Law Feeds

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint. The description adds behavioral details: probes multiple models, returns per-model score/confidence/signals/raw_response plus combined view, and explains API key passthrough. This 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?

The description is front-loaded with the core action, followed by details on models and parameters, and ends with use cases. Every sentence adds value; 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?

Despite no output schema, the description explains the return structure: 'per-model {score, confidence, signals, raw_response} + a combined view.' It covers all necessary context for a tool with 4 parameters and 1 required, leaving no gaps.

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

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

Schema coverage is 100%, but description adds practical meaning: default model is 'Workers AI Llama-3.3-70b (free)', _apiKey only needed if 'anthropic' in models, context 'helps disambiguate common names'. These enrich parameter understanding.

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

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

The description clearly states 'Probe one or more LLMs for what they know about a business/brand/product/topic and score visibility (0-100) per model.' It uses specific verbs (probe, score) and resource (LLMs for entity), and the purpose is distinct from sibling tools like deep_research or entity_profile.

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

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

The description gives explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains default vs. paid model (Workers AI vs. Anthropic with API key). However, it does not directly contrast with sibling tools or mention when not to use it.

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

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

Annotations already indicate readOnly, openWorld, idempotent. Description adds that it routes to 4,484 tools, fills arguments, and returns structured answers with citation URIs, providing useful behavioral context beyond annotations.

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

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

Description is detailed and well-structured with examples and comparative guidance, though slightly lengthy. Every sentence adds value, but could be condensed.

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

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

Despite no output schema, description explains return format (structured answer with citations). Context signals show high schema coverage and good annotations; description adequately covers usage intent and scope.

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

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

Schema coverage is 100% with clear descriptions for all parameters (including aliases). Description adds no additional semantics beyond what the schema provides, meeting the baseline.

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

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

The description clearly states it answers factual questions using authoritative structured data, listing specific domains like SEC filings, FDA data, etc. It differentiates from siblings like ask_pipeworx_grounded and deep_research.

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

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

Explicitly advises to prefer over web search for factual answers, provides example queries, and specifies when to use alternatives (ask_pipeworx_grounded, deep_research).

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 read-only, open-world, and idempotent hints. The description adds behavioral details beyond annotations: cost of an extra LLM call, specific refusal reasons, and extraction mechanism. It does not contradict annotations.

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

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

Despite length, every sentence adds value. The description is structured effectively: purpose, routing behavior, return format, usage guidance, cost tradeoff. No redundant 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?

The description provides the return format with fields including 'evidence' and 'refusal_reason', compensating for no output schema. It covers all essential aspects for a grounded tool with high-stakes 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 all parameters described. The description mentions 'question' but does not add new semantic detail beyond the schema; baseline 3 is appropriate.

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

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

The description clearly states it is a 'hallucination-resistant answer mode for high-stakes reads' and contrasts it with ask_pipeworx, explaining it extracts answers only from tool results. This precisely defines its purpose, distinguishing it from siblings.

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

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

Explicitly specifies when to use ('when an answer will be quoted, cited, or acted on' in high-stakes domains) and when not ('prefer ask_pipeworx for casual lookups'), providing clear context and explicit 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 exhaustively details behaviors beyond annotations: market resolution, classification, fan-out to data packs, response shapes, resolver contract with confidence levels, parent event extraction, news fallback, safety short-circuits for low confidence, closed markets, wide spreads, and cancellation rules. Annotations already indicate readOnly and idempotent, adding no contradiction.

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

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

The description is very long (multiple paragraphs) and dense with technical details. While every sentence is informative, it lacks conciseness and structure. Front-loading the purpose helps, but the sheer volume may overwhelm an agent. Could be more streamlined.

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 (market resolution, classifier logic, multi-source fan-out, safety mechanisms), the description is remarkably comprehensive. It covers all major aspects: resolver contract, parent events, news errors, security behaviors, and cancellation rules. No output schema exists, so the description fully compensates by detailing response fields.

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

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

Schema coverage is 100% with concise descriptions. The description adds significant value by explaining default behavior for 'depth' (quick vs thorough), providing examples for 'market', and clarifying the size implications of 'include_raw'. This goes beyond the schema's basic field descriptions.

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

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

The description clearly states it researches a Polymarket bet by pulling Pipeworx data, specifying input types: slug, URL, or question text. It immediately tells the agent what the tool does with a specific verb-resource pair ('Research a Polymarket bet') and distinguishes from siblings like polymarket_edges by focusing on data retrieval rather than edge calculation.

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

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

Provides explicit use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It gives context on when to inspect results (e.g., medium/low confidence matches). However, it lacks explicit exclusions or direct comparisons to alternatives, but the use cases are clear enough for an agent.

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

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

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description adds rich behavioral context: for companies, it pulls specific financial data from SEC EDGAR/XBRL and handles off-calendar fiscal years; for drugs, it pulls FAERS data and trial counts. It also states results are sorted by primary metric and returns citation URIs.

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

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

The description is well-structured, starting with example queries and then detailing behavior. It is informative but not overly verbose; every sentence adds value. Slightly longer than necessary but justified by the 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?

Given the tool's complexity (two entity types) and absence of an output schema, the description is remarkably complete. It covers all inputs, behavior, data sources, sorting, and output format (paired data + citation URIs). No gaps remain.

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

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

Schema coverage is 100% with both parameters described. The description adds significant meaning: explains the enum values 'company' and 'drug' with details on what data each retrieves, and clarifies the array items including ticker/CIK requirements for companies and drug name examples. This enriches the schema.

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

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

The description clearly states the tool's purpose: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It specifies the verb (compare/rank), resource (companies or drugs), and distinguishes from sibling tools like entity_profile by noting it should be preferred over sequential single-pack 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?

The description provides explicit usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and gives example query patterns. It explains when to use the tool (comparing entities) and what happens (results sorted by primary metric). It could be more explicit about when not to use it, but the context is clear.

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

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

Annotations already declare the tool as readOnly, openWorld, idempotent, non-destructive. The description adds important behavioral context: account requirement, paid plan for 'thorough' depth, expected latency (15-60s), return format (findings packet with gaps, citations), and that it never invents facts. 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 somewhat long but well-structured: starts with requirements, then alternative, core functionality, examples, and caveats. Every sentence serves a purpose, though it 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?

Despite lacking an output schema, the description thoroughly explains the return format (findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, and gaps[]). It covers complexity, constraints, and edge cases (open-world topics returning empty 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%, but the description adds significant value: it explains the 'depth' enum values ('quick=3, standard=5, thorough=8'), clarifies that 'question' is natural language and suitable for broad/multi-part queries, beyond what the schema provides.

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

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

The description clearly states the tool's purpose: 'Grounded multi-source research across Pipeworx's 1130 STRUCTURED data sources.' It specifies the verb (research), resource (structured data), and distinguishes from open-web search and sibling tools like ask_pipeworx.

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

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

The description provides explicit guidance on when to use this tool ('broad/multi-part questions over structured data') and when not ('single lookup' or 'breaking/current news'). It directly names alternatives: ask_pipeworx for single queries and breaking news.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds that it returns top-N tools with full schemas and curated examples, ready to call directly. 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?

Well-structured with front-loaded purpose, then details, then usage advice. Slightly verbose but every sentence adds value.

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

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

For a tool with 6 params, no output schema, but rich annotations, the description covers purpose, usage, return value, and parameter aliases comprehensively.

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

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

Schema coverage is 100% and each parameter is already described with aliases. The description does not add significant meaning beyond the schema; baseline 3 applies.

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

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

Clearly states 'Find tools by describing the data or task' and lists many specific domains. Distinguishes from siblings like search_within by focusing on discovery.

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 'Call this FIRST when you have many tools available' and gives examples of when to use. Provides clear context for when to use vs. not.

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 safe, read-only, idempotent operation. Description adds details about fanning out across sources, soft-fail for patents, and fallback mechanisms. No contradiction.

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

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

The description is relatively long but well-structured, front-loading purpose and usage. It could be slightly more concise but each sentence adds value.

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

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

Without output schema, the description fully covers return values, limitations (only companies, names not supported), and edge cases (patent sunset, fallback). Very complete.

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

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

High schema coverage (100%) with descriptions in schema. Description reinforces input formats (ticker/CIK) and provides examples, adding value beyond schema.

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

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

The description clearly states the tool provides a full cross-source profile of a US public company, lists specific data sources and return fields, and distinguishes from siblings by recommending it over chaining single-pack 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 says when to use (holistic view) and when not (use resolve_entity for names). Provides alternative tool for names.

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, so the tool is non-destructive. The description adds useful context: it normalizes feeds and is CF-robust with a fallback proxy. Missing details like rate limits or return format, but still 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?

Two concise sentences, front-loaded with the primary action. Every sentence adds value: first states what it does, second adds robustness detail and usage guidance. 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 no output schema, annotations handle safety, and sibling tools are listed, the description covers the core behavior well. Could mention default limit or response format, but for a straightforward fetch tool it is sufficient.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description does not add extra meaning beyond the schema; it only mentions 'by URL' which echoes the url parameter. 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 verb 'Fetch and normalize' and the resource 'any RSS / Atom / RDF feed by URL'. It differentiates from siblings like 'list_feeds' which is for curated sources, and 'read_feed' which likely reads subscribed feeds.

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 guidance to use 'list_feeds first for curated sources', indicating an alternative. However, it does not specify when not to use this tool or mention any prerequisites.

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, so the description's 'Delete' and 'clear sensitive data' add some context but are consistent. However, it does not disclose behavior for missing keys or other edge cases, which is a minor gap given the annotations already convey destructiveness.

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

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

Two sentences with no redundancy: first sentence defines purpose, second provides usage guidance. Front-loaded and 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 simple one-parameter destructive tool with no output schema, the description adequately covers purpose, usage, and pairing. It lacks explicit mention of error handling (e.g., what if key doesn't exist), but given the context signals and annotations, it is largely complete.

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

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

Schema description coverage is 100% (one parameter 'key' fully described). The description does not add additional parameter meaning beyond the schema, so a baseline score 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 'Delete a previously stored memory by key,' specifying the action and resource. It distinguishes itself from sibling tools 'remember' and 'recall' by explicitly pairing with them.

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

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

The description provides explicit when-to-use guidance: 'Use when context is stale, the task is done, or you want to clear sensitive data.' It also mentions pairing with 'remember' and 'recall' as alternatives, offering clear usage context.

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

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

Annotations declare readOnly, openWorld, idempotent, non-destructive. Description adds that it fetches the page and extracts data, aligning 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?

Three sentences, front-loaded with main purpose, no redundancy. Efficient and informative.

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?

Simple tool with no output schema; description explains output format and use. Complexity is low, and description fully covers what the tool does and produces.

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

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

Schema covers 100% of parameters. Description adds no new semantic details beyond schema (e.g., url and max_links are already described). Baseline 3 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?

Describes specific verb 'generate' and resource 'llms.txt' with additional context about fetching and emitting. Distinct from sibling tools like ai_visibility_check.

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?

Lists explicit use cases (client indexing, own project drafting, competitor auditing). Does not explicitly mention when not to use or suggest alternatives, but context is clear.

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

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

Annotations already provide readOnlyHint, destructiveHint, idempotentHint, openWorldHint. Description adds behavioral context about optional filtering and redirect to read_feed, which is complementary and non-contradictory.

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 immediately state purpose, return fields, and optional filters. No wasted words, well front-loaded.

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

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

For a simple list tool with 2 optional parameters and no output schema, the description fully covers what the tool returns and how to use it, including a pointer to read_feed for detail.

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. Description adds context about filtering by category or keyword but does not add significant new meaning beyond what schema provides.

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

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

The description clearly states the tool lists curated law & courts feeds with specific fields (id, title, category, source) and distinguishes itself from the sibling read_feed by instructing to pass an id to that 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?

Provides clear context for when to use (listing feeds with optional filters) and hints at alternative read_feed for detail retrieval, but lacks explicit when-not-to-use or exclusion of other siblings.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds return fields and parameter default behavior, complementing annotations without contradiction.

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

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

Two sentences plus a list of returned fields. No wasted words, front-loaded with core action and resource.

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?

Simple tool with one optional param. Description covers purpose, usage, return fields. No output schema needed for this level of detail.

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 description for the single parameter. Description does not add meaning beyond schema, so baseline 3 applies.

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

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

Clear verb 'List' and resource 'subscriptions'. Specifies caller's active subscriptions and lists returned fields. Distinguishes from siblings like subscribe/unsubscribe.

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

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

Explicit guidance: use to review monitoring before adding more or to find an id to cancel. No exclusion of alternatives but context is clear.

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

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

The description goes beyond annotations by disclosing rate limits ('Rate-limited to 5 per identifier per day'), impact ('signal directly affects roadmap'), and quota status ('Free; doesn't count against your tool-call quota'). Annotations are all false (readOnlyHint, destructiveHint, etc.), and the description aligns with a non-destructive, write operation. No contradictions.

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

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

The description is concise at 4 sentences, front-loaded with purpose, then usage rules, then constraints. Every sentence adds necessary information without redundancy. It is well-structured for quick reading by an AI agent.

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

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

For a feedback tool with no output schema, the description covers when to use, how to structure input, and rate limits. It lacks post-submission behavior details (e.g., no confirmation response), but this is acceptable for a tool that simply sends feedback. The description is mostly complete given the tool's simplicity.

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: for the 'type' parameter, it elaborates enum values with examples (e.g., 'bug = something broke'). For 'message', it adds constraints ('1-2 sentences typical, 2000 chars max') and guidance ('Be specific...'). This provides actionable details beyond the schema definitions.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies the action 'tell' and the resource 'Pipeworx team'. It distinguishes from sibling tools by listing specific use cases (bug, feature, data_gap, praise) and contrasts with data retrieval or research tools like ask_pipeworx and deep_research.

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

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

The description provides explicit when to use: for bugs, feature/data gaps, or praise. It also gives guidelines like 'don't paste the end-user's prompt' and 'Describe the issue in terms of Pipeworx tools/packs.' It mentions rate limits and that it's free. However, it doesn't explicitly state when NOT to use it in favor of specific sibling tools, relying on the user to infer from context.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds source (CF analytics-engine), no PII, and caching duration, which enriches understanding 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?

Four sentences, no unnecessary words. Front-loaded with main functionality. 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?

For a simple tool with one parameter and no output schema, the description covers purpose, usage, and behavioral details adequately. No gaps identified.

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

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

Schema covers 100% with enum and description. Description adds semantic guidance on window choice (hot vs. steady-state), exceeding baseline.

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

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

The description clearly states it returns top tools, packs, and call volume over a window, with a specific verb and resource. It distinguishes from siblings by focusing on aggregated calling patterns of other agents.

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 three explicit use cases (hot data, canonical tool confirmation, alignment). Mentions caching behavior. Lacks 'when not to use', but context is strong.

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

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

Annotations already provide readOnlyHint, openWorldHint, etc. Description adds rich context: failure mode, monotonicity logic, semantic anchor, partition filter, fill check details, and response structure. 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 dense but well-structured with clear sections (REQUIRES, SEMANTIC ANCHOR, etc.). It is front-loaded with key requirement. Slightly long but every sentence earns its place given 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?

Given no output schema, the description thoroughly explains return fields (opportunities[], partition_check, fill check). It also references related tools for custom sizing, making it 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 descriptions for both parameters, but description adds significant value by explaining accepted formats (slug vs URL) and behavior differences between modes, beyond what schema provides.

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

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

The description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) with specific examples, and differentiates from sibling tools like polymarket_edges and polymarket_fill_risk.

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

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

Explicitly states that one of event or topic is required, and gives clear guidance on when to use each mode. Mentions fill check and when not to trade, and references sibling tool 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 declare readOnlyHint=true and idempotentHint=true, and the description adds rich behavioral context: caching policy (1h KV-level keyed on all knobs), diagnostics explaining empty segments, and tradeable-edge filters. 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 comprehensive but excessively long as a single dense paragraph. It could benefit from structured lists or sections. While front-loaded with purpose, the volume of detail reduces scanability.

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 details the response structure (by_segment, diagnostics) and caching behavior, making the tool self-contained for an agent to understand inputs, outputs, and edge cases.

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

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

Schema coverage is 100% and the description adds extra meaning beyond the schema, e.g., explaining 'min_kelly' skips small opportunities and 'max_spread_pp' filters wide spreads. Slightly above baseline due to added context.

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

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

The description clearly states the tool scans top Polymarket markets and returns opportunities where Pipeworx data disagrees with market price, differentiating itself from siblings like polymarket_arbitrage by focusing on multi-segment opportunity discovery without paging hundreds of markets.

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

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

The description explicitly positions the tool for 'what should I bet on today' discovery, mentions fed markets are excluded from ranking, and details tradeable-edge knobs. However, it doesn't explicitly contrast with 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?

Annotations declare readOnly, openWorld, idempotent, and non-destructive. The description adds context: data comes from daily snapshots, history bounded by 60-day TTL, decay computed from daily closes (not intraday), and snapshot dates indicate when scans occurred. This provides useful behavioral detail beyond annotations.

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

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

The description is front-loaded with purpose, then details response format and limits. It is somewhat long but every sentence adds necessary information. Could be slightly tighter, but overall well-structured for the 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?

No output schema, but the description fully explains the response structure: tracked array with time-series, trend, decay; expired array with lifespan_days; snapshot_dates. It also covers limits (TTL, intraday limitation). This provides complete context for the agent to understand the output without needing an output schema.

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

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

Schema coverage is 100%, so baseline is 3. The description repeats parameter defaults and clarifies the clamp for days (max 30 vs schema 'clamp 2-30'). This adds minor value but does not significantly deepen parameter understanding beyond the schema.

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

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

The description explicitly states the tool provides 'edge persistence and decay telemetry' and answers 'how long has this edge existed and is it shrinking?'. It distinguishes from raw edge data by contrasting fresh vs old edges. This clearly identifies the tool's unique function.

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

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

The description implies the tool is for analyzing edge persistence over time, but does not explicitly state when to use it versus alternatives like polymarket_edges or polymarket_arbitrage. No 'when-not' or direct comparison to siblings.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds further context about walking the ladder, returning verdicts (clean/degraded/cannot_fill), and highlighting thin legs and directional risk. No contradictions.

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

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

The description is detailed and front-loaded with purpose, but becomes verbose with extensive lists of return fields and risk explanations. While all content is relevant, it could be more concise for an agent to parse quickly.

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 complexity of the tool (two modes, risk analysis, no output schema), the description covers all necessary aspects: input parameters with defaults, behavioral details, output fields, and explicit usage guidance. It feels complete for an agent to decide when and how to use it.

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 extra meaning: explains side default behavior per mode, the interpretation of size_usd for single-market vs basket (max spend vs target proceeds vs settlement notional), and clamp range of 10–1,000,000. This exceeds 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 it performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and differentiates two modes (single-market and basket). It lists specific return fields (e.g., top_of_book, vwap_fill_price, verdict) and distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by prescribing when to use it.

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 THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Elaborates on the risks of partial fills and uncapturable overround, providing clear when-to-use and why.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds extensive behavioral context: compatibility conditions, temporal alignment, skipped cross types, and explains when spreads are meaningful vs. not. 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 informative but somewhat dense; could be structured with bullet points for readability. However, every sentence adds value and it is front-loaded with core purpose.

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

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

Despite no output schema, description explains response structure (leg prices, spread array, safety fields like compatibility_warning, temporal_alignment). Covers edge cases and what to expect, making it complete for agent invocation.

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

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

Schema covers all 3 parameters with descriptions; description adds further semantics: explains topic list, override behavior, and provides examples. Adds value beyond schema.

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

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

Clearly states it computes cross-venue spread between Kalshi and Polymarket for the same resolving question. Differentiates from sibling tools like polymarket_arbitrage by specifying cross-venue focus.

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

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

Explicitly describes two modes (topic shortcuts vs. explicit custom pairing) and warns that most pre-mapped topics currently return compatibility_warning, advising cautious use. Provides clear when-to-use guidance.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that the tool returns normalized items with specific fields (title, link, published, summary), which is useful context beyond annotations. No contradiction.

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

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

The description is two sentences, front-loaded with the main action, and every phrase serves a purpose. No wasted words.

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

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

For a simple read tool with 3 parameters and no output schema, the description covers the return fields and optional filtering. It lacks mention of pagination or error handling, but given the limitations, it is fairly complete.

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

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

Schema coverage is 100% with parameter descriptions. The description mostly restates what is in the schema (feed id source, limit range, keyword filter). It adds marginal value over the schema, so baseline 3 is appropriate.

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

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

The description clearly states the tool reads a curated law & courts feed by ID from list_feeds, returns normalized items with specific fields, and allows optional keyword filtering. This is specific and distinguishes from siblings like list_feeds and fetch_feed.

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 the prerequisite of getting the feed ID from list_feeds, which guides usage. It also explains when to use the query parameter for filtering. However, it does not explicitly contrast with fetch_feed or provide when-not-to-use guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by explaining the scoping by identifier and the behavior when key is omitted. 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 three sentences, front-loaded with the primary action. Every sentence adds unique value: purpose, usage guidance, and pairing with siblings. No wasted words.

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

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

For a simple tool with one parameter, the description adequately covers retrieval and listing. It could benefit from mentioning return format, but given no output schema, this is a minor gap.

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

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

Schema coverage is 100% with a clear description of the key parameter. The description reiterates the same information without adding new semantics, so baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves a value saved via remember or lists all keys when omitted. It provides specific use cases like user's target ticker or research notes, and distinguishes it from sibling tools remember and forget.

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

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

The description explicitly says 'Use to look up context the agent stored earlier' and mentions the scoping mechanism. It implicitly pairs with remember and forget, but does not explicitly state when not to use or provide alternatives, though this is sufficient given the simple 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 transparently explains behavioral traits beyond annotations: it discloses that the tool returns alerts from a persisted feed, that setting mark_read:true flags events as read to affect subsequent calls, and that polling is safe. Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so this adds context about state changes on marking read and feed persistence, 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 and well-structured, with a single paragraph that front-loads the core purpose and then adds specific details about returned fields, filtering, mark_read behavior, and alternative access. Every sentence adds value without redundancy.

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

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

Given the 5 parameters with good schema descriptions, the absence of an output schema, and the presence of annotations, the description covers essential behaviors: what is returned (fields), filtering, and side effects. However, it could briefly mention pagination or default ordering, but overall it is adequate for a simple alert-fetching 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?

The description adds meaningful context beyond the input schema, which already has 100% coverage. It explains that the type parameter filters by subscription type, that since expects an ISO timestamp, that limit controls max events, and that mark_read:true 'flag[s] returned events read so the next call only shows newer ones.' This enriches the schema definitions.

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

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

The description clearly states the tool's purpose: 'Pull fired events from your subscription feed. Returns the most recent alerts...' It specifies the resource (alerts from subscription feed) and the verb (pull/return). It also highlights key fields returned (source, citation_uri, raw payload) and filtering options, distinguishing it from sibling tools like fetch_feed or read_feed, which handle different feed types.

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 usage guidance by mentioning that 'Polls work fine' and that the same feed is available via a REST endpoint for scripts/dashboards. It implies that this tool is for subscription alerts, while alternatives might exist for other purposes. However, it lacks explicit when-not-to-use instructions or direct comparisons with sibling tools.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral details beyond annotations: it explains the external data sources, fallback logic, and the soft-fail for USPTO, as well as the return format with citation URIs.

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

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

The description is dense and front-loaded with example queries, but it is one paragraph without breaks. Every sentence adds value, but it could be slightly more structured with bullet points for readability.

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

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

Given the tool has 3 required parameters, no output schema, and complexity from multiple data sources, the description is complete: it explains the return format, fallbacks, limitations (USPTO soft-fail), and references the sibling tool entity_profile for a different use case.

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

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

The schema already covers all parameters with descriptions (100% coverage). The description adds further context: for 'since' it explains accepted formats (ISO date or relative shorthand) and gives typical usage recommendations; for 'value' it specifies ticker or CIK; for 'type' it notes 'company' only.

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

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

The description states the tool provides a change feed for a company, with specific examples like "What's new with X" and sources such as SEC EDGAR, GDELT/GNews, and USPTO. It distinguishes itself from the sibling tool entity_profile by specifying when to use that 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?

The description provides explicit guidance on when to use this tool (e.g., for latest updates) and when to use entity_profile for static profiles. It also mentions fallback behavior (GDELT→GNews). However, it does not explicitly list when not to use it.

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

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

Discloses key behavioral traits beyond annotations: key-value scoping by identifier, 24-hour retention for anonymous sessions, persistent for authenticated users. No annotation 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 concise sentences with front-loaded purpose, when-to-use, and technical details. No extraneous text.

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 required params and no output schema, the description fully explains storage mechanism, persistence, and relationship with sibling tools.

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

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

Schema coverage is 100%, but description adds value with example key conventions (subject_property, target_ticker) that guide parameter formatting.

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 saves data for later reuse with specific examples (resolved ticker, target address) and differentiates from siblings recall and forget.

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

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

Provides explicit when-to-use guidance ('when you discover something worth carrying forward') and pairs with recall/forget, but lacks explicit when-not-to-use scenarios.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds substantial behavioral details: it cascades through several lookup endpoints internally, returns specific fields (e.g., ticker, CIK, company_name), and provides citation URIs. There is 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 moderately long but well-structured, starting with user-facing examples, then usage guidance, then detailed type support. Each sentence adds value, though a slight trimming could be possible. It is front-loaded with the most critical information.

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

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

Despite lacking an output schema, the description fully explains what is returned for each entity type, including citation URIs and internal cascading behavior. It covers the tool's complexity comprehensively for an AI agent to use 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%, and the description enriches both parameters: for 'type' it lists supported values with return fields, and for 'value' it provides input format examples (ticker, CIK, name) and notes auto-disambiguation. This goes well beyond the schema 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 resolves names to canonical IDs, provides specific example phrases ('What's the ticker for…'), and distinguishes it from sibling tools by noting it replaces 2-3 manual lookups. The verb 'resolve' and resource 'entity' are specific and 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 says 'Use FIRST whenever you have a name but need an ID,' giving clear guidance on when to invoke it. However, it does not explicitly mention when not to use it or compare to specific sibling tools like compare_entities, which slightly lowers the score.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the safety profile is covered. The description adds behavioral context: it internally calls 'ai_visibility_check', returns a ranked list with score/confidence/signal density, and requires an optional API key for some models. This enriches the agent's understanding beyond annotations.

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

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

The description is three sentences: purpose+method, use case, output format. It is front-loaded with the key action, and every sentence adds value without redundancy or fluff. Highly 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 tool's complexity (4 params, no output schema), the description covers the return format, internal call, and parameter usage. It lacks discussion of potential errors or performance limits, but for a read-only batch comparison tool, the coverage is sufficient. The schema specifies entity count limits, which are integrated into the description.

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 4 parameters. The description adds additional semantics: it specifies that the first entity is treated as the 'subject', clarifies model defaults, and gives example context. This provides value beyond the schema and helps the agent use parameters correctly.

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

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

The description clearly states the verb 'compare', the resource 'AI visibility', and that it operates across multiple entities side-by-side. It specifies the method: probes each entity with 'ai_visibility_check' and ranks results. This distinguishes it from sibling tools like 'ai_visibility_check' 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?

The description provides explicit context for when to use the tool: 'competitive AI-marketing audits' and illustrates with a question. It implies that for a single entity check one would use 'ai_visibility_check', but lacks explicit when-not-to-use or alternatives. Thus, clear but not exhaustive.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds useful context: bundlephobia's first measurement can take 5-30s, and sources_failed will list it if timed out. This complements annotations well.

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

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

Single dense paragraph front-loads core action and use cases. While slightly verbose, every sentence adds value. Could benefit from slight structuring but efficient 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?

Without output schema, description fully explains return fields: summary block, per-advisory detail, links, alternative versions. Also covers error handling and graceful degradation. Very complete.

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

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

Schema coverage is 100% (both package and version described). Description adds value by noting scoped packages accepted and version defaults to latest, which is beyond schema definitions.

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

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

Description clearly states composite check for npm packages, combining deps.dev and bundlephobia data. Distinguishes from siblings by noting NPM ecosystem only and referencing separate tools 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: 'whenever an agent asks is X safe/popular/small or what does adding lodash cost me'. Also provides ecosystem constraint (NPM only) and graceful degradation hints.

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

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

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, openWorldHint=true. The description adds substantial behavioral details: 200K char truncation with flag, BGE-base-en embeddings with cosine similarity over 500-char windows, and return of offsets and scores. 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 a single, well-structured paragraph that front-loads the core purpose. Every sentence is informative and 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?

Despite no output schema, the description fully explains return values (passages with character offsets and similarity scores) and operational constraints (cap, flagging). It is complete for the tool's intended use.

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

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

Schema coverage is 100%, so baseline 3. The description adds value by explaining the truncation limit for 'text' (max ~200K chars), providing natural-language query examples for 'query', and specifying the range and default for 'limit' (1-20, default 5).

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, specifying input (text + query) and output (top-N passages with offsets and scores). It also distinguishes from the sibling tool 'ask_pipeworx_grounded' by explaining the pairing use case.

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

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

The description explicitly advises use when 'the record is too big to cram into the prompt' and provides concrete examples (SEC 10-K, article, long tool result). While it lacks explicit when-not statements, 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?

The description adds significant behavioral context beyond annotations: authentication requirements, supported types with specific parameter details, delivery channel options and constraints (SMS cap, webhook auto-disable). However, it does not clarify idempotentHint=true behavior (whether duplicate subscriptions are avoided or allowed), leaving a gap.

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

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

The description is front-loaded with a clear one-sentence purpose, then systematically covers types and delivery channels. While lengthy, every sentence adds unique information. Slight room for tighter phrasing but remains well-structured.

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

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

The description covers authentication, subscription types, parameter details, and delivery channels thoroughly. However, it omits error handling (e.g., SMS cap exceeded, webhook failure), return values (no output schema), and the idempotency behavior implied by annotations. These gaps reduce completeness for a complex tool with nested objects and multiple required parameters.

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 baseline is 3. The description enriches parameter understanding with concrete examples (e.g., items codes for sec_8k, series_id for fred_series) and explains delivery channels in detail, adding value beyond the schema's property descriptions.

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

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

The description clearly states the action ('Create a proactive monitoring subscription to a live-data event stream') and the resource ('subscription'), distinguishing it from sibling tools like list_subscriptions and unsubscribe. Extensive examples of supported types and delivery channels reinforce the tool's purpose.

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

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

The description specifies prerequisites (Pipeworx OAuth account) and outlines available subscription types with parameter examples, helping the agent decide when to use the tool. However, it lacks explicit instructions on when not to use it or comparisons to alternatives, though the context from sibling names partially compensates.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds significant behavioral context: it returns structured example questions, explains the output format (category-bucketed with tool calls), and details how to use arguments. 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 somewhat lengthy but well-structured: starts with trigger phrases, explains purpose and output, then usage. It is front-loaded with key information, though some sentences could be condensed 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?

Despite having no output schema, the description fully explains the output (category-bucketed example questions with tool calls). It covers purpose, when to use, argument options, and related tools. Adequate for the tool's simplicity.

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

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

Schema coverage is 100% for the single parameter. The description enriches the schema by providing concrete example topic values (finance, pharma, etc.) and explaining the behavior when omitted (full spread), adding significant meaning.

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

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

The description clearly states the tool's purpose: it's the onboarding entry point for a new agent, returning category-bucketed example questions with exact tool+argument shapes. It distinguishes from siblings like ask_pipeworx, discover_tools, etc., by highlighting its unique role.

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

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

The description explicitly says when to use this tool ('Use this FIRST when you do not yet know what Pipeworx can do'). It also lists related tools (ask_pipeworx, entity_profile) as things to learn about, providing context. It does not explicitly state when not to use, but the guidance is clear.

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

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

Annotations already indicate idempotent and non-destructive. The description adds key behavioral details: ownership enforcement, deactivation (not deletion), and impact on historical events. No contradictions.

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

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

Two sentences only: first states the action, second adds constraints and side effects. Every sentence is essential, front-loaded with the primary purpose.

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

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

For a simple tool with one parameter and no output schema, the description fully covers purpose, behavior, constraints, and references sibling tool for historical data. 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 has 100% coverage and describes the id parameter clearly. The description does not need to add further parameter semantics, as the schema already explains the parameter's origin and type.

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 by id') and specifies the resource and parameter. It differentiates from siblings by mentioning ownership enforcement and deactivation instead of deletion.

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

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

Provides clear context on ownership enforcement (only cancel own subscriptions) and explains that the row is deactivated not deleted, with a note about historical events via recent_alerts. Does not explicitly state when to avoid using, but the guidance is sufficient.

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

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds rich behavioral detail: returns a verdict (with specific enum values), extracted structured form, actual value with pipeworx:// citation, and percent delta. It also mentions the data source and that it replaces 4-6 sequential calls, providing transparency about performance benefits and limitations.

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

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

The description is a single paragraph that front-loads intuitive usage examples, then states purpose, domain, and return value details. Every sentence adds value, but it could be slightly more structured (e.g., bullet points for return fields). However, it remains concise and clear.

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

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

Given the one-parameter input, no output schema, and the complexity of claim verification, the description provides a thorough explanation of what the tool does, its domain, and its return format. It does not cover edge cases (e.g., non-financial claims), but for a v1 tool, it is sufficiently complete.

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

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

With 100% schema description coverage, the schema already documents the 'claim' parameter adequately. The description does not add significant new information about the parameter beyond the examples shown in the schema, but the overall tool description reinforces the context. Baseline 3 is appropriate when schema covers the parameter fully.

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 begins with specific natural-language examples like 'Is it true that…' and 'fact check', clearly identifying the tool's purpose as claim verification against authoritative sources. It explicitly defines the domain (company-financial claims for public US companies via SEC EDGAR + XBRL), which distinguishes it from sibling tools like deep_research or entity_profile.

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

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

The description states 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear when-to-use guidance. It also specifies the v1 limitation to company-financial claims, implicitly excluding other claim types. While it does not explicitly list when not to use or provide alternative tools, the context is sufficient for most cases.

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