Cdc

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

Annotations indicate safe, idempotent read operations. The description adds behavioral context: default model (free) and optional Anthropic probe with BYO key (cost implication), plus output structure. No contradictions with annotations.

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

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

The description is a single, well-structured paragraph. The first sentence states the core purpose and output; the second covers model options; the third describes returns; the fourth lists use cases. Every sentence adds necessary information 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?

Despite lacking an output schema, the description details the return structure (per-model fields + combined view) and scoring range. It covers all 4 parameters, gives usage context, and addresses the open-world nature of the tool. No gaps remain for the agent to infer.

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 value by explaining default behavior for 'models', when '_apiKey' is needed, and providing examples for 'entity' and 'context'. This goes 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 uses a specific verb ('probe') and clearly identifies the resource ('LLMs') and output ('score visibility 0-100 per model'). It lists concrete use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) which implicitly distinguish it from the diverse 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?

The description explicitly states when to use the tool ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and provides context on default vs. paid models. However, it does not explicitly state when not to use it or name alternative 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description complements this by adding context about the tool's internal routing behavior, output format (structured answer with stable pipeworx:// citation URIs), performance (one fast call), and tier availability. 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 long but well-structured: it opens with a bold 'PREFER OVER WEB SEARCH' directive, then explains the tool, gives use examples, and closes with alternatives. Some redundancy exists (e.g., 'Use whenever...' and 'START HERE' overlap), but the key information is front-loaded and organized effectively.

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 and lack of output schema, the description is thorough: it covers scope (authoritative structured data), return value (structured answer with citation URIs), routing behavior, example queries, and explicit guidance on when to step up to sibling tools. It equips an agent to select and invoke the tool correctly in a wide range of factual 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 description coverage is 100% and all six parameters are aliases for the single 'question' field, fully documented in the schema. The description adds examples of valid questions but no additional parameter-level semantics beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states what ask_pipeworx does: routes questions to one of 5,529 tools across verified sources and returns structured answers with citation URIs. It distinguishes itself from siblings by explicitly naming alternatives like ask_pipeworx_grounded and deep_research and positioning itself as the default entry point.

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 guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and lists trigger phrases like 'what is', 'look up', 'find'. It also gives when-not-to-use/step-up alternatives, e.g., use ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad multi-part questions.

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 valuable context beyond the annotations: it explains the beta nature, the absence of an active candidate, that it is a full working router rather than a fallback, and that results are compared against the stable router. Annotations already declare read-only and idempotent hints, but the description provides current-state behavioral details that are not in 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 three sentences long, front-loaded with the key identity ('Beta version of ask_pipeworx'), and every sentence earns its place. It is concise, informative, and free of redundancy.

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

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

Given the tool's complexity as a beta variant, the description provides sufficient context: relationship to ask_pipeworx, current active state (no candidate), exact matching behavior, and the comparison purpose. It does not elaborate on the routing mechanics, but references to the stable version and same response shape make it reasonably complete 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?

The input schema covers all six parameters (question plus five aliases) with 100% schema description coverage, so the schema fully explains parameter semantics. The description mentions 'same arguments' but adds no additional meaning beyond what the schema 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 identifies this as the beta version of ask_pipeworx, explicitly stating it is an identical universal router with the same tools, arguments, and response shape. It distinguishes itself from the sibling tool ask_pipeworx by being the experimental edge where candidate routing improvements are testable.

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 provides explicit usage guidance: 'Use it exactly like ask_pipeworx when you want the newest routing,' and contrasts with the stable router. It also informs that it currently matches ask_pipeworx exactly, setting clear expectations for when to choose 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?

Annotations already declare read-only, open-world, idempotent, and non-destructive. The description adds substantial context beyond annotations: costs one extra LLM call, returns a specific evidence object with verbatim quote, and details refusal reasons like 'not_in_source' and 'data_truncated'. This is valuable behavioral disclosure that the annotations don't cover.

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?

Every sentence earns its place. The first sentence states purpose, the second explains mechanics, the third details return values and refusals, the fourth gives usage criteria, and the fifth notes cost and alternatives. No wasted words, and the structure effectively front-loads the core purpose.

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

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

The description is complete for a tool with no output schema. It specifies the success return object, refusal reasons, usage context, cost trade-off, and mechanism of grounding. Combined with rich annotations, an agent has everything needed to decide when to invoke this tool and 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?

Schema description coverage is 100%, so baseline is 3. The description doesn't need to elaborate on parameters since all six are aliases for 'question' and the schema already explains natural language input. The description adds no additional parameter-level meaning beyond what the schema provides.

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

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

The description clearly defines the tool as a 'hallucination-resistant answer mode' with a specific verb (extracts) and resource (tool result). It distinguishes itself from the sibling ask_pipeworx by stating 'Same routing as ask_pipeworx' but then adding the grounded extraction behavior. The contrast with casual lookups further clarifies its unique role.

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 guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also provides a when-not-to-use with 'prefer ask_pipeworx for casual lookups' and names the alternative. This fully covers usage boundaries.

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

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

Describes fan-out logic, response shapes, resolver contract, parent event extractor, news fields, safety checks, and resolution-rule risk. Exceeds annotation hints (readOnly, idempotent) with rich behavioral detail.

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?

Well-structured with labeled sections, but somewhat verbose. Every sentence adds value, but length could be trimmed slightly without losing clarity.

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?

Thoroughly covers parameters, outputs, edge cases (low-confidence, closed markets, wide spreads, resolution rules), and response fields. No output schema exists, so description compensates 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?

Enriches each parameter beyond schema: explains market input flexibility, depth options with defaults, and include_raw behavior with size implications. Examples clarify usage.

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

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

The description clearly states the tool researches Polymarket bets, specifies input formats (slug, URL, question text), and distinguishes itself from sibling tools by focusing on evidence packets for betting decisions.

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 use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'), classifiers, and extensive guidance on interpreting results, including warnings about low-confidence matches and resolution rules.

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 non-destructive. The description adds significant behavioral context: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric, and inclusion of citation URIs.

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 lengthy but every sentence serves a purpose. It is front-loaded with trigger phrases and structured logically. Minor opportunity to trim redundancy (e.g., 'off-calendar fiscal years handled correctly' 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?

Despite lacking an output schema, the description explains return format (paired data + citation URIs) and behavior (sorted by primary metric). For a comparison tool, all necessary context is provided, including efficiency gains (replaces 8-15 sequential lookups).

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 100% of parameters. The description enriches the 'type' and 'values' parameters by specifying exact data retrieved per type (revenue, net income, cash, debt for companies; adverse events, approvals, trials for drugs) and constraints (2-5 items, tickers vs names).

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 performs side-by-side comparison of 2-5 companies or drugs in a single call, distinguishing it from sequential lookups. Specific trigger phrases like 'compare X and Y' and 'head to head' are provided, making the purpose unmistakable.

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 instructions are given: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Examples of usage ('which is bigger / better / larger / more profitable' / 'rank these companies') further clarify when to invoke this tool.

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

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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses timing (15-60s up to ~90s), output structure (findings packet with gaps[], contradictions[]), citation behavior (only when source emits resolvable URI), the explicit 'never invented' guarantee, and the semantic excerpting behavior. This is rich, non-redundant behavioral context.

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

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

The description is long but densely packed with unique information. It is front-loaded with the account requirement and core functionality, then covers use cases, depth behaviors, and output details. Minor redundancy exists (e.g., 'not open-web search' implied twice), but every major 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 absence of an output schema, the description thoroughly explains return values (findings packet, gaps[], contradictions[], hop field, citation_uri) and edge cases (empty gaps for news topics, citations only when resolvable). It also covers prerequisites, updates, and performance, making it complete for a complex 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%, so the schema already documents both parameters. The description adds a few operational details like the paid plan for depth:thorough and expected latency, but these are more behavioral than parameter-defining. It does not materially clarify syntax or beyond what the schema's depth enum description covers.

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 states a specific verb ('research'), a specific resource ('Pipeworx's 1455 STRUCTURED data sources'), and a precise mechanism (decomposition into facets, parallel routing to 5,529 tools). It clearly distinguishes from siblings by explicitly saying 'this is NOT open-web search' and contrasting with ask_pipeworx.

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 provides explicit when-to-use guidance ('Best for broad/multi-part questions over structured data'), when-not-to-use ('For a single lookup use ask_pipeworx'), and alternatives for current news ('prefer ask_pipeworx'). It also states the account prerequisite and which depth requires payment.

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 readOnlyHint=true and idempotentHint=true. Description adds that results are 'ready to call directly, no second schema lookup needed', explaining output behavior beyond annotations. No contradictions. Adequate disclosure for a non-destructive discovery tool.

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

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

Two sentences: first covers purpose and use context, second details output format. No superfluous text. Front-loaded with key information. Ideal conciseness.

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

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

Given no output schema, description explains return structure (tool names, descriptions, schemas with examples). Lacks mention of pagination or limit's default/max, but those are in schema. Covers essentials for a discovery 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 descriptions for all parameters. Description does not add significant meaning beyond listing aliases for 'query'. Baseline 3 is appropriate since the schema itself is well-documented.

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 ('find', 'browse', 'search', 'look up', 'discover') and a clear resource ('tools'). It lists domains (SEC filings, FDA drugs, etc.) and explains returns (names, descriptions, schemas), making purpose unmistakable. The tool's discovery role distinguishes it from sibling tools that perform specific analyses.

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 when to use: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set'. Strong guidance on primary use case, though lacks explicit alternatives or when-not-to-use scenarios.

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, openWorldHint, idempotentHint, and not destructive. The description adds valuable context about fanning out across multiple sources, returning specific fields, and noting that patent data soft-fails due to API sunset. However, it doesn't mention rate limits or error handling 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?

The description is relatively long but well-organized, starting with example queries. Every sentence adds value, though some redundancy in listing data sources could be tightened.

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 lists all major output fields (CIK, filings, fundamentals, patents, news, LEI) and provides formatting details (e.g., URIs, sorting). However, the lack of an output schema means the agent must parse the informal structure, slightly reducing 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 covers both parameters with descriptions. The description adds context that only 'company' type is supported, value must be ticker or zero-padded CIK, and names are not supported, which is helpful 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 provides a 'full cross-source profile of a US public company'. It lists example queries, data sources, and output fields, distinguishing it from siblings like 'resolve_entity' and '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?

Explicitly states 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also specifies that names are not supported and directs to 'resolve_entity' for name resolution, providing clear when-to-use and when-not-to-use guidance.

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

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

Annotations already convey destructive and idempotent behavior; description adds no extra context beyond confirming deletion. 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?

Two efficient sentences, front-loaded with purpose, 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 simple one-parameter, no-output-schema tool with clear annotations, the description covers purpose, usage, and pairing 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 coverage is 100%, and the description does not elaborate on the 'key' parameter beyond what the schema states. Baseline 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?

Description uses specific verb 'delete' and resource 'memory', clearly distinguishing from siblings '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 use cases (stale context, task done, clear sensitive data) and mentions pairing with remember/recall, but lacks explicit when-not-to-use 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 readOnly, openWorld, idempotent, non-destructive behavior. The description adds valuable behavioral context: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' 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 and well-structured: one sentence on purpose, one on process, and one listing use cases. Every sentence adds value with no wasted words.

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

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

Given the simple inputs, no output schema, and comprehensive annotations, the description is complete. It explains inputs, process, output format, and use cases without 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 coverage is 100% with clear descriptions for both parameters. The description does not add significant new information beyond what the schema provides, so baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: generating a production-ready llms.txt file for any URL. It uses a specific verb ('generate') and resource ('llms.txt'), and it distinguishes itself from siblings like ai_visibility_check by focusing on creating the file rather than analyzing presence.

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 lists use cases: getting a client's site indexed, drafting for own project, or auditing competitors. It provides clear context on when to use but does not explicitly state when not to use or offer alternatives, though siblings are listed separately.

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 readOnly, openWorld, idempotent, non-destructive hints. Description adds specifics: up to 1000 rows, return format with row array and column names from first row, and limit defaults.

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 filler. Essential information front-loaded: main action then prerequisite. 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 full schema coverage, annotations, and presence of output schema, the description covers all necessary context: what the tool does, how to identify the dataset, and row limit. No 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 already describes both parameters (100% coverage). Description adds context: ID format example ('9mfq-cb36') and that first row yields column names, enhancing 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?

Clearly states 'Fetch up to 1000 rows from a CDC Socrata dataset by its four-by-four dataset ID', specifying verb, resource, and identifier format. Distinguishes from sibling search_datasets by mentioning it as a prerequisite.

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 instructs to use search_datasets first to find the dataset ID, providing clear when-to-use guidance. Also mentions default and maximum limit for rows.

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, and destructiveHint=false. The description adds behavioral detail by specifying the return format (fields) and that it lists 'active' subscriptions by default, with an option to include inactive. This complements 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?

Two sentences: first states action and output, second provides usage guidance. No wasted words, front-loaded with essential 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?

For a simple list operation with comprehensive annotations and a single optional parameter, the description is sufficient. It explains the purpose, output, and common use cases. No output schema exists, but the description lists returned fields.

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 description for include_inactive. The description adds meaning by stating 'active subscriptions' (implied default) and explaining the parameter's effect (include cancelled). This goes beyond listing the parameter.

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

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

The description explicitly states the tool lists the caller's subscriptions and enumerates returned fields (id, type, etc.), clearly defining its action and resource. It distinguishes from sibling tools like subscribe and unsubscribe.

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 advises using this tool to review current subscriptions before adding more or to find an id for cancellation, providing clear context. It does not explicitly state when not to use it, but the guidance is sufficient.

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

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

Annotations provide only basic hints (readOnly=false, etc.), but the description adds rich behavioral context: the claim_token mechanism for follow-up, the fact that feedback is read daily and affects roadmap, rate limiting ('5 per identifier per day'), and cost ('Free; doesn't count against your tool-call quota'). This goes far beyond what annotations convey.

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

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

The description is dense but every sentence serves a purpose: purpose, usage triggers, exclusions, token mechanics, roadmap impact, rate limits, and cost. No filler or redundancy. It is front-loaded with the core purpose and flows logically from when-to-use to how-it-works.

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 4 params, 0 required, nested objects, and no output schema, the description covers all necessary ground: usage scenarios, exclusions, token return, rate limiting, and how to follow up. It even explains what to avoid ('don't paste the end-user's prompt'). This is complete guidance for a tool of this complexity.

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

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

Schema descriptions already cover 100% of parameters, so baseline is 3. The description adds extra value by showing concrete usage with claim_token: 'pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})' and clarifies that it should be passed 'with no other arguments.' This enhances understanding beyond the schema alone.

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

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

The description opens with a specific verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' This clearly identifies the tool as a feedback channel and distinguishes it from sibling tools like ask_pipeworx or discover_tools. The title 'Send Pipeworx Feedback' reinforces the purpose without ambiguity.

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

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

The description gives explicit when-to-use guidance: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also provides a clear exclusion: 'ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server... file it with that server instead.' This is exemplary alternative handling.

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, etc.), the description discloses the data source (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h). This adds significant value.

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 concise sentences followed by a bulleted list of use cases. No redundant information; every sentence contributes meaning.

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

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

Given the tool has only one parameter, no output schema, and rich annotations, the description covers purpose, usage, and behavioral traits 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 context about window behavior ('shorter windows surface what's hot right now; longer windows show steady-state demand'), enhancing the schema's enum 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 explicitly states that the tool returns trending tools, packs, and call volume over recent windows. It uses specific verbs ('returns') and clearly distinguishes from siblings like 'discover_tools' by focusing on popularity metrics.

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?

Three concrete use cases are listed (discovering hot data sources, confirming canonical tool, aligning use case). It provides clear context but does not explicitly state when not to use or name 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?

The description discloses detailed behavioral traits beyond the annotations: it explains the scanning logic (monotonicity checks, partition sums, fill checks via CLOB depth), response structure (opportunities[] with gap_pp, suggested_trade, etc.), and edge cases (semantic anchor, placeholder filtering). It adds significant context beyond what the readOnlyHint and idempotentHint 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 relatively long but well-structured with clear sections for each mode, semantic anchor, partition filter, response, and fill check. Every sentence provides necessary information, though some redundancy could be trimmed. It is front-loaded with the core purpose and mode selection.

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

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

Despite no output schema, the description thoroughly explains the response structure and all relevant details (monotonicity context, partition check, fill check). It covers both user intents (single-event and cross-event) and links to a sibling tool for custom sizing. The description is complete given the tool's complexity and schema coverage.

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

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

The schema coverage is 100%, and the description adds rich meaning: it clarifies that 'event' accepts slug or URL and focuses on a single event, while 'topic' accepts a seed question for cross-event scanning. It explains the behavioral differences and provides examples, which goes well beyond the schema's parameter 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 that the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes: no-args trending scan, event-specific, and topic cross-event scan, which differentiates it from sibling tools like polymarket_edges or polymarket_fill_risk.

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

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

The description provides explicit guidance on when to use each parameter: no args for a trending scan of top markets, 'event' for a specific market (recommended), and 'topic' for cross-event scanning. It explains the advantages of cross-event mode and mentions a custom sizing alternative (polymarket_fill_risk), covering both when-to-use and 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?

The description goes far beyond the annotations (which already indicate read-only, idempotent, and open-world) by detailing three model families, edge calculation, knobs, response structure with diagnostics, and caching. It fully discloses behavioral traits.

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

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

The description is densely packed with information and front-loaded with the purpose, but it is a single long paragraph that could be better structured with bullet points or sections. Every sentence adds value, but readability could be improved.

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 all necessary aspects: purpose, model families, response fields (including by_segment and diagnostics), parameters, caching, and edge cases like Fed bets. It is complete even without an 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?

With 100% schema coverage, the description still adds significant value by explaining tradeable-edge filters, slippage context, and the rationale behind knob defaults (e.g., 'Polymarket has zero trading fees but bid/ask typically eats 20-50bp'). This enriches 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 scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, specifying the action and resource. However, it does not explicitly differentiate itself from sibling tools like polymarket_arbitrage or polymarket_edge_tracker, which could be considered similar.

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 ('what should I bet on today') and mentions caching behavior, but it does not specify when not to use this tool or suggest alternative tools. This limits guidance for the agent in selecting between 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?

Beyond the readOnly and other hints, the description details response fields (tracked, expired, snapshot_dates), their meanings, and computation approach (e.g., decay from daily closes, edge_pp_net signed by direction). This provides substantial behavioral context.

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

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

The description is fairly concise given the complexity, with clear sections for purpose, response structure, and limits. It could be slightly tighter, but 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?

Despite no output schema, the description thoroughly documents the response structure (tracked, expired, snapshot_dates) with key field explanations. It also covers data limits and sources, making it self-contained for agent usage.

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 description adds value over the schema by explaining the 'window' parameter as 'snapshot family' and providing explicit defaults and bounds (days default 14, max 30; window default '1wk'). Schema coverage was already 100%, but the description enhances clarity.

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: providing edge persistence and decay telemetry from daily polymarket_edges snapshots. It answers a specific question about edge longevity vs. width, distinguishing it from sibling tools 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 implicitly guides when to use (e.g., for historical edge analysis) and notes limits (60-day TTL). However, it does not explicitly contrast with siblings or state 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?

Adds significant behavioral context beyond annotations, including how the tool walks the ladder, fills details, identifies thin legs, and warns about forced directional risk.

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?

Well-structured with bold key terms and bullet-style formatting, front-loaded with purpose, every sentence adds value given the tool's complexity.

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

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

Despite no output schema, the description thoroughly explains return fields, edge cases like thin books and partial fills, and covers both modes comprehensively.

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 description coverage, the description still adds meaning by explaining modes, default behavior, and interpretation of size_usd across modes.

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 performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes with specific return fields.

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 advises using this tool before acting on any polymarket_arbitrage or polymarket_edges trade above ~$500, explaining why with concrete failure modes.

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

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

The description goes far beyond annotations by explaining safety fields (compatibility_warning, temporal_alignment, skipped counters) and their interpretations. It fully discloses conditions that make spreads meaningful, adding significant context.

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

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

The description is well-structured and front-loaded with the core purpose. While lengthy, every section adds value. Minor redundancy exists (e.g., repeating the topic list), but overall it is efficient.

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

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

Despite no output schema, the description thoroughly explains the response structure, safety fields, and edge cases. Combined with annotations, the agent has complete context to invoke this tool correctly.

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

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

With 100% schema description coverage, the baseline is 3. The description adds value by detailing the pre-mapped topic shortcuts and explaining how parameters interact (e.g., topic vs explicit tickers). This earns a 4.

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

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

The description clearly states it computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two modes and explains the output, distinguishing itself from sibling tools like polymarket_arbitrage.

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

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

The description provides clear context for when to use the tool (e.g., comparing spreads) and warns that most pre-mapped topics return compatibility warnings. However, it does not explicitly contrast with sibling tools like polymarket_arbitrage, leaving some ambiguity about 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, idempotentHint, destructiveHint. The description adds value by explaining the dual behavior (specific key vs. list all) and scoping. 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?

Three concise sentences that front-load the primary action, then provide rationale and scoping. No unnecessary 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 the single optional parameter, no output schema, and robust annotations, the description fully covers behavior, use case, and relationships with siblings. No 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 coverage is 100% and the schema description already explains the key parameter's dual role. The description restates this without adding new semantic depth, meeting the baseline for high coverage.

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

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

The description clearly states the tool retrieves a value saved via remember or lists all saved keys when key is omitted. It uses specific verb-resource pairs (retrieve/list, saved values/keys) and distinguishes itself from siblings 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?

Explicitly states when to use: to look up previously stored context without re-deriving. Notes scoping to user identifier. Implicitly contrasts with remember (save) and forget (delete), providing clear usage guidance.

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

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

Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds that mark_read modifies read status to control future results, which is a behavioral trait beyond annotations, and mentions polling suitability. However, there is a conflict: mark_read=true changes state contradicting readOnlyHint=true.

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, front-loaded with purpose, and each sentence adds value: purpose, return fields, filtering, mark_read behavior, polling hint, alternative endpoint. 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?

Despite no output schema, the description covers what the tool returns (source, citation_uri, payload) and addresses all key aspects: usage context, filtering, side effect of mark_read, polling, and alternative access. It is 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 parameter descriptions. The description adds no new parameter info but provides an example for type ('sec_8k') and explains mark_read's effect, reinforcing the schema. The description adds marginal value beyond schema.

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

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

The description clearly states the tool pulls fired events from the subscription feed, specifies return fields (source, citation_uri, payload), and distinguishes it as the tool for reading alert events from siblings like list_subscriptions.

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

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

The description implies usage for reading recent alerts and provides polling suitability and an alternative endpoint, but does not explicitly compare to siblings like list_subscriptions or state 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 declare readOnlyHint, idempotentHint, and destructiveHint. Description adds valuable behavioral context: fallback logic (GDELT→GNews), soft-fail for USPTO, parallel execution, and return format (changes[] grouped by source + citations). No contradictions with annotations.

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

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

Single paragraph with zero wasted words. Purpose and usage examples front-loaded. Every sentence earns its place, covering query patterns, data sources, parameter formats, and sibling differentiation.

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

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

Despite no output schema, the description clearly states the return structure (changes[] grouped by source + total_changes count + citation URIs). The tool's complexity (3 parameters, multiple data sources) is fully addressed. No gaps remain 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 is 100%. Description enriches each parameter: explains that 'since' accepts ISO date or relative shorthand with examples, clarifies 'value' can be ticker or zero-padded CIK, and notes 'type' only supports 'company'.

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 and resources: "change feed for a company," lists data sources (SEC EDGAR, GDELT→GNews, USPTO), and provides example queries. It clearly distinguishes from sibling entity_profile by advising when to use each.

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 when to use this tool ("What's new with X", "latest on Y") and when not to ("Use entity_profile instead for static profile"). Provides context for typical monitoring with 'since' and mentions the one-call parallel nature.

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 show idempotentHint=true and readOnlyHint=false. Description adds behavioral details: scoped by identifier, persistent for auth, 24-hour expiry for anonymous. 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?

Five sentences, well-structured. First two sentences convey core purpose and usage, then details. 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?

For a write tool with 2 simple params and no output schema, description fully covers purpose, use cases, data model, behavior details, and companion tools. Complete and sufficient.

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

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

Schema coverage 100% with param descriptions. Description adds value with key naming conventions (e.g., 'subject_property') and explains value is any text. Exceeds baseline for high coverage.

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

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

The description clearly states 'Save data the agent will need to reuse later' with specific verb and resource (key-value pair). It distinguishes from sibling tools 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?

Provides explicit when-to-use guidance: 'when you discover something worth carrying forward' with examples. Mentions pairing with recall/forget. Does not explicitly state when not to use, 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?

Beyond the annotations (read-only, idempotent, open-world), the description reveals important behavior: internal cascading lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit source labeling for each identifier, and the `unresolved` field for failed resolutions. This is exactly the kind of behavioral context that aids correct invocation.

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 long but well-structured: opening examples, core purpose, then detailed type breakdowns and caveats. Every sentence contributes value, though the length is a bit heavy for a quick scan. It is appropriately sized for the tool's complexity.

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 output schema, the description takes on the burden of describing return contents: for company it lists CIK, ticker, LEI, FIGI with source labels and unresolved handling; for drug it lists RxCUI, ingredient, brand and a citation. It also covers error behavior (graceful degradation), making the tool's behavior fully predictable.

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 description greatly enriches the schema. For `type`, it details what identifiers are returned for company (CIK, ticker, LEI, FIGI) vs drug (RxCUI, ingredient, brand). For `value`, it provides concrete input examples like ticker (AAPL), CIK (0000320193), and drug names ('ozempic'). The schema alone would not convey this depth.

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 function: resolve a user-spoken NAME to canonical/official identifiers required by other tools. It immediately provides example queries and explicitly defines the supported types (company, drug), which distinguishes it from sibling tools that operate on already-resolved IDs.

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 'Use FIRST whenever you have a name but need an ID,' giving clear when-to-use guidance. It does not list exclusions or mention alternative tools, but the directive is strong and unambiguous.

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?

Discloses that each entity is probed with ai_visibility_check, and results are ranked by score. Annotations already indicate read-only, idempotent, and non-destructive behavior, and description adds that it returns a ranked list with score, confidence, and signal density.

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

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

Three sentences, front-loaded with main purpose, no wasted words. 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?

Despite no output schema, description explains return format (ranked list with score, confidence, signal density). Complex tool with multiple entities, but description covers key aspects. Entity count constraint is in 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%, but description adds value: explains that the first entity is treated as the 'subject' for narrative, and provides an example query clarifying the 'context' parameter's role in disambiguating common names.

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 compares AI visibility across multiple entities, probes each with ai_visibility_check, and ranks by score. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (general comparison).

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 when to use: 'useful for competitive AI-marketing audits'. Implies alternative for single entity (ai_visibility_check) though not explicitly called out. Provides a concrete example query.

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), the description details partial failure graceful degradation, bundlephobia timing (5-30s), and sources_failed field. 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?

Single dense paragraph front-loading purpose, then details, usage, and limitations. 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?

Despite no output schema, description fully covers fan-out, data sources, response shape, partial failure, and ecosystem scope. Complete for a complex composite 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?

Both parameters are described in schema (100% coverage). Description adds that `version` defaults to latest, `package` accepts scoped packages, and explains the return structure (summary block fields).

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: a composite check for adding an npm package, covering license, advisories, version history, and bundle size. It distinguishes itself from siblings by specifying the npm ecosystem only.

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

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

Explicitly says when to use ('is X safe/popular/small' or 'what does adding lodash cost me') and when not (PyPI/Maven/Cargo/Go fall under deps.dev:version directly). Also notes bundlephobia timeout behavior.

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, and destructiveHint=false. The description adds the return structure (names, descriptions, IDs, update dates) but no additional 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?

The description is two sentences plus an example, front-loading the core purpose and returning info with no unnecessary words.

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

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

For a simple tool with one parameter, output schema available, and comprehensive annotations, the description adequately covers purpose, return format, and use case, leaving no 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 coverage is 100% for the single 'query' parameter, with a description including examples. The description adds no new semantic meaning beyond the schema, so baseline 3 applies.

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 verb 'Search', the resource 'CDC public health datasets', and the method 'by keyword', clearly distinguishing it from siblings like 'get_dataset'.

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 with an example and implies usage for broad dataset search, but does not explicitly state when to avoid this tool or mention alternatives like 'search_within'.

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 safe, read-only behavior, and the description adds technical details: embedding model (BGE-base-en), similarity method (cosine), window size (500-char overlapping), character limit (200K), and truncation behavior. 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 concise and front-loaded with the core action. Every sentence adds value, from use cases to technical constraints to pairing advice. 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 no output schema, the description fully covers purpose, usage triggers, technical behavior, parameter details, and integration with sibling tools. Complete enough for an agent to use correctly without additional context.

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

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

Schema coverage is 100%, and the description enriches each parameter with real-world context: text as document max 200K chars, limit as integer range with default, and query with natural-language examples. Provides meaning beyond schema.

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

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

The description clearly states the tool's function: semantic search inside a fetched record. It provides specific examples (SEC 10-K, article) and distinguishes from sibling tools like ask_pipeworx_grounded by focusing on intra-record search.

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 when to use: 'when the record is too big to cram into the prompt.' It also mentions pairing with ask_pipeworx_grounded as an alternative workflow, providing clear context for usage decisions.

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, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds context: requires OAuth account, delivery specifics, and webhook failure handling. 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 a single dense paragraph but well-structured with examples and clear separation of information. Slightly long but earns its content; minor improvement would be breaking into shorter sentences.

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

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

For a complex tool with 3 parameters, nested objects, and no output schema, the description is highly complete. It covers return value, prerequisites, supported types, and delivery channels comprehensively.

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. The description reinforces these with usage examples and additional context for params and delivery, adding value beyond the schema.

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

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

The description clearly states the tool creates a proactive monitoring subscription to a live-data event stream and returns the subscription ID. It lists supported types and distinguishes from siblings like unsubscribe and list_subscriptions.

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 specifies when to use (create subscriptions for monitoring) and when not (anonymous/BYO cannot persist). It provides prerequisites (Pipeworx OAuth account), delivery channel options, and limitations (SMS cap, webhook auto-disable).

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, destructiveHint=false. The description adds that the tool returns example questions with exact tool+argument shapes, describing the output effectively. 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 moderately long but well-structured. It starts with common user queries, defines output, then usage. Every sentence adds value, though it could be slightly more concise.

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

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

Given the tool's simplicity (one optional parameter) and no output schema, the description is complete. It explains purpose, usage, output format, and even lists topics. No missing context 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 coverage is 100%, baseline 3. The description adds value by explaining that omitting topic gives a full spread and passing a topic focuses the results. It also lists example topics beyond the schema's enum, providing richer guidance.

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: returning category-bucketed example questions for onboarding. It lists alternative user queries and distinguishes this from sibling tools like 'ask_pipeworx' by positioning it as the first point of entry when the agent doesn't know what Pipeworx can do.

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: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains how the topic parameter can focus the results. It doesn't explicitly state when NOT to use, but the context implies that for specific queries one should use 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 provide basic hints, but the description adds key behaviors: ownership check, soft deletion (deactivate), and no impact on historical data. No contradiction with annotations.

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

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

Two sentences, immediately states purpose, includes critical usage constraints. No unnecessary words.

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

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

For a single-param tool with no output schema, the description covers purpose, usage constraints, and behavioral effects. Completely adequate.

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

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

Schema has 100% coverage with description 'Subscription id (uuid) returned by subscribe.' The description adds that the id comes from subscribe, which is helpful but not essential. 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 uses a specific verb 'Cancel' and identifies the resource 'subscription by id'. It clearly distinguishes from sibling tools like 'subscribe' and 'list_subscriptions'.

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 ownership enforcement ('you can only cancel your own subscriptions') and explains the effect (deactivation not deletion). While it doesn't name alternatives, the context is clear enough for the agent.

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, openWorld, idempotent, non-destructive), the description adds crucial behavioral context: the meaning of `could_not_verify` (with verification_error) and the warning not to present it as evidence, plus the definition of `unsupported`. It also discloses that output includes a verdict, but the description explains return values since no output schema exists. The description does not contradict 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 lengthy but appropriately structured and front-loaded: trigger phrases, purpose, usage rules, pathway distinction, output format, then critical caller warnings. Every sentence adds value, and the 'IMPORTANT for callers' note is a clean structural highlight. 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?

With no output schema, the description fully explains return values (verdict, actual value, citation, reasoning) and nuanced semantics for `could_not_verify` and `unsupported`. It covers the two processing pathways and the efficiency benefit. The description is complete for a complex verification tool, leaving no major 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 baseline is 3. The description does not add parameter-specific semantics beyond what the input schema already provides; it mentions tolerance calculations in general but does not elaborate on `tolerance_pct` or `claim` syncing. The schema descriptions are sufficient for 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 explicitly defines the tool as 'natural-language claim verification against authoritative sources' and provides trigger phrases. It distinguishes itself from siblings by detailing the structured SEC EDGAR/XBRL fast path for company-financial claims and the grounded pipeline for other factual claims, making its unique scope clear.

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

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

The description states 'Use whenever the agent needs to check whether something a user said is factually correct' and differentiates when the structured path vs. the grounded pipeline applies. It also notes that it replaces 4–6 sequential calls, giving efficiency context. No explicit exclusions are needed since the tool covers all factual claims.

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