Mercury Number

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

Annotations already indicate read-only, idempotent, etc. Description adds return format (per-model {score, confidence, signals, raw_response}) and clarifies API key handling and costs, enhancing behavioral understanding 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?

Description is front-loaded with the main action in the first sentence. It is concise (3 sentences) without redundancy, covering purpose, usage, and output efficiently.

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 4 parameters and no output schema, the description covers purpose, when to use, parameters, output format, and limitations (BYO key). Annotations provide additional safety context, making it complete.

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

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

Input schema has 100% coverage. Description adds context for each parameter, such as defaults and optional usage (e.g., context param disambiguates), going beyond the schema's brief 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 the tool probes LLMs for brand visibility and scores them, with specific verb 'probe' and resource 'LLMs'. It distinguishes from siblings like 'compare_entities' and 'scan_competitor_ai_presence' by focusing on visibility scoring.

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?

Provides clear context for usage: AI-marketing audits, pre-launch checks, competitive monitoring. Mentions default model and condition for using Anthropic. However, no explicit exclusions or comparison to alternatives.

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

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

No annotations exist, so the description must cover behavioral traits. It explains that the tool automatically picks the right tool and fills arguments, which is useful. But it doesn't disclose side effects, latency, or if the tool may fail for unsupported questions.

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 plus examples. It is front-loaded with the core purpose. However, the examples add length and could be considered slightly excessive for such a simple 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 simplicity (one param, no output schema, no nested objects) and no annotations, the description adequately covers its behavior and usage. It explains what the tool does and how to use it, leaving little ambiguity.

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 'question'. The description adds meaning by clarifying the parameter is natural language and showing examples, but doesn't add much beyond what the schema's description already 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 accepts a natural language question and returns an answer from the best data source. It distinguishes itself from sibling tools by its general question-answering role rather than specific operations like discover_tools or remember.

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 to just describe what you need without browsing tools or learning schemas, and includes three diverse examples. However, it doesn't specify when NOT to use it or mention alternatives among siblings.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds behavioral context such as 'fans out to the right packs', 'returns an evidence packet plus a simple market-vs-model comparison', and explains the tool's internal logic, which goes beyond the annotations without contradiction.

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

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

The description is moderately lengthy but well front-loaded with the core action. It includes use cases and a value proposition. Some redundancy exists (e.g., repetition of market input formats from schema), so it could be more concise.

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

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

Given the tool's moderate complexity and lack of output schema, the description adequately explains input options and general output ('evidence packet plus market-vs-model comparison'). However, it omits details on output structure, error handling, or edge cases, making it slightly incomplete.

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 coverage is 100% with descriptions for both parameters. The description largely reiterates the schema info (e.g., 'Pass a market slug...'). It adds some context about depth ('quick = 2-3 evidence sources, thorough = full fan-out') but does not significantly enhance 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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb (research), resource (Polymarket bet via Pipeworx), and method (one call), which distinguishes it from sibling tools like ask_pipeworx 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?

The description provides explicit use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It implies when to use but does not explicitly state when not to or list alternative tools, though the context of sibling tools 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?

No annotations provided; description carries full burden. Discloses return data (paired data + URIs) and replaces sequential calls. Does not mention error handling, rate limits, or permissions.

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 that front-load the key action and data. Every word serves a purpose, 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?

No output schema, but description covers return data adequately. Could mention potential limitations (e.g., data freshness, error scenarios) but sufficient for a comparing 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 coverage is 100%, but description adds value with examples for both entity types, clarifying what each 'values' array should contain (tickers/CIKs for company, drug names for drug).

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?

Clear verb ('Compare'), specific resource ('2–5 entities'), and distinct action ('side by side in one call'). Differentiates from siblings by explicitly stating it replaces 8–15 sequential agent 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?

States when to use (comparing entities) and notes efficiency gain over sequential calls. Lacks explicit when-not or alternative tools, 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 full burden. It explains the tool returns a list of tools with names and descriptions, which is a core behavioral trait. It doesn't detail sorting or ranking behavior, but the purpose is clear. No contradictions with missing 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?

Three sentences, all meaningful: explains what it does, what it returns, and when to use it. 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?

For a search/discovery tool with two well-documented parameters and no output schema, the description is complete. It tells the agent what to expect (list of tools with names and descriptions) and when to use it (first, when many tools are available).

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 no additional parameter information beyond what the schema provides, which is acceptable since the schema already describes both parameters with good detail.

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 'search' and resource 'tool catalog', clearly stating it returns relevant tools with names and descriptions. It distinguishes from siblings like 'ask_pipeworx' by being a tool discovery mechanism rather than a direct Q&A or memory 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?

Explicitly states 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task', giving clear when-to-use guidance. The name 'discover_tools' and sibling context imply it's the primary search tool, and the description reinforces that.

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

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

No annotations provided, so the description carries full burden. It discloses that the tool makes one call replacing 10–15 sequential calls and returns citation URIs. The read-only nature is implied by 'profile' but not explicitly stated. Adding an explicit statement about non-destructiveness would improve transparency.

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

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

The description is concise (~80 words), well-structured: overall purpose, detailed contents, return format, efficiency claim, and exclusion for federal contracts. Every sentence is informative 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 adequately explains return values (citation URIs). It covers limitations (only company, no names), mentions performance benefits, and references sibling tools. All relevant context for an agent to decide usage is present.

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

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

Schema coverage is 100%, and the description adds significant context: for type, it notes limitations ('Only company supported today'), and for value, it specifies accepted formats (ticker or CIK) and directs to resolve_entity for names, going well 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 returns a 'full profile of an entity across every relevant Pipeworx pack' and enumerates specific data sources (SEC filings, XBRL, USPTO patents, news, LEI). It mentions returning citation URIs, and distinguishes itself from siblings like usa_recipient_profile and resolve_entity.

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

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

The description explicitly instructs when to use this tool (comprehensive profile) and when not to (federal contracts → usa_recipient_profile). It also advises to use resolve_entity if only a name is available, providing clear guidance on alternatives.

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

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

No annotations are provided, so the description carries full burden. It indicates deletion (destructive action) but does not disclose any behavioral traits such as whether the deletion is permanent, whether it requires specific permissions, or whether it returns a confirmation.

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 conveys the essential information without any 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?

For a simple delete operation with one parameter and no output schema, the description is minimally complete. However, it could mention whether the operation is idempotent or what happens if the key does not exist.

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), and the description adds clarity by using 'by key' to specify the parameter's role beyond the schema's 'Memory key to delete'.

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 'Delete a stored memory by key', which is a specific verb ('Delete') and resource ('stored memory'), and it distinguishes from siblings like 'remember' (store) and 'recall' (retrieve).

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 does not provide guidance on when to use this tool versus alternatives, nor does it mention prerequisites or side effects. It only states the action, leaving the agent to infer usage context.

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

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

Annotations provide readOnly, openWorld, idempotent, non-destructive hints. The description adds behavioral context: fetches the page, extracts title/description/key links, and emits standard markdown format. This compounds transparency 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 a single paragraph of ~70 words, front-loading the main purpose, then details, then use cases. Every sentence adds value with 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?

For a simple 2-parameter tool with no output schema, the description covers purpose, inputs, process, output format, and use cases. It could mention error handling or URL validity, but given the context, it is sufficiently 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?

Both parameters are fully described in the schema (100% coverage). The description adds context about max_links default and max (already in schema) and mentions 'key links' but does not significantly enhance semantic 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 'Generate a production-ready llms.txt file for any URL' with specific verb and resource. It distinguishes from siblings by focusing on llms.txt generation, while siblings like ai_visibility_check and scan_competitor_ai_presence deal with broader AI visibility.

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: getting a client's site indexed, drafting for own project, or auditing competitor. It implies when to use but does not explicitly exclude alternatives or mention when not to use.

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

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

Annotations are empty, so the description carries full burden. It discloses that the number is based on Mercury's current zodiac position, returns both number and cosmic context, and implies randomness. No hidden side effects are expected, and the description is honest about the astrological justification.

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: two sentences that front-load the primary function, state the output, and provide usage guidance. No redundant or 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 that an output schema exists (though not shown), the description does not need to detail return values. It fully covers the tool's purpose, usage context, and parameter behavior, making it complete for an AI agent to decide invocation.

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

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

Schema coverage is 100% with the 'sign' parameter fully described (enum, optional, self-deprecating description). The tool description adds no additional parameter semantics beyond what the schema already conveys, meeting baseline expectations.

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 generates a random number (1-100) with astrological justification. The action 'Get' and resource 'random number' are specific. No sibling tool has overlapping functionality, making differentiation natural.

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

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

The description explicitly advises using this tool when a number with Mercury retrograde insights or cosmic reasoning is needed. While it does not list when not to use or specific alternatives, the narrow scope makes the guidance 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?

No annotations are provided, so the description carries the full burden. It discloses the rate limit of 5 messages per day and that it is free. It does not mention any side effects or return behavior, but the primary behavioral constraint is well stated.

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

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

The description is concise (3 sentences), front-loaded with purpose, and includes actionable guidelines and constraints. Every sentence adds essential information 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 feedback tool with 3 parameters and no output schema, the description is complete. It covers when to use, what to include, rate limits, and cost implication. No additional return value explanation is needed for a one-way feedback submission.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by instructing the user to describe what they tried and not include the verbatim prompt, which complements the schema's parameter descriptions. This extra guidance raises the score.

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

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

The description clearly states the tool's purpose: sending feedback to the Pipeworx team. It enumerates specific use cases (bug reports, feature requests, missing data, praise), distinguishing it from siblings which are for querying or system functions.

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

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

The description explicitly tells when to use the tool (for feedback) and what to include (describe what you tried) and exclude (end-user's prompt). It also mentions rate limiting and that it's free, providing clear guidance on usage expectations.

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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds valuable detail: self-aggregating from CF analytics, no PII, caching behavior (5min-1h). No contradiction with annotations.

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

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

Description is front-loaded with the core answer, followed by use cases, then technical details. No wasted words; each sentence adds value.

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

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

Given one simple parameter and no output schema, the description fully explains what is returned (top tools, packs, count) and caching. No gaps remain.

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

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

Schema covers the single parameter 'window' 100% with enum and description. Description adds context about shorter vs longer windows (hot vs steady-state), enhancing the schema meaning.

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

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

Clearly states the tool returns top tools, packs, and call volume over recent windows. Lists three specific use cases, distinguishing it from siblings like discover_tools and 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?

Explicitly provides three scenarios for use: discovering hot data sources, confirming canonical tool choice, and checking use case alignment. Does not mention when NOT to use it or explicitly 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?

Annotations already declare readOnly and openWorld hints. Description adds valuable behavioral context: walking child markets, extracting dates/thresholds, sorting, and returning specific trade suggestions. 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?

Four sentences, each essential. Front-loaded with purpose, then rule, then input/output. 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?

Covers the core logic, input, and output format. Lacks explicit mention of edge cases (e.g., no arbitrage found) but is sufficient for the tool's straightforward behavior.

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 covers 100% of parameters, providing clear input (event slug or URL). Description reinforces this but adds no new semantic depth 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?

Explicitly states the tool finds arbitrage opportunities via monotonicity violations on Polymarket events, with a concrete example and explanation of the rule. Clearly distinguishes from siblings like 'polymarket_edges'.

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

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

Describes when to use (for Polymarket events with multiple by-date/threshold markets) and what it does, but does not explicitly mention when not to use or compare to 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: the tool scans top markets, groups by asset, fetches price history once, computes model probabilities using a lognormal model from FRED and Coinpaprika, and ranks by edge. It also mentions returning suggested trade direction. This goes beyond annotations, though it could mention model limitations.

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

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

The description is well-structured with a clear opening statement that front-loads the main purpose. Subsequent sentences provide necessary detail without redundancy. It is slightly verbose 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?

Given the tool has 3 optional parameters, no output schema, and moderate complexity, the description explains the process (asset grouping, one-time price fetch, model computation) and output (top N ranked by edge with direction). It is sufficient for an agent to understand what it returns, though a more precise output structure could 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?

Schema coverage is 100% with descriptions for all three parameters (limit, window, min_edge_pp). The description does not add significant meaning beyond the schema; it mentions the default and max for limit but repeats schema info. No additional parameter context is provided.

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

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

The description clearly states it scans high-volume Polymarket markets and returns ones where Pipeworx data disagrees most with market price. The verb 'scan' and resource 'Polymarket markets' are specific, and the output 'top N ranked by edge magnitude with suggested trade direction' is well-defined. It distinguishes itself from siblings like 'polymarket_arbitrage' by focusing on edge discovery via model disagreement.

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 positions the tool for the 'what should I bet on today' question, guiding users to discover opportunities without manual paging. It provides clear context but does not explicitly list alternatives or when not to use it. Sibling tools exist (e.g., bet_research, polymarket_arbitrage), but no direct comparison is made.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds details on return values (leg-by-leg prices, spread in percentage points) and mode behavior, providing context 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 concise (~120 words), well-structured with clear sections for TWO MODES, and front-loaded with the core purpose. Every sentence adds value.

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

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

For a tool with no output schema and 3 optional parameters, the description adequately covers what it returns and how to use it. Minor gap: no mention of error handling for non-matching events, but overall complete.

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

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

Schema coverage is 100%, and the description adds significant value by explaining the topic values (with examples) and the override behavior for explicit tickers, going well beyond the 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?

The description clearly states the tool computes cross-venue spreads between Kalshi and Polymarket for the same event, with two explicit modes. The purpose is specific and distinct 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 explains two usage modes (topic shortcuts vs explicit tickers) and provides context on why the spread exists (different participant pools). However, it does not explicitly differentiate from sibling tools or state when not to use 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?

No annotations are provided, so the description must fully disclose behavior. It explains the two modes and the purpose (retrieve context), but does not mention any side effects, performance implications, or limits. However, the tool is a simple retrieval, so 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 with no wasted words. It front-loads the purpose and follows with usage guidance. Every sentence is essential.

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, no nested objects), the description is nearly complete. It could optionally mention that keys are case-sensitive or what happens if a key doesn't exist, but these are minor gaps.

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 defines the parameter well. The description adds value by explaining the semantic difference between providing a key (specific retrieval) vs omitting it (list all). This goes beyond the schema's description.

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

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

The description clearly states the verb 'retrieve' and resource 'memory by key', and distinguishes the two modes of operation: retrieving a specific key or listing all keys. This differentiates it from siblings like 'remember' and 'forget'.

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

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

The description explicitly tells when to use it: to retrieve context saved earlier in the session or previous sessions. It does not explicitly mention when not to use it or alternatives, 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?

No annotations provided, so description carries full burden. It reveals parallel fan-out to three sources, return structure (changes, count, URIs), and input formats. Lacks explicit mention of side effects but as a read-only retrieval tool, transparency 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?

Single paragraph packs purpose, behavior, parameter details, and output structure efficiently. No wasted words, though could benefit from bullet points for readability.

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

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

Covers all essential aspects: purpose, input parameters, return format, and use cases. Lacks error handling or edge cases, but is sufficient for agent decision-making.

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?

Adds significant value beyond input schema: explains type enum limitation, since accepts ISO/relative dates with examples like '30d', value accepts ticker or CIK. Provides usage guidance for since parameter.

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

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

The description clearly states the tool's purpose: aggregating recent changes for an entity from multiple sources (SEC, GDELT, USPTO) since a given time. It differentiates from siblings like entity_profile and compare_entities by focusing on temporal updates.

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 recommends use cases: 'brief me on what happened with X' or change-monitoring workflows. Does not state when not to use, but context implies it's for initial briefing rather than deep analysis.

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 correctly indicates this is a write operation and mentions persistence duration, but does not disclose potential side effects (e.g., overwriting existing keys) or whether there are limits on memory size or number of entries.

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 short, informative sentences. Front-loaded with the core purpose, then usage context, 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 simple schema (2 string params) and no output schema, the description covers the tool's behavior adequately. The only gap is the lack of details on overwrite behavior or capacity limits, but overall it's complete enough 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 schema already describes both parameters well. Description adds minimal extra meaning beyond examples of key usage. 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?

Clearly states the tool stores a key-value pair in session memory, with specific examples of use cases. Differentiates from sibling tools like 'forget' and 'recall' by describing its write 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?

Provides explicit guidance on when to use: saving intermediate findings, user preferences, or context across tool calls. Also distinguishes between authenticated (persistent) and anonymous (24-hour) sessions, which helps the agent decide based on user context.

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

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

No annotations, so description carries full burden. It discloses read-only behavior (resolve, no destructive actions), specifies input and output details. Lacks mention of error handling or idempotency, but sufficient for a simple lookup.

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 waste. Front-loaded with purpose, then details, then benefit. Efficient and clear.

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 low complexity, 2 parameters, and no output schema, description fully covers input, output, and value proposition. Complete for agent decision-making.

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 100%, but description adds concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and versioning info ('v1: type="company"'), enhancing semantic understanding 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 the tool resolves an entity to canonical IDs, specifies supported entity type (company), acceptable inputs (ticker, CIK, name), and outputs (ticker, CIK, name, resource URIs). Distinguishes from siblings by being a consolidated lookup replacing multiple 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 'in a single call' and 'replaces 2–3 lookup calls', guiding when to use. Contextually clear it's for entity resolution, but no explicit when-not-to-use or direct alternative naming.

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 signal read-only, idempotent, open-world. The description adds behavioral details: it probes each entity, ranks, and returns score/confidence/signal density. It does not contradict annotations and adds meaningful context beyond 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 a single focused paragraph with no redundant information. It front-loads the purpose and provides sufficient detail without exceeding length.

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 describes the return format (ranked list with score, confidence, signal density). It also explains the role of the first entity and optional parameters, making it complete 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?

Schema description coverage is 100%, so the schema already documents all parameters. The description does not add further meaning beyond what is in the schema, meeting the baseline expectation.

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

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

The description clearly states the tool compares AI visibility across multiple entities, uses ai_visibility_check, and ranks results. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the exact use case.

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 a clear use case ('competitive AI-marketing audits') and implies when to use this tool (multiple entities) vs. siblings (single entity). However, it does not explicitly exclude alternatives or mention when not to use it, which would warrant 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 the return format (verdict types, citation format) and notes it replaces manual steps. However, it lacks details on idempotency, rate limits, authentication requirements, or how authoritative sources are selected, which would enhance transparency.

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

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

The description is concise (two sentences), front-loaded with the core purpose, and every sentence provides useful information. No unnecessary words or repetition.

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

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

For a tool with one parameter and no output schema, the description covers the main aspects: input type, return values, and context of use. Slightly more detail on required claim format (e.g., natural language, but schema already says string) 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 coverage is 100% for the single parameter, and the description adds examples (e.g., 'Apple's FY2024 revenue was $400 billion'). The schema already provides a good description, so the description adds marginal value beyond 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?

The description clearly states the tool's purpose: to fact-check natural-language claims against authoritative sources. It specifies the supported domain (company-financial claims for US public companies) and the data sources (SEC EDGAR + XBRL). This distinguishes it from sibling tools, which are unrelated to fact-checking.

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 this tool: to replace 4-6 sequential agent calls, implying it streamlines a multi-step process. However, it does not explicitly state when not to use it (e.g., for non-financial claims or non-US companies) or list alternative tools for unsupported claims, leaving some ambiguity.

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