Data Delaware

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

Annotations already indicate safe, read-only, idempotent behavior. The description adds that the default model is free, Anthropic requires a BYO key with direct payment, and returns per-model and combined views. This provides useful context beyond annotations without contradiction.

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

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

The description is concise, with three front-loaded sentences covering purpose, defaults, and use cases. No unnecessary words; every sentence adds value.

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

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

Despite no output schema, the description explains the return structure (per-model score, confidence, signals, raw_response, combined view) and model options. Combined with good annotations, it provides complete context for selecting and invoking the tool.

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

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

Schema description coverage is 100%, providing clear parameter purposes. The description reinforces key points like default model and API key direct pass-through, adding contextual value (e.g., 'you pay Anthropic directly') that aids 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 probes LLMs about an entity and scores visibility (0-100) per model, providing a specific verb and resource. However, it does not explicitly differentiate from sibling tools like scan_competitor_ai_presence, though the uniqueness is implied by the scoring and multi-model aspect.

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 mentions when to use (AI-marketing audits, brand checks, competitive monitoring) and explains default model and optional API key for Anthropic with cost implications. It does not explicitly state when not to use or provide alternatives, but the context is clear.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that it routes to 4,552 tools, fills arguments, and returns structured citations. No contradiction, but could detail failure modes or limits.

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

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

Front-loaded with key instruction, each sentence adds value. However, length is justified by need to differentiate many siblings. Could be slightly more compact.

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

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

Given complexity (routing to many sources) and coverage by annotations/schema, description explains output format and use cases. Missing error handling or data freshness, but acceptable for a query tool.

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

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

Schema covers all 6 parameters (aliases for question) at 100% coverage. Description reinforces natural language input but doesn't add 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 routes questions to verified sources and returns structured answers with citations, specifying over 20 domains (SEC filings, FDA drugs, etc.). It distinguishes from siblings like ask_pipeworx_grounded and deep_research by defining their differences.

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

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

Provides explicit guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and describes when to step up to ask_pipeworx_grounded or deep_research. Also handles breaking news and gives concrete examples.

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 confirm read-only, idempotent, and non-destructive behavior. The description adds value by detailing the exact refusal reasons ('not_in_source', 'no_tool_match', etc.) and the extra LLM call cost. However, it doesn't elaborate on the routing mechanism beyond stating it picks the right tool, which is acceptable given the 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: it starts with the core purpose, explains the process, details the return format, provides usage context, and notes the alternative. It is front-loaded with the key benefit. However, it is relatively long and could potentially be streamlined without losing content.

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

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

Given the tool's complexity (routing among 4552 tools, returning structured data with refusals), the description covers all necessary aspects: return format with fields, refusal reasons, cost tradeoff, and use cases. No output schema is required because the description sufficiently explains the output structure.

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

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

All 6 parameters are aliases for 'question', and the schema coverage is 100% with each alias described. The description mentions that the question is in natural language and used to route to tools, but this adds limited semantic value beyond the schema. Baseline 3 is appropriate as the schema covers the parameter meanings adequately.

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 is a 'Hallucination-resistant answer mode for high-stakes reads,' explains the routing and extraction process, and distinguishes itself from ask_pipeworx by noting the extra LLM call cost. It provides a specific verb ('answers') and resource ('questions using only tool result'), making the purpose very clear.

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

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

The description explicitly states when to use this tool: '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 advises to prefer ask_pipeworx for casual lookups, providing clear 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 extensively discloses behavioral traits: fan-out patterns, resolver contract (match confidence, alternatives), parent event extraction, news fallback, safety short-circuits, tradeability warnings, and cancellation rule parsing. Annotations already declare readOnlyHint=true, and the description adds rich context without contradiction.

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

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

The description is long but well-organized into clear sections (classifiers, fan-out examples, response shapes, safety). Every sentence adds value; minimal redundancy. Could be slightly more concise, but the depth justifies the length.

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

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

Given no output schema, the description thoroughly covers return shapes (market, analysis, evidence), safety conditions, cancellation rules, and edge cases. Agents can fully understand what to expect. No missing critical information.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds meaning beyond schema by providing examples (slug, URL, question text), explaining depth options and include_raw behavior, and noting default values. This goes beyond baseline 3.

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

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

The description uses the specific verb 'research' with the resource 'Polymarket bet' and details the process (pulling Pipeworx data, resolving, classifying, fanning out). It clearly distinguishes from sibling tools like ask_pipeworx or polymarket_edges by focusing on bet-level research with evidence packets and model 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?

The description explicitly states use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It also provides when-not-to-use scenarios (low-confidence matches, closed markets) and warns about resolution-rule risk. However, it does not directly compare to sibling tools or specify when to use 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?

Description adds significant behavioral context beyond annotations: details data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinicaltrials for drugs), handles fiscal year offsets, sorts results by primary metric, and returns citation URIs. 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?

Description is front-loaded with trigger examples, contains no redundant sentences, and efficiently conveys all necessary information. Every sentence earns its place despite moderate length.

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

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

Given 2 parameters and no output schema, description covers entity types, data sources, sorting behavior, and citation links. It provides sufficient context for the agent to select and invoke the tool correctly.

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

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

Schema coverage is 100% but description enriches meaning: explains that 'type' selects between company (financial data) and drug (adverse events/approvals/trials) and that 'values' accepts tickers/CIKs or drug names. It also clarifies sorting and output format.

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

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

Description clearly states the tool performs side-by-side comparison of 2-5 companies or drugs in one parallel call, with specific examples of trigger phrases. It distinguishes from sibling tools by noting it replaces 8-15 sequential lookups.

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 sequential single-pack lookups when comparing entities', provides context for when to use (comparing entities) and specifies entity count range (2-5).

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds specific return fields and the data source, but does not contradict annotations. It provides additional behavioral context beyond annotations, but not significantly more.

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, each earning its place. Front-loaded with the core action, then details return fields and usage. No waste.

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

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

Despite no output schema, the description fully explains what is returned (resource_id, name, description, category, update date) and how to proceed. It is complete for a read-only search 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 baseline is 3. The description does not add additional meaning beyond what the input schema already provides for the parameters.

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

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

The description uses the specific verb 'Search' and identifies the resource as 'Delaware Open Data catalog of open datasets by keyword'. It clearly distinguishes from sibling tools by being the only one dedicated to searching that specific catalog.

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 clear context for use (search by keyword) and suggests the next step ('pass the resource_id to query/metadata'). However, it does not explicitly state when not to use it or compare with alternatives, missing some 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?

Disclosures include account requirement, paid tier for 'thorough' depth, parallel decomposition, and return format (findings packet with citations and gaps). No contradiction with annotations (readOnlyHint, openWorldHint, etc.).

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?

Somewhat long but front-loaded with critical info (account, alternative). Each sentence adds value, though could be slightly more concise 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?

Given complexity (2 params, no output schema), description explains return format, expected duration (15-60s), and data sources. Complete enough for the agent to use effectively.

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

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

Schema coverage is 100%. The description adds context: depth values map to facet counts (quick=3, standard=5, thorough=8) and question accepts natural language. Adds 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 specifies a 'grounded multi-source research' tool using 1160 structured data sources, decomposing questions into facets and routing to 4552 tools in parallel. It clearly distinguishes from siblings like ask_pipeworx and ask_pipeworx_grounded.

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 (broad/multi-part questions over structured data) and when not (breaking news, current events). Provides alternatives: ask_pipeworx for single lookup or current news, and notes account requirements.

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, idempotentHint true, destructiveHint false. The description adds that it returns top-N tools with names, descriptions, and full input schemas with curated examples, and each result is ready to call directly. This provides behavioral context beyond annotations without contradiction.

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

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

The description is somewhat lengthy but well-structured: starts with purpose, then usage guidance, then return value details. Every sentence provides useful information; no redundancy.

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

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

Given 6 parameters (only 1 required) and no output schema, the description explains the return value (tools with schemas) and usage scenario. It fully covers what the tool does, when to use it, and what to expect, making it 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?

Schema description coverage is 100%. The description adds meaning by giving natural language examples for 'query' and listing aliases. It also mentions 'limit' parameter. This adds 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 finds tools by describing data or task, using verbs like find, browse, search, look up, discover. It lists specific domains (SEC filings, FDA drugs, etc.) and distinguishes itself from siblings as the discovery/browsing tool.

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

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

The description explicitly says '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.' It provides clear usage context but does not explicitly state when not to use it or list 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?

Beyond annotations (readOnly, openWorld, idempotent), the description discloses that patents API sunsets May 2025 and soft-fails, news has GDELT→GNews fallback, and names are not supported. This adds valuable behavioral context without contradicting 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 information-dense and includes a clear list of returned data. While thorough, it could be slightly more compact by avoiding redundant introductory phrases. Still, it remains well-structured and readable.

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 only 2 parameters and no output schema, the description fully explains what the tool returns, including sources (SEC, XBRL, USPTO, GDELT, GLEIF) and edge cases (patents soft-fail, news fallback). It leaves no ambiguity about the tool's output.

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 meaning: it clarifies that 'type' only supports 'company' (others coming soon) and that 'value' must be ticker or zero-padded CIK (names not supported). This supplements the schema's description.

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

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

The description clearly states the tool's purpose: 'full cross-source profile of a US public company in ONE parallel call.' It lists specific deliverables (CIK, filings, fundamentals, patents, news, LEI) and explicitly contrasts with chaining multiple tools, making the purpose unambiguous.

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

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

The description explicitly states when to use: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also provides clear exclusion: 'names not supported (use resolve_entity first if you only have a name).' This gives excellent guidance on alternatives.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. Description adds context about clearing sensitive data and stale context, which is valuable beyond what annotations provide. 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 sentences, first states action immediately, second provides usage guidelines. No filler, very 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?

Given the simple tool (1 param, no output schema, good annotations, clear siblings), the description covers purpose, when to use, and related tools completely. 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?

Parameter key is described in schema as 'Memory key to delete'. Description does not add extra semantics beyond this. With 100% schema coverage, baseline of 3 is appropriate.

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

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

Description explicitly states 'Delete a previously stored memory by key.', providing a specific verb and resource. Sibling tools include remember and recall, so this tool is clearly distinguished as the deletion operation.

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

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

Provides explicit conditions for use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also mentions pairing with remember and recall, which are siblings, offering clear guidance on alternatives.

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

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

Annotations (readOnlyHint, idempotentHint, non-destructive) are present. Description adds that it fetches the page, extracts title/description/key links, and outputs standard markdown format. 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?

Short, front-loaded with purpose, no wasted words. Efficiently conveys 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?

Description covers purpose, usage, and output format. Lacks details on error handling but overall sufficient given tool simplicity and annotations.

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

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

Schema coverage is 100%, so baseline is 3. Description implicitly covers url but adds no extra meaning for max_links beyond schema. Does not compensate 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 it generates a llms.txt file for a URL, targeting AI crawlers. Lists specific use cases (client, own project, competitor audit). Distinguishes from siblings as no other tool generates llms.txt.

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: getting client's site indexed, drafting own llms.txt, auditing competitor. However, does not mention when not to use or mention alternatives; no exclusion criteria.

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

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

Annotations already provide readOnlyHint, destructiveHint, etc. The description adds context about the return fields and the default scope ('active'), which aligns with annotations. No contradictions.

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

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

Two concise sentences: first states purpose and return fields, second gives usage guidance. 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 list tool with one optional parameter, the description provides sufficient context including return fields and usage scenario. No output schema needed as return fields are listed.

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 parameter is already documented. The description does not add details about the include_inactive parameter beyond implying the default scope is active. This meets the baseline of 3.

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

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

The description clearly states 'List the caller's active subscriptions' and specifies the return fields (id, type, params, created_at, last_fired_at, fire_count). It distinguishes from sibling tools like subscribe and unsubscribe by focusing on listing.

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

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

The description gives explicit usage guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to use the tool, though it does not explicitly mention when not to use it or list alternatives.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds no additional behavioral context (e.g., caching, rate limits, or side effects) beyond reiterating the output fields.

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

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

A single sentence that front-loads the primary action and resource, includes the most important output fields, and provides an example. No redundant or unnecessary words.

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

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

Given no output schema, the description lists key return fields (columns, types, row count, category, last-updated), which is helpful. However, it could specify the data format or structure more explicitly. Still, it provides sufficient context for a simple 1-parameter 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?

Parameter schema has 100% coverage with a description and example already. The tool description restates the parameter and example, adding minimal extra meaning beyond the schema. Baseline of 3 is appropriate.

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

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

The description clearly states the tool retrieves schema and metadata for a Delaware Open Data dataset, listing specific elements (columns, types, row count, category, last-updated) and including an example resource ID. It distinguishes itself from sibling tools like 'datasets' by focusing on a single dataset's detailed schema.

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 use when you have a resource_id and need metadata, but lacks explicit guidance on when to use this tool versus alternatives (e.g., 'datasets' for listing). No 'when not to use' or prerequisites are mentioned.

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 key behaviors beyond annotations: rate-limited to 5 per identifier per day, free, doesn't count against tool-call quota, and team reads digests daily. No contradiction with annotations (readOnlyHint=false, etc.).

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

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

Concise and front-loaded: opens with purpose, then lists use cases, then provides details. Every sentence earns its place, 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 no output schema and simple feedback submission, the description is complete. It covers purpose, when to use, constraints, and content guidelines. No gaps 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?

Input schema has 100% description coverage for all 3 parameters. The description adds minimal extra meaning beyond the schema, as the schema already describes enum values and structure. 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: sending feedback about Pipeworx being broken, missing, or needing existence. It lists specific use cases (bug, feature, data_gap, praise) and distinguishes it from sibling tools, none of which are feedback-related.

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 the tool (for tool issues, missing tools, praise) and provides clear exclusions (don't paste end-user prompts). Also mentions rate limits and that it's free and doesn't count against quota, guiding proper usage.

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 behavioral context beyond annotations: data source ('CF analytics-engine'), privacy ('no PII'), and caching ('5min-1h'). Annotations already indicate read-only and idempotent, and description aligns without contradiction.

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

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

The description is well-structured with a clear action sentence followed by bulleted use cases. It is front-loaded and each sentence adds value, though slightly verbose for a simple tool.

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

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

Given the tool's simplicity (one optional parameter, no output schema), the description covers purpose, use cases, data source, and caching. No major gaps are present.

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

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

Schema coverage is 100%, but the description adds value by explaining the trade-off between window sizes ('shorter windows surface what's hot right now; longer windows show steady-state demand'). This goes beyond the enum description.

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

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

The description clearly states the action ('returns') and the resources ('top tools, top packs, and total call volume') over a specified window. It distinguishes itself from sibling tools like 'discover_tools' by focusing on trending call data.

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

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

Three explicit use cases are provided (discovering hot data sources, confirming popular tool, aligning use case). While it doesn't name alternatives explicitly, the context signals guide usage effectively.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds rich behavioral context: monotonicity violations, partition-sum checks, semantic anchor (Jaccard similarity), placeholder filter, fill check against live CLOB depth, and specific output fields. 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 detailed and well-structured with sections for each mode, but somewhat verbose. Still, every sentence adds value, and front-loading the core purpose helps the agent. Minor room for tightening.

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 fully explains the return structure: opportunities array with fields, partition_check details, fill check logic, and conditions for valid signals. Complete 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 coverage is 100%, and description adds extensive meaning: explains formats (slug/URL for event, seed question for topic), describes the modes in detail, and clarifies the trade-off between them. Goes well beyond the basic schema descriptions.

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

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

Clear verb+resource: 'Find arbitrage opportunities on Polymarket'. Distinguishes between three modes (trending_scan, event, topic) with specific examples, and no sibling tool has identical functionality.

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

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

Explicitly tells when to use each parameter: 'Call with NO args for trending_scan', 'pass event for a specific market', 'topic for cross-event scan'. Also provides exclusion guidance: 'when the partition signal fires... do not trade it' and recommends alternative tool 'polymarket_fill_risk' for custom sizing.

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 and non-destructive. Description adds extensive behavioral context: details on three model families with specific algorithms (lognormal barrier, GDELT ratio, partition overround with sport-specific alpha), slippage assumptions, caching, response structure, and limitations (placeholder filter, Fed signal unreliability). 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 very detailed but verbose. Includes internal implementation details (specific alpha values, funnel counters) that may overwhelm an AI agent. Structure is logical (segments, filters, diagnostics) but could be more concise.

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

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

Given 9 parameters, no output schema, and nested response structure, description covers everything needed: response segments, diagnostics, caching, filtering logic, and limitations. Over-complete but ensures agent can correctly invoke and interpret results.

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

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

All 9 parameters have schema descriptions (100% coverage). Description adds valuable context beyond schema, e.g., explaining min_edge_pp is net of slippage, slippage_pp default rationale, min_partition_leg_kelly behavior for partitions. Enhances understanding but schema already covers basics.

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

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

Clear verb+resource: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' Distinct from siblings like polymarket_arbitrage and polymarket_edge_tracker. Also includes context for AI agents ('what should I bet on today').

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 purpose and when to use: 'Built for what should I bet on today — agents discover opportunities without paging hundreds of markets.' Provides detailed filtering guidance via knobs (min_liquidity, max_spread_pp, etc.) and explains which segments to use. Also excludes Fed bets with justification.

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, idempotent, non-destructive. The description adds critical behavioral details: data source (daily snapshots), history bounds (60-day TTL), decay computation from daily closes (not intraday), and snapshot gaps due to cache-miss. 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 fairly long but well-structured with a purpose statement, argument details, response breakdown, and limits. It front-loads the key question. Some redundancy exists (repeating defaults), but overall it earns its length due to 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 thoroughly explains the response fields (tracked, expired, snapshot_dates), including edge cases (gaps, TTL, decay derivation). It covers what the tool returns and its limitations, making it complete for an AI agent.

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

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

Schema coverage is 100% and already describes days and window. The description repeats these defaults and options but adds context about the response structure. However, it does not provide new meaning beyond what the schema already conveys, hence a baseline 3.

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

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

The description clearly states it provides 'edge persistence and decay telemetry' from daily snapshots, answering a specific question about edge longevity. It distinguishes itself from sibling tools like polymarket_edges by focusing on time-series decay analysis.

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

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

The description explicitly tells when to use this tool: to assess if an edge is fresh or old ('a fresh wide edge and a 3-week-old wide edge are different trades'). It does not explicitly list when not to use it or alternatives, but the context implies it's for decay analysis, not raw edge listing.

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 extensive behavioral context beyond the annotations (readOnlyHint, idempotentHint, etc.). It details return fields, implications of thin books, and forced directional risk. 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 with clear sections (SINGLE-MARKET, BASKET) and front-loaded purpose. Every sentence adds value given the tool's complexity, though slightly verbose.

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

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

With no output schema, the description fully explains all return values for both modes, including verdict categories and forced directional risk. It covers edge cases and provides complete context 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%, but the description adds significant meaning: explains mutual exclusivity of market/event, different interpretations of size_usd per mode, and default side logic for basket. 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 clearly states 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and explains two distinct modes (single-market and basket). It explicitly differentiates from sibling tools polymarket_arbitrage and polymarket_edges by specifying when to use this tool before those.

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 THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also warns against using without this check due to risk of unhedged directional positions.

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 reveals significant behavioral traits beyond annotations: it details compatibility_warning conditions (matched_pairs:0 with skipped_cross_type>0 or 0), temporal_alignment checks, and skipped_cross counters. Annotations provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint, which are non-contradictory. The description adds crucial context about data filtering and warning flags, making behavior fully transparent.

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 verbose but well-structured with clear sections (modes, response, safety fields). Every sentence adds value, explaining core mechanism, limitations, and output structure. Could be slightly more concise, but effective front-loading of purpose and modes makes it efficient for agent understanding.

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

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

Given no output schema, the description thoroughly explains response structure (leg-by-leg prices, top_spreads_pp, safety fields). It covers all input modes, edge cases (compatibility warnings, temporal alignment), and counters (skipped_cross_type). With 3 optional parameters and no complex nesting, the description is fully self-contained for correct 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?

Schema coverage is 100% with descriptions for each parameter. The description adds value by explaining the two modes and how explicit tickers override topic-mapped sides, and provides example values for topic (e.g., 'fed', 'btc'). It clarifies the relationship between parameters beyond schema text, earning a 4 above baseline 3.

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

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

The description clearly states the tool computes cross-venue spread between Kalshi and Polymarket, explains two modes (topic shortcuts and explicit tickers), and differentiates from sibling tools like polymarket_arbitrage which focus on intra-venue arbitrage. Action verb 'cross-venue spread' with specific resources is explicit.

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 usage by explaining that pre-mapped topics often return compatibility warnings ('most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable'), helping users set expectations. It explains when the tool is useful (same resolving question) and when spreads are meaningless (non-equivalent bet shapes or temporal mismatch). Does not explicitly list 'when not to use' alternatives, but clarity on limitations suffices.

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. Description adds that results are returned as JSON, SoQL clauses are without leading $, and default limit is 1000. This provides useful behavioral context beyond annotations.

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

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

Description is extremely concise: two sentences, front-loaded with the main action and resource, then details. No unnecessary words. Every sentence adds value.

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

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

For a tool with 7 parameters all documented in schema, and no output schema, the description adequately explains the tool's purpose, filtering capabilities, and return format. It mentions default limit and pagination. Complete given the 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 has 100% coverage, so baseline is 3. Description adds value by clarifying that SoQL clauses should be written without the leading '$' (e.g., 'where' not '$where'), which is not in the schema. Also explains the role of limit/offset and the required resource_id format.

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

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

Description clearly specifies verb 'Run a Socrata SoQL query' and resource 'Delaware Open Data dataset by resource_id'. It distinguishes from siblings like 'datasets' (which lists datasets) and 'metadata' by being the actual query tool. No ambiguity.

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

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

Description states when to use: to query a dataset by resource_id with SoQL filters. It does not explicitly state when not to use or name alternatives, but the context is clear enough. A slight gap in explicit exclusion or sibling differentiation.

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

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

Annotations already provide readOnlyHint, destructiveHint, idempotentHint. The description adds scoping information ('Scoped to your identifier...'), which is valuable beyond annotations. No contradiction.

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

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

Three sentences, front-loaded with the main action, efficiently conveying purpose and usage without 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 key-value retrieval tool, the description, annotations, and schema together cover all needed aspects: safety, parameter behavior, usage context, and scoping. No output schema is necessary.

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

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

Schema coverage is 100%, baseline 3. The description clarifies that omitting the key lists all keys, adding meaning beyond the schema's property description.

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

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

The description specifies the action (retrieve/list), the resource (saved memory), and distinguishes from siblings like 'remember' and 'forget'. It clearly answers what the tool does.

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 when to use the tool (e.g., to avoid re-deriving context) and mentions pairing with remember and forget. However, it does not explicitly state when not to use it or alternatives.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. Description adds context: marks events as read in same call, polling suitability, and specific fields returned. No contradictions.

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

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

Four sentences, front-loaded with main purpose, followed by details. Every sentence adds unique information without redundancy or fluff. 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, the description explains what fields are returned (source, citation_uri, payload), how filtering works, and the mark_read mechanism. Also mentions polling and alternative access. Complete for expected 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?

Schema coverage is 100%, so baseline 3. Description adds value by providing example filter value ('sec_8k'), clarifying ISO timestamp format, and explaining mark_read's effect on future calls. This goes beyond schema descriptions.

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

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

Clearly states 'Pull fired events from your subscription feed' with specific verb 'Pull' and resource 'fired events'. Distinguishes from sibling tools like 'list_subscriptions' by focusing on events, not subscription metadata.

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

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

Provides explicit guidance on filtering by type and since, using mark_read, and polling. Mentions alternative endpoint for scripts/dashboards, but does not explicitly state when not to use this tool (e.g., for writing or managing subscriptions).

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

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

Annotations already mark it as readOnly and idempotent. The description adds details about fan-out to SEC, GDELT→GNews fallback, USPTO soft-fail, and return structure with citations, exceeding annotation coverage.

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 informative and front-loaded with examples, but slightly verbose. Every sentence adds value, though it could be streamlined 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?

Given the tool's complexity (multiple sources, fallback, date flexibility) and no output schema, the description fully explains behavior, return structure, and alternatives.

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

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

Schema coverage is 100%, and description adds meaningful context: type is only 'company', since explains ISO vs relative shorthand and suggests '30d' for monitoring, value accepts ticker or CIK.

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

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

The description clearly states the tool returns a change feed for a company, with specific example queries like 'What's new with X' and distinguishes from entity_profile by noting it provides a static profile instead.

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

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

Explicitly provides when to use this tool vs entity_profile ('Use entity_profile instead when you want the static profile...'), gives example queries, and explains acceptable date formats.

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 a write operation (readOnlyHint=false) and non-destructive behavior. The description adds valuable context: data is scoped by agent identifier, retention differs by authentication status, and it is idempotent (implied). 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 concise (4 sentences) with front-loaded purpose. Every sentence adds value: purpose, usage context, retention behavior, and sibling references. No filler or redundancy.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description is complete. It covers what is saved, when, how long it persists, and pairing with other tools. 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% (both 'key' and 'value' have descriptions). The description mentions storing as a key-value pair but adds no extra semantics beyond the schema. Per rubric, baseline 3 for high coverage 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 'save' and resource 'data' and clearly states it stores key-value pairs for later reuse. It distinguishes from sibling tools 'recall' and 'forget' by mentioning them as complementary, making the purpose distinct.

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: 'when you discover something worth carrying forward'. It provides context on retention (persistent vs 24h) and directs to sibling tools 'recall' and 'forget' for retrieval and deletion, leaving no ambiguity.

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 show readOnly, openWorld, idempotent, non-destructive. Description adds that it cascades through multiple endpoints internally, replacing 2-3 manual lookups, providing useful behavioral insight beyond annotations.

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

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

Single paragraph with conversational front-loading. Every sentence adds distinct value (examples, usage, return details, supported types). 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?

No output schema, but description fully explains return structure for both types, including citation URIs. Parameters are completely covered. The workflow from name to ID is clear.

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

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

Schema coverage is 100%, so baseline 3. Description enriches by providing concrete examples (ticker, CIK, brand/generic names) and explaining auto-disambiguation and return values, adding 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?

Description starts with clear invocation examples, explicitly states 'resolve a user-spoken NAME to the canonical/official identifier', and lists supported types with specific returns. Distinguishes from sibling tools by focusing on name-to-ID resolution, unlike entity_profile or compare_entities.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID.' While it doesn't list when not to use, the context of being a preliminary step is clear and sufficient for typical agent 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?

Discloses internal probe with ai_visibility_check and output format (ranked list with score, confidence, signal density). Annotations already declare readOnly, idempotent, non-destructive; description adds value beyond annotations.

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

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

Three sentences, front-loaded with purpose. Every sentence adds value: purpose, behavior, use case. No fluff or repetition.

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

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

Describes return format (ranked list with score, confidence, signal density) despite no output schema. Covers all parameters and usage context. Complete for a comparison tool.

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

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

Schema description coverage is 100%. Description adds extra semantics: 'first entry treated as subject for narrative', context disambiguates, models default to workers-ai, _apiKey needed for anthropic. Adds 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?

Clearly states 'Compare AI visibility across multiple entities side-by-side', specifies it uses ai_visibility_check, ranks by score, and surfaces most/least recognized. Distinguishes from sibling tools like ai_visibility_check (single 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?

Provides context: 'useful for competitive AI-marketing audits' and gives an example query. Does not explicitly state when not to use or name alternative tools, but the context is clear enough for selection.

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

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

Goes well beyond annotations by detailing internal behavior: fans out across deps.dev and bundlephobia, handles partial failures gracefully, mentions 5-30s timeout for bundlephobia first measurement, and notes that sources_failed will list timeouts. No contradiction with annotations (all read/idempotent/open world hints are consistent).

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 information-dense but could be slightly more concise. It front-loads the key purpose and then details behavior. Every sentence earns its place, but some length may deter quick scanning.

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 enumerates all return fields (summary block, advisory details, links, versions). It covers scope, failure modes, and ecosystem limitations. Missing explicit mention of rate limits or auth, but annotations and context signals provide enough.

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

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

Schema description coverage is 100% (both parameters have descriptions). The tool description adds minimal extra value: only clarifies scoped packages and default version behavior. 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 specific verbs ('composite check', 'fans out') and clearly defines the resource ('npm package to my project'). It distinguishes itself from sibling tools by explicitly scoping to NPM ecosystem and mentioning alternative approaches for other ecosystems.

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 whenever an agent asks "is X safe / popular / small"') and when not to ('NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'). Provides context about partial failures and timing.

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 read-only, open-world, idempotent hints. Description adds character offsets, similarity scores, embedding model (BGE-base-en), window size (500-char), and 200K char cap with truncation flagging. No contradictions.

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

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

Two paragraphs, front-loaded with purpose. Every sentence adds value: use case, output details, limitations, and pairing. No fluff.

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

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

Given 3 parameters and no output schema, description explains return format (passages with offsets and similarity scores), limitations, and integration with other tools. Complete for agent 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% with good descriptions already. Tool description adds overall context (e.g., use cases, examples like SEC filings) but does not significantly improve individual parameter 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?

The description clearly states it performs semantic search inside a fetched record, with specific verb 'search' and resource 'record'. It distinguishes from siblings like ask_pipeworx_grounded by describing its focused, passage-level 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 (record too big for prompt) and how it pairs with ask_pipeworx_grounded. Also implies not for initial fetching, providing clear context.

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

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

The description adds extensive behavioral context beyond annotations: requires OAuth account, returns new id, details per type (e.g., sec_8k items meaning), delivery options (feed always on, email/sms optional with constraints, webhook with signing secret and auto-disable after 10 failures). Annotations already indicate readOnlyHint=false, idempotentHint=true, destructiveHint=false, which are consistent.

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

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

The description is detailed but front-loaded with the core purpose. It uses a list structure for types and delivery channels, making it scannable. While dense, every sentence adds value; slightly more whitespace could improve readability.

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

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

Given the tool's complexity (3 parameters, required dependencies, multiple types/delivery options), the description fully covers all necessary aspects: auth requirement, type-specific params, delivery constraints and behavior. No output schema needed since return is simply a subscription id.

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema: provides concrete examples for each subscription type (e.g., items:['5.02'] = officer change), explains delivery channel details (e.g., webhook signing, SMS cap), and clarifies field constraints. This greatly aids correct parameter 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 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It uses a specific verb (create) and resource (subscription), and distinguishes from sibling tools like list_subscriptions and unsubscribe by focusing on creation.

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 that a Pipeworx OAuth account is required and that anonymous/BYO accounts cannot persist subscriptions, providing clear guidance on when not to use. It also lists supported types and delivery channels, though it does not explicitly compare to alternative tools for the same task.

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

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

Annotations already cover safety (readOnlyHint, idempotentHint, etc.). The description adds value by explaining the output format (category-bucketed example questions with tool+argument shapes) and the tool's role as an onboarding entry point. 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?

Description is front-loaded with the primary use case and alternative phrasings. It is moderately long but every sentence provides essential context (purpose, output, parameter usage, when to use). Slightly verbose but appropriate for the onboarding function.

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

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

For a simple tool with 1 optional parameter, no output schema, and complete annotations, the description covers all necessary aspects: when to use, what it returns, how to parameterize, and the overall role in the tool ecosystem.

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

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

Schema coverage is 100%, but the description enriches the single parameter 'topic' by listing concrete focus areas (finance, pharma, etc.) and stating that omitting it provides a cross-category spread, which aids selection beyond the schema's minimal description.

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

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

The description explicitly states the tool's purpose as an onboarding entry point that returns category-bucketed example questions. It distinguishes from siblings like 'discover_tools' by focusing on example questions and meta-tool learning, not just listing 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?

Clear guidance is provided: '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 the topic parameter for focusing queries and indicates when to omit it for a full spread.

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 details that subscriptions are deactivated (not deleted) and that historical events persist, which complements the annotations (destructiveHint=false). It adds clarity beyond the structured fields by explaining the soft delete and ownership enforcement.

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: first states the core action and constraint, second explains side effects and alternative. Efficient and 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 tool with one parameter, no output schema, and clear annotations, the description covers the behavior (soft delete), ownership, and relevant sibling ('recent_alerts'). No gaps remain.

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

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

With 100% schema coverage, the schema already describes the 'id' parameter. The description merely restates 'by id' without adding new meaning or format details, meeting the baseline.

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

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

The description clearly states the action ('Cancel a subscription') and the resource ('subscription by id'). It distinguishes itself from siblings like 'subscribe' and 'list_subscriptions' by specifying its unique operation.

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

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

The description provides ownership enforcement and notes that historical events remain available via 'recent_alerts', implying when to use that sibling. However, it does not explicitly state when not to use this tool or list alternatives beyond the one mention.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds details about the output (verdict categories, structured form, actual value with citation, percent delta) and the efficiency benefit, which is beyond what annotations provide. 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 each sentence adds value: examples, usage, scope, return details. Front-loaded with examples. Could be slightly more concise but well-structured overall.

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

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

Given no output schema, the description fully explains the return values (verdict types, structured form, citation, delta). The domain and source are specified. Annotations provide a strong safety and behavioral profile. The description is complete for effective tool selection and invocation.

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

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

Only one parameter 'claim' with 100% schema description coverage. The description supplements the schema by explaining the natural-language format and providing examples, adding clarity beyond the schema's own description.

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

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

The description starts with clear examples of natural-language claim verification queries, states the tool's purpose as 'check whether something a user said is factually correct', and specifies its domain (company-financial claims via SEC EDGAR + XBRL). It effectively distinguishes itself from sibling tools by claiming it replaces multiple sequential calls.

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

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

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It also delimits scope to company-financial claims. However, it does not provide explicit when-not-to-use scenarios or compare directly to specific sibling tools.

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