Wakatime

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

Annotations already indicate readOnly, idempotent, openWorld, non-destructive. The description adds that Workers AI is free and Anthropic requires a BYO key, and that results include per-model scores. No contradictions; it builds on annotations with important usage 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?

Three sentences, each serving a purpose: what it does, default vs Anthropic, and use cases. No redundant words, front-loaded with the primary action.

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

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

Given no output schema, the description includes the return structure (per-model fields and combined view). Covers purpose, parameters, use cases, and default behavior. Could mention error handling or rate limits but is sufficient for an agent.

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

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

Schema coverage is 100%, but the description adds value by explaining the default model, the role of '_apiKey' (BYO for Anthropic), and the purpose of 'context' (disambiguation). This goes 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 uses specific verb 'Probe' and resource 'LLMs for what they know about a business/brand/product/topic', clearly stating the tool's purpose. It distinguishes from sibling tools like 'compare_entities' and 'validate_claim' by focusing on visibility scoring per model.

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 concrete use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It mentions default model and the need for an API key for Anthropic, guiding proper usage. It lacks explicit when-not-to-use but is clear on 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?

The description discloses key behaviors beyond annotations: routing to 2,789 tools across 604 verified sources, filling arguments, and returning structured answers with stable citation URIs. This adds significant context about the tool's internal mechanism, complementing the readOnly and idempotent hints.

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 key instruction and provides a comprehensive list of examples. While it is relatively long, every sentence serves a purpose. It could be slightly more concise, but the detail is valuable for agent understanding.

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 broad scope and lack of output schema, the description adequately explains the tool's capabilities, input, and output format (structured answer with citations). It does not detail error handling or limitations, but the examples and routing explanation provide sufficient completeness for most use cases.

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

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

The sole parameter 'question' has a schema description, but the tool description provides rich context about what types of questions are appropriate (e.g., 'current US unemployment rate', 'Apple's latest 10-K'). This adds semantic value beyond the schema's 'natural language' note, helping the agent formulate effective queries.

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 answer factual queries about current or historical data across many domains. It uses a specific verb ('ask') and resource ('pipeworx'), and distinguishes itself from web search and sibling tools by positioning as the preferred choice for authoritative structured data.

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

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

The description explicitly advises when to use the tool ('PREFER OVER WEB SEARCH') and provides extensive examples of query types. It lacks explicit exclusions but offers strong positive guidance, making it clear when to invoke this tool over 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?

Beyond annotations (readOnly, idempotent, openWorld), the description reveals key behaviors: market resolution, bet classification, fan-out to appropriate data packs, and generation of an evidence packet with model comparison. It also explains the depth and include_raw options' effects, aligning with annotation hints.

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 purpose first, then usage, then behavioral detail. It is not overly verbose, but some internal details (fan-out logic) could be condensed. The front-loading of intent is effective.

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

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

Given no output schema, the description adequately explains the return (evidence packet plus comparison). It covers input, process, and output for a complex multi-source tool. However, it omits specifics of the evidence packet structure or model comparison format, which might require further exploration.

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 three parameters have schema descriptions (100% coverage). The description adds value by providing examples for 'market' (slug, URL, question text), clarifying 'depth' options ('quick' vs 'thorough'), and explaining 'include_raw' trade-offs (summary vs full payloads with size estimates), which aids selection.

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

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

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data, with specific input types (slug, URL, question) and outputs (evidence packet, market-vs-model comparison). It distinguishes itself from siblings like polymarket_arbitrage by being the core demo product that consolidates data.

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

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

Explicitly lists three use cases ('should I bet on X?', 'what does the data say?', 'is there edge?') and positions itself as the preferred tool over needing to discover packs individually. However, it does not explicitly state when not to use it or name alternatives beyond implication.

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 idempotentHint=true, so the description carries a lower burden. However, it adds no behavioral details like pagination, default sorting, or what 'recent' means, which are not disclosed in annotations.

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

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

The description is extremely short (4 words), but conciseness should not sacrifice essential information. It underspecifies the tool and does not earn its place by adding value beyond the name.

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 5 parameters with 0% schema coverage and an existing output schema, the description fails to provide necessary context. It leaves gaps in understanding required inputs and return values, making the tool incomplete for an agent.

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

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

With 0% schema description coverage, the description must explain parameters but does not. It fails to clarify what 'project', 'user_id', 'author', 'branch', or 'page' represent, making the tool hard to use correctly.

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 'Recent commits for a project.' clearly identifies the resource (commits) and action (retrieve), but lacks specificity about the source or scope. It's not a tautology but is vague compared to sibling tools.

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

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

No guidance on when to use this tool versus alternatives like 'durations' or 'heartbeats'. The description does not mention prerequisites or exclusions, leaving the agent to infer usage from 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 already declare readOnlyHint=true. Description adds data source specifics (SEC EDGAR/XBRL, FAERS, FDA, clinicaltrials.gov) and notes it returns citation URIs. No contradiction; provides additional 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?

Informative and front-loaded, but slightly longer than necessary. Every sentence adds value, though could be tightened slightly without loss.

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, describes return as 'paired data + pipeworx:// citation URIs'. Covers entity types, data sources, number range, use cases, and efficiency benefit. 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 coverage is 100% with good parameter descriptions. Description adds value by explaining what each type pulls (financials vs. adverse events/approvals/trials) and provides examples for values. Fully compensates any gap.

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 it compares 2-5 companies or drugs side by side, with clear verbs and resources. Distinguishes from sibling tools like entity_profile (single entity) and lists concrete use cases.

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 when-to-use patterns (e.g., user says 'compare X and Y', 'X vs Y', tables/rankings). Mentions both entity types and their data sources. Does not explicitly exclude single-entity comparisons, 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?

Annotations (readOnlyHint, openWorldHint, idempotentHint) already cover the safety profile. The description adds no behavioral details beyond 'profile', such as what fields are returned or if the data is cached. It relies entirely on 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, concise sentence with no wasted words. It is appropriately sized for a tool with no parameters.

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 no parameters and an output schema exists, the description is minimally adequate. It identifies the resource, but could be more specific (e.g., 'returns name, email, and role'). Still, it achieves basic 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?

No parameters exist, so the description does not need to add meaning. The schema coverage is 100%, meeting the baseline for this dimension.

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 'Authenticated user profile' clearly indicates the resource (profile of the current user) and implies retrieval, distinguishing it from sibling tools like 'user' which might be for other users. However, it lacks an explicit verb like 'get' or '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?

No guidance on when to use this tool versus alternatives. The description does not provide context about its typical usage or exclusions, leaving the agent to infer based solely on the name.

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=true, destructiveHint=false, idempotentHint=true. The description adds that it returns top-N most relevant tools with names and descriptions. No contradictions, but the description adds minimal behavioral context beyond the 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 somewhat lengthy with a list of domains that could be trimmed. It front-loads the main purpose but includes an exhaustive list that may not be necessary. 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?

No output schema exists, but the description mentions it returns 'top-N most relevant tools with names + descriptions.' This is adequate but minimal; more detail on ranking or how to interpret results 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%, so baseline 3. The description includes example queries ('analyze housing market trends'), which adds some value but does not significantly augment the schema definitions.

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 finds tools by describing data/task, listing many domains (SEC filings, financials, etc.). It distinguishes from sibling tools by specifying 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).'

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?

It explicitly says 'Use when you need to browse, search, look up, or discover what tools exist for...' and provides examples. It also advises calling this FIRST when browsing. Though it doesn't explicitly state when not to use it, 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 readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context beyond the name. No mention of output format or data nature.

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

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

Extremely concise but under-specifies; it is not efficient front-loading of essential information, as it omits critical details.

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 8 parameters, no schema descriptions, and an output schema without explanation, the description is far from complete. It fails to provide a functional understanding.

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

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

With 0% schema coverage and 8 parameters, the description provides no parameter explanations. Undocumented parameters like 'branches' and 'slice_by' are left ambiguous.

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 'Durations for a date' indicates the resource and scope, but lacks a verb. It is minimally clear, yet not tautological.

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

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

No guidance on when to use this tool versus siblings like 'commits' or 'heartbeats'. Missing any context on selection.

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 cover readOnlyHint, idempotentHint, and destructiveHint. The description adds 'Public' which implies no authentication, but this is consistent with annotations. No significant extra behavior disclosed.

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 words, no wasted text. Front-loaded and direct.

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?

With no parameters and an output schema, the description is minimal but sufficient to convey the tool's purpose. However, it does not detail what 'stats' include, which might require consulting the output schema.

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

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

No parameters exist, so schema coverage is 100%. The description need not add parameter info, and the baseline is 4.

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

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

The description 'Public editor stats' clearly indicates the tool returns statistics about editors, with 'Public' suggesting open access. It distinguishes from sibling 'stats' by specifying 'editor', but lacks an explicit verb.

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

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

No guidance on when to use this tool versus alternatives like 'stats' or 'meta'. The description does not provide context for selection.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context: what data is returned (SEC filings, financials, patents, news, LEI) and the inclusion of pipeworx:// citation URIs. 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?

Two sentences with no wasted words. The first sentence states the purpose and use case; the second lists return contents. Efficient and well-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 complexity (aggregating multiple sources) and no output schema, the description adequately lists return types and mentions citation URIs. It provides sufficient context for an agent to 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?

Schema description coverage is 100%, so baseline is 3. The description adds value by clarifying that 'value' accepts ticker or zero-padded CIK and explicitly states names are not supported, directing to resolve_entity. This extends 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: 'Get everything about a company in one call.' It provides specific use case examples (e.g., 'tell me about X', 'brief me on Tesla') and distinguishes itself from sibling tools by noting it replaces calling 10+ tools across various sources.

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 (when a user asks for company profiles or when many tool calls would be needed) and when not to use it (names not supported—use resolve_entity first). This provides clear decision criteria.

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 destructiveHint=true and idempotentHint=true. Description adds 'Delete' which matches, but does not elaborate on permissions, reversibility, or other behavioral traits beyond what annotations provide.

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

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

Two sentences with no waste, front-loaded with the action. 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?

For a simple tool with one parameter and no output schema, the description covers purpose, usage, and relationship to siblings completely.

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 no additional meaning beyond the schema's 'Memory key to delete'. Baseline 3 is appropriate.

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

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

The description clearly states 'Delete a previously stored memory by key', specifying a verb (Delete) and resource (memory by key), and distinguishes from sibling tools like remember and recall.

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 when-to-use scenarios: 'when context is stale, the task is done, or you want to clear sensitive data'. Lacks explicit when-not-to-use or alternatives but 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?

Annotations already indicate readOnly, idempotent, non-destructive behavior, and the description adds that the tool fetches the page, extracts title/description/key links, and emits standard llms.txt format. This fully discloses the operation without 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 concise with no redundant information; it front-loads the main purpose and follows with a bullet list of use cases. Every sentence serves a clear function.

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, two well-documented parameters, and no output schema, the description provides sufficient context: it explains the output format and intended usage scenarios. No additional details are necessary for an AI agent to use the tool effectively.

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

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

Input schema has 100% description coverage for both parameters (url and max_links), so the baseline is 3. The tool description does not add extra semantic meaning beyond the schema, but it also does not detract.

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 generates a production-ready llms.txt file for any URL, specifying the verb 'generate' and the resource 'llms.txt'. It distinguishes from sibling tools, none of which perform this specific task.

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 concrete use cases (getting a client's site indexed, drafting for own project, auditing competitor) which provide clear usage context. However, it does not explicitly state when not to use the tool or mention alternatives, though siblings do not compete, so no exclusions needed.

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 a read-only, non-destructive tool, but the description adds no behavioral details beyond that. It does not mention pagination, rate limits, or what the output contains, leaving gaps that annotations alone do not fill.

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 words is overly terse; the description sacrifices clarity for brevity. It is not appropriately sized as it omits essential information about the tool's function.

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?

With two parameters and an output schema present, the description is far too minimal. It does not hint at the output format or provide any examples, making it incomplete for effective 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 description coverage is 0%, yet the description does not explain any parameters. It fails to clarify that user_id is required or what page controls, so the parameter semantics are entirely unsupported.

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 'User's goals' is a fragment that states the resource but lacks a verb, making it unclear whether the tool lists, creates, or updates goals. It is slightly more than a tautology because it adds possessive scope, but it does not clearly define the tool's action.

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

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

No guidance is provided on when to use this tool versus alternatives like 'stats' or 'projects'. The description offers no context for selection or exclusion.

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, destructiveHint, idempotentHint, and openWorldHint. The description adds no behavioral traits beyond stating the resource type, missing details like return format or rate limits.

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

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

The description is very short (5 words), which is concise but at the expense of completeness. It could be improved by adding more detail without verbosity.

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 2 parameters and an output schema, the description is insufficient. It does not mention return values, pagination, or how to interpret raw heartbeats. The low schema coverage exacerbates the lack of 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 0%. The description only mentions 'date' but not 'user_id', failing to explain any parameter semantics. It does not compensate for the lack of 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 'Raw heartbeats for a date.' identifies the resource (heartbeats) and a filter (date), but lacks a verb like 'retrieve' or 'get'. It is somewhat clear but does not differentiate from sibling tools like 'durations' or 'commits'.

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

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

No guidance on when to use this tool vs. alternatives such as 'durations' or 'projects'. No explicit usage context or exclusions provided.

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

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

The description adds only the word 'Public' beyond annotations, which already indicate a safe, read-only, idempotent operation. The openWorldHint suggests external data but is not explained; behavioral details are minimal.

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 words), but this brevity sacrifices necessary detail. While not verbose, it under-delivers for a tool with three optional parameters.

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

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

The description is too minimal to be complete. It omits what the leaderboard ranks, how parameters affect results, and does not leverage the existing output schema (not shown). It fails to contextualize the tool's functionality sufficiently.

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 0%, so the description must explain parameters, but it only says 'Public leaderboard.' The parameters (page, language, country_code) are left completely unexplained, missing opportunities to clarify their impact.

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 'Public leaderboard' is vague and does not specify what the leaderboard ranks (e.g., users, projects, scores?) or what data it returns. It fails to distinguish itself from other listing tools among siblings.

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

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

No guidance is provided on when to use this tool versus alternatives like 'stats' or 'projects'. Conditions for use, prerequisites, or exclusions are absent.

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 it's a read-only, idempotent operation. The description adds no behavioral context beyond the annotations. It does not mention what the meta data includes or any side effects (none expected). This fails to add value beyond structured fields.

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 very short (3 words), but it is not information-rich. It is front-loaded but lacks necessary detail. Every word is present, but the description could be more informative without being verbose.

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

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

Despite having an output schema and zero parameters, the description is too minimal. It does not convey the purpose of the tool adequately. An agent might not know what 'API meta' refers to, especially among many sibling tools.

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?

There are zero parameters, and schema coverage is 100%. Baseline is 3 per instructions. The description does not need to explain parameters, but it adds no clarification about the tool's function.

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 'Current API meta' is vague; it doesn't specify what meta information is returned (e.g., version, endpoints). It lacks a specific verb and resource detail, making it hard to distinguish from other informational tools like 'stats' or 'current_user'.

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

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

No guidance on when to use this tool versus alternatives. Among siblings, there is no explicit context for when 'meta' is appropriate, such as 'use this to get API metadata' or 'prefer this over other tools for non-user-specific information'.

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?

Adds behavioral context beyond annotations: rate-limited to 5 per identifier per day, free, doesn't count against quota, and signal affects roadmap. Annotations provide no behavioral traits, so description carries full burden and does so well, though doesn't describe response behavior.

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

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

Concise at 5 sentences, front-loaded with purpose, and every sentence adds value. No redundancy or filler.

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?

Adequately covers usage and constraints given no output schema. Lacks mention of what the tool returns (e.g., success confirmation), but for a feedback submission tool this is minor. Still clear and complete enough.

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

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

Schema coverage is 100% and description adds further meaning: explains 'type' enum values with more detail, describes 'context' as optional structural context, and specifies 'message' constraints (plain text, 1-2 sentences, 2000 chars max).

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

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

The description uses specific verbs (tell, report) and resource (Pipeworx team) and lists clear categories (bug, feature, data_gap, praise). It distinguishes from sibling tools like ask_pipeworx by being exclusively for feedback.

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

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

Explicit when-to-use: when a tool returns wrong data (bug), when a desired tool is missing (feature/data_gap), or when something worked well (praise). Also includes what not to do (don't paste prompts) and mentions rate limits and quota.

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 valuable context: data source (CF analytics-engine), no PII, caching behavior (5min-1h), and the fact that it's derived from agent calls. This goes well beyond annotations and provides full 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 very concise: one sentence for core function, a bullet list of use cases, and two sentences for behavioral notes. All sentences add value, no redundancy, and the most important information comes first.

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 read-only tool with one parameter and no output schema, the description covers all essential aspects: what it returns, how it works, caching, data privacy, and usage scenarios. Nothing critical is missing.

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 clear enum and description. The description adds semantic value by explaining that shorter windows surface 'hot' data and longer windows show steady-state demand, which helps the agent choose appropriately. This extra guidance justifies a score above baseline 3.

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

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

The description clearly states it returns top tools, packs, and call volume over configurable windows. It also lists three specific use cases, making the tool's purpose very concrete. While it doesn't explicitly differentiate from siblings like discover_tools, the context of 'what other AI agents are calling' is distinct enough.

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 three explicit use cases that guide when to use the tool. It also explains the data is self-aggregating and cached, helping set expectations. It does not mention when not to use or contrast with alternatives, but the context is sufficient for appropriate invocation.

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, openWorld, idempotent, non-destructive. The description adds significant behavioral context: how it searches (walks child markets, groups related markets), how cross-event mode works, and what it returns (ranked opportunities with trade direction + reasoning). 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?

The description is well-structured and front-loaded with purpose. It uses clear sections for modes and examples. Slightly verbose in the cross-event explanation, 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?

No output schema exists, but the description explains return format (ranked opportunities with suggested trade direction and reasoning). It covers input, behavior, and output comprehensively for a search/analysis 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% with both parameters described. The description adds meaning by explaining the two modes, giving example values (slug, topic), and detailing when each parameter is used. It clarifies the mutually exclusive nature implicitly.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket by checking monotonicity violations. It distinguishes two modes (event and topic) and differentiates 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?

The description explains when to use each mode (event for single event, topic for cross-event) with concrete examples. It implicitly covers when-not-to-use by highlighting cross-event mode catches cases single-event misses, but it does not explicitly exclude other 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 already indicate read-only, idempotent, non-destructive behavior. The description adds significant context: the algorithm (scanning top markets, grouping by asset, fetching price history once), the return format (top N with trade direction), and scope limitations (V1 covers crypto-price bets). 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 concise (about 90 words) and front-loaded with the main purpose. Every sentence adds value, covering what it does, how it works, and intended use case. No unnecessary words.

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

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

Given moderate complexity, full schema coverage, and thorough annotations, the description is complete enough. It explains the algorithm and output, though it could specify the exact structure of returned opportunities. No output schema exists, so the description partially compensates.

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 detailed parameter descriptions, so baseline is 3. The tool description does not add new meaning beyond what the schema already provides for each parameter; it only gives overall context.

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

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

The description explicitly states the tool scans high-volume Polymarket markets and returns those where Pipeworx data disagrees most with market price. It differentiates from siblings like polymarket_arbitrage and polymarket_kalshi_spread by focusing on edge discovery for betting opportunities.

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 frames the tool for the 'what should I bet on today' question, giving clear context. It implies when to use (discovering opportunities) but does not explicitly state when not to use or mention alternatives. The V1 mention provides scope boundaries.

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

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

Annotations indicate readOnly and idempotent behavior, which the description does not contradict. The description adds value by detailing the return values (prices and spreads) and noting the real arb signal nature. It is transparent and consistent 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 concise at ~5 sentences, well-structured with clear sections for modes and output. Every sentence adds value without wordiness.

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

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

The description covers the purpose, usage, parameter interactions, and basic output structure. While it lacks a detailed output schema, it adequately describes the return values (prices, spreads). Given no output schema, this is sufficient for agent understanding.

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

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

With 100% schema coverage, baseline is 3. The description adds context beyond the schema by explaining the two modes, how parameters interact (topic vs explicit, overrides), and provides examples for explicit ticker/slug. This significantly aids parameter understanding.

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

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

The description clearly states the tool computes a cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two distinct modes (topic shortcuts and explicit ticker/slug) and distinguishes itself from siblings like polymarket_arbitrage by focusing on spread calculation.

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

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

The description explains when to use the tool (to capture arb signals from pricing differences) and describes the two modes of operation. It does not explicitly mention when not to use it or alternatives, but the guidance is sufficiently clear for agent invocation.

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, destructiveHint=false, and idempotentHint=true. The description does not add behavioral context beyond these, but it also does not contradict them. With annotations present, the bar is lower, and the description is minimally 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 terse, but this conciseness comes at the expense of informativeness. It consists of two words that add little value, failing to earn its place by conveying essential purpose or details.

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 2 parameters (one required) and an output schema, the description is grossly incomplete. It does not explain the function, how parameters affect results, or what the output represents. Even with annotations, it leaves too much to inference.

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 0%, so the description bears full responsibility for explaining parameters. It completely fails to clarify the meaning of 'q' (likely a filter) or that 'user_id' identifies the owner. No parameter semantics are provided.

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

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

The description 'User's projects' is a noun phrase that restates the tool name without specifying any action. It implies the resource is projects belonging to a user but lacks a verb, making purpose vague. Sibling tools like 'commits' and 'durations' have similar naming patterns, but no differentiation is provided.

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

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

No guidance on when to use this tool versus alternatives such as 'user' or 'summaries'. There is no mention of prerequisites, typical use cases, or exclusions. The description offers no context for selection.

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 (readOnlyHint=true, idempotentHint=true, destructiveHint=false) are consistent with the description. The description adds scoping details (anonymous IP, BYO key hash, account ID) and the behavior when key is omitted, which are beyond what annotations provide.

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

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

The description is two sentences and front-loaded with the main action. It is fairly concise but could be slightly more terse; however, it is not overly wordy.

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 annotations and schema, the description covers all necessary aspects: purpose, usage context, scoping, and relationship with sibling tools. No output schema exists, but the description sufficiently explains what to expect.

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

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

The schema has 100% description coverage for the single optional parameter 'key'. The description adds the important note that omitting the key lists all saved keys, providing meaning 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 tool retrieves a value saved via remember or lists all keys when the argument is omitted. It distinguishes from sibling tools like 'remember' and 'forget' by specifying retrieval behavior.

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 tool is for looking up previously stored context without re-deriving it. It mentions scoping to the agent's identifier and pairing with remember and forget, but does not explicitly state when not to use it 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?

Annotations already indicate safety (readOnly, idempotent). Description adds behavioral details: fans out to three sources, returns structured changes, total_changes count, and citation URIs. Explains since parameter formats.

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 two sentences with embedded examples. No wasted words. Front-loaded with use case and examples.

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 mentions return fields (changes, count, URIs). Lacks explicit mention of pagination or maximum range, but sufficient for monitoring queries.

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 has 100% coverage. Description adds practical guidance: type is always company, value can be ticker or CIK, since accepts ISO or relative with suggested defaults like '30d'.

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 answers 'What's new with a company in the last N days/months?' and provides multiple example queries. It differentiates from siblings by focusing on recent changes across multiple sources (SEC, GDELT, USPTO).

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

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

Description provides clear usage context with example queries and explains the since parameter. However, it does not explicitly state when not to use it or compare with 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 readOnlyHint=false (writes), idempotentHint=true (safe to repeat), destructiveHint=false. The description adds: 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours,' which is valuable context beyond annotations. No 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 a single, well-structured paragraph of about 80 words. It front-loads the purpose, then usage, then details. Every sentence adds value 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?

For a simple write tool with 2 parameters and no output schema, the description covers purpose, usage, behavior, and param semantics. It mentions related tools (recall, forget). No gaps for this complexity level.

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

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

Schema coverage is 100% with descriptions for key and value. The description adds examples of what to store (findings, addresses, preferences) but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later.' It specifies the resource (key-value memory) and distinguishes from siblings 'recall' and 'forget' by mentioning them explicitly.

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 usage guidance: 'Use when you discover something worth carrying forward,' and suggests pairing with 'recall' and 'forget'. It covers persistence behavior for authenticated vs anonymous users. It could be improved by stating when not to use it, but the guidance 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 readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false. The description adds value by stating that the tool returns IDs plus pipeworx:// citation URIs, which is behavioral context not covered by annotations. 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 concise—4-5 sentences—with no unnecessary words. It front-loads the purpose and ID systems, then provides examples and usage guidance. Every sentence adds value, 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?

Despite having no output schema, the description explains what is returned (IDs + citation URIs) and positions the tool in the workflow (use before other identifier-dependent tools). It covers both parameters thoroughly and provides examples, making it complete for a lookup tool given the richness of annotations and schema.

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

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

Schema coverage is 100% with descriptions for both 'type' and 'value' parameters. The description goes beyond the schema by providing concrete examples for each parameter (e.g., for 'value': ticker, CIK, name for company; brand/generic name for drug) and clarifying the enum options. This adds significant semantic meaning, justifying a score above the baseline of 3.

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: 'Look up the canonical/official identifier for a company or drug.' It specifies the exact ID systems (CIK, ticker, RxCUI, LEI) and provides concrete examples. This distinguishes it from siblings like entity_profile or compare_entities, which serve different 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: 'Use this BEFORE calling other tools that need official identifiers.' It also gives examples of user inputs (e.g., 'Apple', 'Ozempic') and notes that it 'Replaces 2–3 lookup calls,' implying it consolidates multiple lookups. This provides clear guidance on 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 already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds that it probes each entity with ai_visibility_check, ranks by score, and returns score/confidence/signal density, providing useful behavioral 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 (four sentences) and front-loaded with the primary purpose. Every sentence 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?

Given the annotations cover safety and schema covers parameters, the description explains the internal process (probing, ranking) and output fields (score, confidence, signal density). It is complete enough for an agent to select 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?

Schema coverage is 100% with comprehensive parameter descriptions, including that the first entity is treated as the subject. The tool description adds no significant parameter information beyond what the schema already provides, meeting the baseline.

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

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

The description clearly states it compares AI visibility across multiple entities side-by-side, using ai_visibility_check, ranking, and surfacing most/least recognized. It differentiates from sibling tool ai_visibility_check, which is a single-entity probe.

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

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

The description provides context for use in competitive AI-marketing audits with an example question. It implies when to use vs. single-entity probes but does not explicitly state when not to use or offer 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, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds no further behavioral 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 two-word description is extremely concise but not useful; it sacrifices necessary detail and does not earn its place due to lack of 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?

With 5 parameters, an output schema, and many sibling tools, this description is severely incomplete. It provides no insight into functionality, return values, or usage scenarios.

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

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

Schema description coverage is 0%, but the description offers no explanation of parameters (range, project, timeout, user_id, writes_only). It fails to compensate for the lack of 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 'Aggregate stats.' is vague, using a noun phrase without a verb. It does not specify what kind of stats or for which entity, leaving the tool's purpose unclear among 30+ siblings.

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

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

No guidance on when to use this tool versus alternatives like leaders, summaries, or durations. The description provides no context for selection.

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

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

Annotations indicate readOnlyHint=true, destructiveHint=false, and idempotentHint=true, but the description 'Daily summaries.' adds no behavioral context beyond what structured fields already convey. No 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?

Extremely short (two words) but under-specified, not earning its place as it provides minimal useful 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?

Despite having an output schema, the description is insufficient for a tool with 8 parameters and many siblings. It lacks any contextual 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 0% with 8 parameters. The description does not mention any parameter semantics, failing to compensate for the lack of schema documentation.

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 'Daily summaries.' is vague; it does not specify a verb or resource clearly. Among siblings like commits, durations, and heartbeats, it fails to differentiate its purpose.

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

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

No guidance on when to use summaries vs alternatives. The description provides no context on typical scenarios or exclusions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds no behavioral context beyond that. It does not explain what 'public profile' entails or any side effects.

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

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

Extremely concise at two words, but under-specified. It sacrifices necessary information for brevity, making it less helpful.

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 simple input and available output schema, the description is too minimal. It does not address the ambiguity among siblings or explain the return value, making it incomplete for effective 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 description coverage is 0%, so the description must compensate. It fails to do so; it does not mention the 'user_id' parameter or its format, leaving the agent to infer from the name 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 'Public profile.' indicates retrieving a user's public profile, which is clear but lacks specificity. It does not differentiate from sibling tools like 'current_user' or 'entity_profile', making the purpose somewhat vague.

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

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

No guidance on when to use this tool versus alternatives. The description offers no context about prerequisites, constraints, or when not to use it.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds that it uses SEC EDGAR + XBRL, returns a verdict with citation, and notes v1 scope. No contradiction. Provides useful behavioral 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 (5 sentences) with front-loaded purpose. Every sentence serves a purpose: definition, usage, scope, output details, and value proposition. 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 single-parameter tool with no output schema, the description fully explains the output (verdict, extracted form, actual value, citation, percent delta) and limitations (v1, US public companies). It's complete for an agent to understand usage and expectations.

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 single 'claim' parameter is described in the schema with examples. The description adds context about the type of claims supported (company-financial) and expected formats, going beyond the simple schema description. With 100% schema coverage, baseline is 3; description adds significant value.

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 clear verbs ('fact-check, verify, validate, or confirm/refute') and specifies the resource ('natural-language factual claim'). It distinguishes itself by focusing on company-financial claims via SEC EDGAR + XBRL, which is unique among sibling tools like 'ask_pipeworx' or 'bet_research'.

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 usage guidance: 'Use when an agent needs to check whether something a user said is true' with example queries. Mentions replacement of multiple sequential calls. Could explicitly mention when not to use or cite alternatives, but still clear.

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