes a 'Useful for' section listing three clear scenarios (client indexing, personal project drafting, competitor auditing). While it does not explicitly mention when not to use the tool or compare with sibling tools, the provided contexts are enough for a typical agent to decide applicability.

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 of behavioral disclosure. It describes the basic functionality but lacks details on permissions, rate limits, error handling, or response format. For a read operation with no annotation coverage, this is a significant gap.

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

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

The description is a single, efficient sentence that front-loads the core purpose and includes the optional parameter. There is zero wasted text, making it highly concise and well-structured.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description covers the basic purpose and parameters adequately but lacks behavioral details like response format or error conditions. It is minimally viable but has clear gaps in 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 description coverage is 100%, so the schema already documents both parameters ('year' and 'category') thoroughly. The description adds minimal value by mentioning the optional filtering by category but does not provide additional syntax or format details 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 specific action ('Get all Nobel Prizes'), resource ('Nobel Prizes'), and scope ('awarded in a specific year, optionally filtered by category'). It distinguishes from the sibling tool 'search_laureates' by focusing on prizes rather than laureates.

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 ('awarded in a specific year, optionally filtered by category'), but does not explicitly state when not to use it or mention the sibling tool 'search_laureates' as an alternative for different queries.

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

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

With no annotations, the description carries full burden. It discloses the rate limit and a content rule, but does not explain what happens after submission (e.g., confirmation, storage), authentication needs, or any side effects. The information is adequate but not thorough.

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

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

The description is two sentences with no fluff. The first sentence states purpose, the second adds usage guidelines and limitations. Front-loaded and compact.

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?

Output schema is absent, but for a feedback tool the return value is likely implicit. The description covers usage, constraints (rate limit, content rule), and purpose. It misses stating what the tool returns (e.g., success confirmation), but is otherwise sufficient for a simple feedback function.

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 baseline is 3. The description does not add meaningful parameter-level information beyond what the schema provides. It reinforces the message parameter's intent but does not compensate for any gaps.

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

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

The description clearly states the verb 'Send feedback' and the resource 'Pipeworx team'. It enumerates specific use cases (bug reports, feature requests, missing data, praise) and adds a guideline about what not to include. This distinguishes it from siblings which are data retrieval or processing tools.

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

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

The description explicitly says when to use the tool: for bug reports, feature requests, missing data, or praise. It also provides a behavioral constraint ('do not include the end-user's prompt verbatim') and a rate limit. However, it does not contrast with sibling tools or state when to avoid this 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?

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds transparency about the data source ('CF analytics-engine'), privacy ('no PII'), and caching behavior ('Cached 5min-1h depending on window'). This provides valuable context for an agent.

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 four sentences, each adding value. It is front-loaded with the primary function, then lists use cases, and ends with details on data source and caching. No unnecessary 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's simplicity (single optional parameter, no output schema, comprehensive annotations), the description covers all essential aspects: purpose, usage guidance, parameter semantics, data provenance, privacy, and caching. It is fully adequate for an agent to decide and invoke 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?

The parameter 'window' is already well-described in the schema (enum with defaults). The description adds meaning by explaining the trade-off between shorter and longer windows ('what's hot right now' vs. 'steady-state demand'), going beyond the schema definition.

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

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

The description clearly states that the tool returns top tools, top packs, and total call volume over a specified window. It uses specific verbs ('returns') and resources ('top tools, top packs, call volume'), and its purpose is distinct from sibling tools like 'discover_tools' or 'entity_profile'.

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

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

The description lists three explicit use cases (discovering hot data sources, confirming canonical choice, assessing alignment) that guide when to use the tool. While it does not explicitly state when not to use, the context is clear and helpful.

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 declare readOnlyHint and openWorldHint. Description adds behavioral detail on search strategy across events and grouping logic, without contradicting annotations. No mention of rate limits or side effects, but these are not expected for a read-only tool.

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

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

Description is well-organized with clear separation of modes and examples. Slightly wordy but informative; no redundant sentences.

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

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

Given no output schema, description adequately explains return value (ranked opportunities with reasoning). Parameters fully covered. Complexity of cross-event search is addressed with an example, making it self-contained.

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%, providing baseline 3. Description adds value by explaining each parameter's role (event mode vs topic mode) with concrete examples, going 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 verb (find arbitrage opportunities) and resource (Polymarket markets via monotonicity violations). Distinguishes two modes (event and topic), providing specific use cases. No ambiguity.

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 describes when to use each mode with an example of cross-event mode catching cases single-event mode misses. Does not list negative cases or alternatives, but 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?

The description goes well beyond the annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) by detailing the internal process: scanning top markets, grouping by asset, fetching price history once, computing model probability, ranking by edge, and returning top N with suggested direction. It also mentions the model (lognormal from FRED + live coinpaprika) and version (V1). No contradictions with annotations.

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

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

The description is a single paragraph that efficiently covers purpose, method, and output. It is longer but every sentence contributes meaningful information. It is front-loaded with the main action and then provides technical details. Could be slightly shorter, but overall well-structured.

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

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

No output schema exists, so the description must explain return values. It states 'Returns top N ranked by edge magnitude with suggested trade direction' but lacks specifics on output fields (e.g., market name, asset, edge percentage, direction). Given the complexity of the data, more detail would be beneficial. Score reflects adequate but not comprehensive coverage.

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 all three parameters described (limit, window, min_edge_pp). The description adds context on how these parameters are used in the ranking process, but does not substantially add meaning beyond the schema descriptions, which already specify defaults and ranges. 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 specifies the tool scans high-volume Polymarket markets, identifies where Pipeworx model data disagrees with market prices, and returns top ranked edges with trade direction. It explicitly states the scope (crypto-price bets, lognormal model from FRED + coinpaprika) and the output (ranked by edge magnitude). This is a specific verb+resource combination that distinguishes itself from 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 explicitly states the intended use: 'Built for the "what should I bet on today" question — agents/users discover opportunities without paging through hundreds of markets by hand.' It provides clear context for when to use, but does not include explicit when-not-to-use or alternatives, leaving some ambiguity relative to sibling tools.

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

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

Annotations indicate read-only, idempotent, and non-destructive behaviors. The description adds value by detailing the computation of spread, return of leg-by-leg prices, and the spread in percentage points. No contradictions with annotations.

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

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

The description is well-structured with clear sections for modes and return values. It is slightly lengthy but every sentence adds value, making it efficient for the complexity of the tool.

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

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

Given the tool's complexity (two modes, cross-venue spread, three optional parameters), the description covers essential use cases and return structure. No output schema, but the description compensates by describing the output. Could include an example call or more detail on output format, but sufficient.

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

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

Schema description coverage is 100% for all three parameters. The description further explains the two operating modes and how parameters interact (e.g., explicit tickers override topic-mapped ones), adding meaning beyond the schema.

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

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

The description clearly states it computes cross-venue spread between Kalshi and Polymarket, a specific resource and action. It distinguishes from siblings like 'polymarket_arbitrage' by focusing on this specific pair. The two modes (topic and explicit) are clearly outlined.

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 two modes for use: topic shortcuts for common events and explicit tickers for custom pairings. It provides context for when each is appropriate but does not explicitly state when not to use the tool or list alternatives, though it does differentiate from similar tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it retrieves or lists memories, works across sessions, and requires a key for retrieval (omit for listing). However, it lacks details on error handling (e.g., if key doesn't exist), return format, or performance limits. The description doesn't contradict annotations (none 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 front-loaded with the core functionality in the first sentence, followed by a concise usage guideline. Every sentence earns its place by providing essential information without redundancy, making it efficient and well-structured.

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

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

Given the tool's low complexity (1 optional parameter, no output schema, no annotations), the description is mostly complete: it covers purpose, usage, and parameter semantics. However, it lacks details on return values or error cases, which would be helpful for an agent. Since no output schema exists, some gap remains, but it's adequate for this simple tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: it explains that omitting the key lists all memories, and ties the parameter to retrieving context from earlier sessions. This enhances understanding of the parameter's role, justifying a score above baseline.

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

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

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'), and distinguishes it from siblings like 'remember' (store) and 'forget' (delete). It explicitly mentions retrieving context saved earlier in sessions.

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

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

The description provides explicit guidance on when to use this tool: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It also specifies context: 'Use this to retrieve context you saved earlier in the session or in previous sessions,' clearly indicating its role versus alternatives like 'remember' for storage.

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?

Describes the parallel fan-out to SEC EDGAR, GDELT, USPTO for company type. Mentions return format (structured changes + count + URIs). Without annotations, this provides substantial behavioral insight, though lacks detail on rate limits or error handling.

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

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

Four sentences, each serving a distinct purpose: purpose, parameter details, return format, and usage guidance. No wasted words; information is efficiently packed.

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 and moderate complexity (3 parameters, fan-out logic), the description covers purpose, parameters, behavior, and use cases thoroughly. It provides enough context for an agent to select and use this 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 description adds significant value: clarifies 'since' accepts ISO dates and relative strings (e.g., '7d', '30d'), specifies 'value' can be ticker or CIK, and confirms 'type' only supports 'company'. 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?

Description clearly states 'What's new about an entity since a given point in time' and specifies the fan-out behavior for company type. It distinguishes itself from siblings like entity_profile (current state) and compare_entities (comparison), making its unique purpose evident.

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 suggests use cases: 'brief me on what happened with X' or change-monitoring workflows. While it doesn't explicitly state when not to use it, the context is clear enough given sibling tools.

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

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

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it explains persistence differences (authenticated vs. anonymous sessions with 24-hour limit) and the tool's purpose for cross-call context. However, it doesn't mention potential limitations like storage capacity or error conditions.

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

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

The description is perfectly concise with two sentences that each earn their place: the first states the core purpose, and the second adds crucial behavioral context about persistence. No wasted words, and information is front-loaded appropriately.

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 2-parameter tool with no annotations and no output schema, the description provides good context about what the tool does and its persistence behavior. However, it doesn't explain what happens on success/failure or return values, leaving some gaps in 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 description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.

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

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

The description clearly states the tool's purpose with specific verb ('Store') and resource ('key-value pair in your session memory'), and distinguishes it from siblings by specifying its unique storage function compared to tools like 'recall' (likely retrieval) and 'forget' (likely deletion).

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 ('save intermediate findings, user preferences, or context across tool calls'), but doesn't explicitly mention when not to use it or name specific alternatives among sibling tools 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?

No annotations are provided, so the description must disclose behavioral traits. It states the function (resolve to canonical IDs) and expected outputs (ticker, CIK, name, URIs), but does not mention idempotency, error handling, required permissions, or whether it modifies data. The version note ('v1') is mentioned but not explained.

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, fitting in a single sentence with additional details about the version and examples. It is front-loaded with the primary purpose. However, the version information could be structured more clearly, and the list of outputs is inline rather than bulleted.

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 moderate complexity (2 parameters, no output schema), the description covers the core functionality well: what it does, inputs, outputs, and that it replaces multiple calls. However, it lacks details on what happens when an entity is not found, the format of resource URIs, and any limitations or edge cases. Since there is no output schema, more information about the return structure would 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 description coverage is 100% for both parameters. The description adds examples of valid values (e.g., 'AAPL', '0000320193', 'Apple') and clarifies that only 'company' is supported in v1, which adds meaning beyond the schema's enum and description. This helps the agent understand parameter usage better than the schema alone.

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

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

The description clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources in a single call. It specifies the supported entity type ('company') and the input formats (ticker, CIK, name). It distinguishes itself from sibling tools like search_laureates by focusing on entity resolution and explicitly mentions it replaces multiple lookup 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?

The description implies the tool should be used for entity resolution when you have a ticker, CIK, or company name, and mentions it replaces 2-3 lookup calls, suggesting it is more efficient. However, it does not explicitly state when not to use it or provide comparisons to sibling tools like ask_pipeworx that might also perform lookups.

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?

Describes the internal probing with ai_visibility_check, ranking by score, and output metrics (score, confidence, signal density). Annotations already cover read-only and idempotent behavior; description adds process and output details.

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

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

Four sentences, front-loaded with main purpose, followed by method, use case, and output. No redundant or overly verbose content.

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?

Fairly complete: explains purpose, methodology, input (entities as subject + competitors), output structure. Lacks explicit ordering direction (descending by score) and error handling, but sufficient for a wrapper 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?

Schema covers all parameters with descriptions. Description adds context that the first entity is the subject and explains the models parameter options, providing meaning beyond the schema.

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

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

Clearly states 'Compare AI visibility across multiple entities side-by-side,' specifying the action, resource, and method. Differentiates from sibling 'ai_visibility_check' by being a comparative, multi-entity wrapper tool with ranking.

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 positions for competitive AI-marketing audits with an example question. Does not state when not to use or direct alternatives, but 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns 'biography, prizes won, and motivation,' which adds some context about output content. However, it lacks details on critical behaviors like pagination, rate limits, error handling, or whether searches are case-sensitive/fuzzy. For a search tool with zero annotation coverage, this leaves significant gaps.

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

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

The description is a single, efficient sentence that front-loads the core functionality ('Search Nobel Prize laureates by name and/or prize category') and follows with output details. There's no wasted verbiage, but it could be slightly more structured (e.g., separating usage from output).

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

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

Given the tool's moderate complexity (search with two optional parameters), no annotations, and no output schema, the description is minimally adequate. It covers what the tool does and what it returns, but lacks context on behavioral traits, error cases, or sibling tool differentiation. Without an output schema, the description's mention of return content ('biography, prizes won, and motivation') is helpful but not fully compensatory.

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 input schema already fully documents both parameters (name and category) with descriptions and examples. The description adds no additional parameter semantics beyond what's in the schema—it merely restates the parameters without providing extra context like format nuances or interaction effects. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Search Nobel Prize laureates by name and/or prize category.' It specifies the verb ('Search') and resource ('Nobel Prize laureates'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'get_prizes_by_year' (which appears to search by year rather than name/category), so it doesn't reach the highest 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 provides no guidance on when to use this tool versus alternatives. It mentions the parameters (name and category) but doesn't explain scenarios where this search is preferred over the sibling 'get_prizes_by_year' or other potential methods. There's no mention of prerequisites, limitations, or typical 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?

No annotations provided, but the description discloses supported domain, data sources, return values, and version limitations. It does not mention destructive behavior or auth, but for a read-only fact-check tool this is adequate.

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

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

The description is extremely concise (3 sentences), front-loads the core purpose, and adds value without redundancy.

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

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

For a tool with one parameter and no output schema, the description is comprehensive: it explains what it does, its limitations, return values, and value proposition.

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

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

Schema coverage is 100% with a single parameter 'claim'. The description provides examples and context beyond the schema, helping the agent understand the expected input format.

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 the domain (company-financial claims for US public companies), and lists return values. It distinguishes itself from siblings by being a specialized fact-checker.

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 (fact-checking claims, especially company-financial) and mentions it replaces multiple agent calls, but does not explicitly state when not to use or compare to siblings.

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