Bcrp Pe

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

The description supplements annotations by disclosing the default model, the need for an API key for Anthropic (with cost implications), and the return structure. This goes beyond what annotations alone provide, fully informing the agent of side effects and expectations.

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 (three sentences) with front-loaded purpose, efficient use of words, and no filler. Every sentence adds crucial information: operation, parameters, use cases.

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

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

Given the tool's moderate complexity (4 parameters, no output schema), the description covers purpose, parameters, behavioral notes, and example use cases comprehensively. It leaves no obvious gaps for an agent to misinterpret.

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 to parameters: it explains the default model for the 'models' parameter, the cost implications of '_apiKey', and how 'context' aids disambiguation. Since schema coverage is already 100%, this extra value merits a top score.

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

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

The description clearly states the tool probes LLMs for knowledge about entities and provides a visibility score (0-100). It distinguishes from sibling tools by focusing on multi-model visibility scoring, unlike e.g., ask_pipeworx which queries a single specific source.

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

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

The description explicitly lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring), guiding when to use it. However, it does not mention when not to use it or provide alternatives, missing a small opportunity for clearer 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, and destructiveHint=false. Description adds that it returns structured answers with stable pipeworx:// citation URIs and routes questions internally, which is consistent and adds context about auto-argument filling and result format.

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 key instruction, followed by examples and sibling comparisons. Slightly verbose but every sentence adds value; could be trimmed slightly but remains 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 no output schema, the description implies return format (structured answer with citations). Covers purpose, usage, parameter aliases, examples, and comparison with 5+ sibling tools. Fully contextualized for a complex tool.

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

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

Schema coverage is 100% with descriptions for all aliases and the main question parameter. Description adds no additional parameter details beyond examples, 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?

Clear verb+resource: asks questions and routes to 4,862 tools across 1272 sources. Distinguishes from siblings like ask_pipeworx_grounded and deep_research by explaining when each is appropriate.

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 'PREFER OVER WEB SEARCH' and provides specific query types (SEC filings, FDA data, etc.) and example user intents. Clearly states when to step up to sibling tools for grounded answers or broad 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?

Beyond annotations (readOnlyHint, etc.), description details internal routing, evidence extraction from tool results only, and precise refusal reasons, providing comprehensive behavioral insight.

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 thorough but slightly verbose; however, it is well-structured with clear sections (purpose, output, usage, tradeoff), making it efficient for an 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?

Covers all necessary aspects: purpose, output format with success/refusal cases, usage boundaries, and cost implications, leaving no gaps despite the tool's complexity.

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

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

Schema coverage is 100%, and description clarifies that the question is used in natural language to route to the right tool, adding context beyond the aliases listed in 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?

Description clearly defines a hallucination-resistant answer mode for high-stakes reads, using specific verbs ('extracts', 'returns') and distinguishes itself from sibling 'ask_pipeworx' by explaining the grounded approach.

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 (answers to be quoted/cited/acted on) and when to prefer sibling 'ask_pipeworx' (casual lookups), with a cost tradeoff mentioned.

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

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

Annotations declare readOnlyHint, idempotentHint, and destructiveHint false. Description adds extensive behavioral details: market resolution, classification, fan-out, safety mechanisms (low-confidence short-circuit, closed markets, wide spreads, resolution-rule risk), response shapes, and evidence source behaviors. This far exceeds what annotations provide.

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

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

Description is very long and dense with multiple sections. While it front-loads the main purpose in the first sentence, the lengthy classifiers, fan-out examples, response shapes, and safety notes could be better organized. Conciseness suffers but structure is adequate 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?

Given high complexity, the description covers all aspects: inputs, outputs, fan-out logic by category, response fields, safety warnings, resolution rules, and edge cases. Without an output schema, it thoroughly explains return values ('RESPONSE SHAPES', 'RESOLVER CONTRACT', 'NEWS 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%, so baseline is 3. Description adds significant context: explains market input types via examples, clarifies depth with 'quick' vs 'thorough', and describes include_raw with rationale. Adds value 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 the tool researches Polymarket bets by pulling Pipeworx data. It specifies three input types (slug, URL, question text) and outputs an evidence packet with market-model comparison. Distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on data research rather than edge detection or arbitrage.

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

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

Explicitly states use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. Provides clear context for when to invoke but does not mention when not to use or alternatives among siblings.

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

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

Description reveals real-time data sourcing, off-calendar fiscal year handling, result sorting, and citation URIs. Annotations already indicate safety; description adds behavioral details beyond them.

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

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

Well-structured with example queries upfront, then details. Lengthy but each sentence adds value. Could be slightly more concise without losing 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?

Covers input, behavior, and output (paired data with URIs) despite no output schema. For a complex tool with two entity types, description is fully informative.

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

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

Adds significant meaning: explains data each 'type' retrieves, gives examples for 'values', specifies constraints (2-5 items, tickers/CIKs vs drug names). Schema coverage is 100%, description enhances 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?

Description clearly states it performs side-by-side comparison of 2-5 companies or drugs, specifying data sources and fields. Distinguishes from siblings by emphasizing it replaces sequential lookups.

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

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

Explicitly prefers this tool over sequential lookups with 'ALWAYS PREFER'. Provides example queries and explains when to use company vs drug type. No explicit negatives 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 declare readOnlyHint and idempotentHint, but description adds critical context: it is NOT open-web search, uses parallel tools, returns gaps and contradictions (for thorough), and provides time expectations. 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 lengthy but well-structured, front-loading critical requirements (account, sign-in, alternative). Every sentence adds value, though it could be slightly more concise for the complexity it covers.

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

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

Despite no output schema, the description fully explains the return format (findings packet with evidence, confidence, source, fetched_at, citation_uri, gaps, contradictions for thorough) and behavior for different depths. Complete for understanding and 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%, and description adds significant meaning: for depth, explains each enum value with numbers and features (quick=3 single hop, standard=5 with gap recovery, thorough=8 with contradictions). For question, clarifies natural language and broad questions are fine.

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 'grounded multi-source research across Pipeworx's 1272 STRUCTURED data sources' and decomposes questions into facets. It distinguishes itself from ask_pipeworx (single lookup) and other sibling tools.

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

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

Explicitly states when to use (broad/multi-part questions over structured data) and when not to (breaking news, current news; use ask_pipeworx). Also mentions account requirement and alternative for unpaid users.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds value by stating the tool returns top-N relevant tools with full schemas and examples, ready to call without second schema lookup. This extra context earns a 4.

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

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

Five sentences, no fluff. Starts with purpose, lists use cases, describes return format, and gives best practice. Every sentence is meaningful and well-structured.

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

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

For a discovery tool with 100% schema coverage and no output schema, the description fully explains functionality: returns top-N tools with schemas and examples, ready to call. No gaps remain given the context.

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

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

Schema coverage is 100%, baseline 3. Description adds value by providing example queries and explaining that parameters like task, q, description, search are aliases for query. This exceeds bare schema documentation.

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

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

The description clearly states it finds tools by describing data/task, uses specific verb 'Find tools' and resource 'tools'. It distinguishes itself from sibling tools like search_within, as no other tool provides a discovery 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?

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist' and lists domains. Also advises 'Call this FIRST when you have many tools', providing 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=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds valuable behavioral context such as parallel fan-out across sources, the USPTO API sunset and soft-fail behavior, and the specific return structure. While it doesn't cover every edge case (e.g., rate limits), it substantially supplements the annotations.

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

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

The description is comprehensive but slightly verbose. It front-loads with examples and uses a structured list for returned fields, which aids readability. A minor trim of the field list or less example phrasing could improve conciseness, but overall it's well-organized 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?

Given the tool's complexity (multiple data sources, diverse return fields), the description covers all key aspects: input format, output components, source-specific notes (e.g., USPTO sunset), and cross-references to sibling tools. The absence of an output schema is compensated by the detailed field enumeration. The rich annotations further lighten the description's burden.

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 critical meaning beyond the schema. It specifies that 'type' only supports 'company' (with person/place coming soon) and that 'value' must be a ticker or zero-padded CIK, explicitly ruling out names. This directly addresses common agent misunderstandings and reduces errors.

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

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

The description opens with concrete examples like 'Tell me about X' and explicitly states it returns a 'full cross-source profile of a US public company in ONE parallel call.' It lists all data sources (SEC, XBRL, USPTO, news, GLEIF) and returned fields, making the purpose unmistakable. It also distinguishes itself from siblings like 'resolve_entity' by clarifying that names are not supported.

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: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also provides a clear when-not: 'names not supported (use resolve_entity first if you only have a name).' This gives the agent precise direction on when to use this tool versus alternatives.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. Description confirms destructive behavior ('Delete', 'clear sensitive data') but adds minimal new behavioral info. No contradiction.

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

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

Two efficient sentences: first defines core purpose, second provides usage guidance and tool pairing. No redundant words; every sentence adds value.

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

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

Given simple tool with one parameter and comprehensive annotations, description fully covers purpose, usage, and relationships. No output schema needed; context is complete.

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

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

Input schema has 100% coverage, so schema already describes the 'key' parameter adequately. Description does not add extra parameter details beyond referencing deletion by key, meeting baseline expectations.

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

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

The description states 'Delete a previously stored memory by key', clearly identifying the verb (delete) and resource (memory by key). It distinguishes from siblings by naming 'remember' and 'recall' as companion tools, making the purpose unambiguous.

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

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

Provides explicit use cases: 'context is stale, the task is done, or you want to clear sensitive data'. Mentions pairing with remember/recall for context. Lacks explicit when-not-to-use but sufficient for typical 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 false. The description explains the fetch-extract-emit process, confirming non-destructive behavior. While it adds process details, it doesn't address auth or rate limits, but annotations already cover safety profile.

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 bullet list. Front-loaded with core function, no unnecessary words. Every sentence adds value.

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

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

Explains output format and use cases, compensating for missing output schema. Does not cover error handling or limitations (e.g., inaccessible URLs), but is adequate for a simple tool with rich annotations.

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

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

Schema coverage is 100%, so baseline is 3. The description essentially repeats the schema parameter descriptions without adding new meaning or constraints beyond what's already in the input 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 generates an llms.txt file for a URL, specifying the target AI crawlers and output format. It distinguishes well from sibling tools, none of which are related to llms.txt generation.

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

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

Explicitly lists use cases (getting client's site indexed, drafting for own project, auditing competitor). Does not mention when not to use or alternatives, but given sibling diversity, this is acceptable.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: the data source (BCRP API), code suffix encoding frequency, period format details, language options, and the constraint that all codes must share the same frequency. No contradictions with annotations.

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

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

The description is moderately dense but efficiently packs essential information: purpose, input format, examples, period formatting rules, and a link for more codes. Each sentence adds value, though it could benefit from structured bullets 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 no output schema, the description adequately explains the return format (series names + period/value rows). It covers input constraints (same frequency, period format) and provides enough examples to ensure correct usage. No essential details are missing.

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

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

With 100% schema coverage, the description still adds significant meaning: it explains the joining format of series codes, provides examples, details per-frequency period formats, and explains lang enum values. This goes well beyond the schema descriptions.

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

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

The description clearly states it retrieves time-series data from the BCRP statistics API, specifying the exact input format (series codes joined with '-'), the output (series names plus aligned period/value rows), and provides concrete examples of well-known codes. This is a specific verb+resource that distinguishes it from any sibling tools.

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

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

The description explains what the tool does but does not explicitly state when to use it versus alternatives or when not to use it. While the sibling list contains no similar data retrieval tools, the lack of usage context is a minor gap.

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 return field details but no behavioral surprises beyond what annotations convey.

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

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

Two sentences, no wasted words. First sentence states purpose and output, second gives usage guidance. Ideal length and structure.

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

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

No output schema, but description lists return fields. Parameters fully documented in schema. Annotations cover behavior. Sufficient for a simple read-only list tool.

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

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

Schema coverage is 100% with parameter description. Description does not add additional context about the parameter beyond what schema provides, meeting 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?

Description clearly states 'List the caller's active subscriptions' with expected return fields. Distinguishes from sibling tools like subscribe and unsubscribe by specifying the action of listing.

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

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

Explicitly says when to use: 'to review what you're monitoring before adding more or to find an id to cancel.' Does not list alternatives but context with siblings implies appropriate use.

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

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

With no annotations providing behavioral hints, the description fully discloses key traits: rate limiting, free usage, no quota impact, and that the team reads digests daily. It also warns against pasting user prompts, which is a behavioral constraint.

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, covering all key aspects in a few sentences. It front-loads the purpose and follows with actionable guidance. Minor structure improvement possible (e.g., bullet points), but still clear and 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 simplicity (no output schema, few parameters), the description covers important context: when to use, rate limits, quota impact, and team attention. It could mention the lack of a response, but this is a minor omission.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by elaborating on the 'type' enum values (e.g., 'bug = something broke') and the optional context fields (pack, tool, vertical). It also provides message formatting guidance (be specific, 1-2 sentences, 2000 chars max).

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies distinct use cases (bug, feature/data_gap, praise) and distinguishes itself from sibling tools like ask_pipeworx and discover_tools.

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

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

The description provides explicit when-to-use guidance for bugs, feature requests, data gaps, and praise. It also tells the agent what not to do (don't paste end-user prompts), mentions rate limits (5 per identifier per day), and notes that it's free and doesn't count against quota.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds valuable context: data derived from CF analytics-engine, no PII, caching behavior (5min-1h). This goes beyond annotations without contradicting them.

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

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

Three well-structured sentences: first states purpose, second lists use cases, third adds technical details. Every sentence adds value, no redundancy.

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

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

Given the tool's simplicity (1 optional param, no output schema), the description covers all needed context: return value structure, window interpretation, data source, privacy, caching. 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% with a description for the only parameter. Description adds meaning by explaining the trade-off between windows ('what's hot right now' vs 'steady-state demand'), beyond the enum list.

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

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

The description clearly states the tool returns 'top tools, top packs, and total call volume' over a window, with specific verb 'returns' and resource 'trending data'. It distinguishes itself from siblings like discover_tools by focusing on current call volume.

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

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

Explicitly lists three use cases (discovering hot data sources, confirming canonical choice, aligning with agent needs) and advises on window selection ('shorter windows surface what's hot right now; longer windows show steady-state demand'). No when-not needed given 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 indicate safe read-only, open-world, idempotent behavior. The description adds extensive behavioral context: algorithmic details (monotonicity, partition checks, Jaccard similarity, placeholder filters), output structure, fill check logic, and limitations (skipped_low_similarity, placeholders filtered). No contradictions with annotations.

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

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

The description is detailed but well-structured, with a clear front-loaded purpose. It uses paragraphs effectively and covers all aspects without excessive verbosity. Minor redundancy could be trimmed, but overall it earns its length given the tool's complexity.

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

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

Given the tool's complexity (two modes, fill check, semantic and partition filters), the description is remarkably complete. It describes the response structure, internal algorithms, and edge cases, and references a sibling tool for deeper usage. No output schema is 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 description coverage is 100%, and the description adds substantial meaning beyond the schema. It explains the event slug format with examples, describes the topic parameter with seed question examples, and clarifies the internal logic for each mode, making the parameters' semantics rich and actionable.

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: finding arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes between modes (trending scan, event, topic) and explains the underlying algorithms, making the purpose specific and distinct 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?

The description provides explicit usage guidance: call with no args for trending scan, pass `event` for single-event mode, `topic` for cross-event scanning. It recommends `event` for specific markets, explains when to use `topic`, and includes a fill check condition to avoid trading if realizable_edge_pp ≤ 0, plus a reference to polymarket_fill_risk for custom sizing.

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

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

Adds extensive behavioral context beyond annotations: details edge calculation (slippage, Kelly fractions, model families), response structure (by_segment, diagnostics), caching, and caveats like Fed markets. Annotations provide safety, but description is comprehensive.

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?

Very detailed and well-organized, but quite lengthy. Could be more concise while retaining key info. First sentence is clear, but overall verbosity reduces conciseness.

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

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

Given the tool's complexity (9 parameters, no output schema), the description thoroughly covers output structure, filtering logic, and diagnostics. Also includes caching behavior. Complete for the tool's purpose.

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

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

Schema coverage is 100%, so baseline is 3. The description adds valuable context for many parameters (e.g., slippage_pp explains Polymarket fees, min_liquidity suggests setting to 5000), justifying a higher score.

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

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

The description clearly states the tool scans Polymarket markets and returns opportunities where Pipeworx data disagrees with market price. It distinguishes from sibling tools like polymarket_arbitrage by focusing on proprietary edge signals.

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 it's built for 'what should I bet on today' and saves agents from paging hundreds of markets. Lacks explicit when-not-to-use or 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?

Adds significant detail beyond annotations: history depth bounded by 60-day TTL, snapshot dates reflect scan gaps, decay from daily closes not intraday, and full response structure. Annotations already cover readOnly, idempotent, non-destructive.

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: starts with purpose, then details response fields, then limits. 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?

Comprehensively explains the response (tracked, expired, snapshot_dates) and limitations (TTL, gaps). No output schema, but description provides sufficient detail for effective use.

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

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

Schema coverage is 100%, so baseline is 3. Description clarifies 'window' as snapshot family (24hr, 1wk, 1mo) and 'days' lookback with defaults, adding context beyond the schema descriptions.

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

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

The description clearly states 'Edge persistence and decay telemetry' and answers a specific question about edge age, distinguishing it from sibling tools like polymarket_edges which likely provide current edges.

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

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

Implies usage for assessing edge persistence but does not explicitly state when to use this tool versus alternatives like polymarket_edges or polymarket_arbitrage. No '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 readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable behavioral details: it walks the order book ladder, returns a verdict (clean/degraded/cannot_fill), and explains that theoretical edge may not be capturable. No contradictions with annotations.

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

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

The description is well-structured with clear sections for single-market and basket modes, and front-loaded with the purpose. While every sentence provides value, slightly verbose for the density of information, earning a 4 rather than 5.

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 lack of output schema, the description covers all return fields for both modes, usage warnings, and risks. Minor gap: it doesn't explicitly explain how to interpret the verdict or handle cases where neither market nor event is provided (schema allows that, but description says required).

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

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

Schema coverage is 100%, but the description adds significant meaning: it explains the two modes requiring different parameters (market vs event), interprets size_usd for buys vs sells and basket notion, and provides defaults. This compensates for any lack of detail in the schema descriptions.

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

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

The description clearly states the tool performs a 'realizable-vs-theoretical edge check' against live order book depth, with two distinct modes (single-market and basket). It specifies exact return fields and explicitly differentiates itself from sibling tools like polymarket_arbitrage and polymarket_edges.

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

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

The description explicitly advises using this tool before acting on arbitrage signals or trades above $500, warning about thin books and partial fills. It lacks explicit 'when not to use' guidance but provides clear context on the recommended usage 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?

Discloses two modes, safety fields (compatibility_warning, temporal_alignment), and counters for skipped comparisons. Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false; the description adds significant behavioral context without contradiction.

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

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

The description is relatively long but well-structured with clear sections (modes, response, safety fields). Every sentence adds value, though some redundancy could be trimmed. 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?

Given the complexity (cross-venue comparison, multiple modes, safety fields) and lack of output schema, the description thoroughly explains behavior, returned fields (leg-by-leg prices, top_spreads_pp, compatibility_warning, temporal_alignment, skipped counters), 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% with descriptions for each parameter. The description adds context about mode relationships and parameter interactions (overrides), going beyond the schema's field-level 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 computes cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes two modes (topic shortcuts and explicit tickers) and contrasts with related sibling tools like polymarket_arbitrage.

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

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

Explicitly states when to use topic mode vs explicit mode, warns that most pre-mapped topics currently return compatibility_warning, and explains conditions under which spreads are not meaningful (e.g., incompatible bet shapes, temporal misalignment).

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

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

Adds context beyond annotations: scoped to identifier, pairs with remember/forget. No contradictions with readOnlyHint etc.

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

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

Concise at 4 sentences, front-loaded with primary function. 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?

Complete for a simple retrieval tool: explains what, how, scoping, and pairing. No missing information given schema and annotations.

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

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

Schema coverage is 100% with clear description of 'key' parameter. Description reiterates the schema's note about omitting key to list, adding no extra semantics.

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 'Retrieve a value previously saved via remember, or list all saved keys' with specific verb and resource. Distinguishes from sibling tools like remember and forget.

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

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

Provides clear context on when to use: to look up stored context like ticker or notes. Mentions scoping but doesn't explicitly list alternatives or when not to use.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds details: returns source/citation_uri/payload, mark_read flag updates read status, polling works fine. No contradictions, and adds 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?

Three sentences, front-loaded with purpose, no wasted words. Each sentence adds essential information. Ideal length for quick understanding.

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

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

Given 5 optional parameters, no output schema, description covers return format (source, citation_uri, payload), behavior of mark_read and unread_only, polling suitability, and alternative access URI. Complete for a read-only list tool.

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

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

Schema coverage is 100%, but description enriches each parameter with beyond-schema meaning: type example 'sec_8k', since explanation 'events fired_at >= this time', mark_read notes 'so next call only shows newer ones', unread_only explains 'read_at is null'. This adds significant value.

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

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

Description clearly states 'Pull fired events from your subscription feed' and specifies return fields (source, citation_uri, raw event payload). Distinguishes from siblings by focusing on alert feed retrieval, and even notes an alternative access method (GET endpoint).

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?

Gives clear context for when to use (polling) and provides alternative (direct GET endpoint for scripts/dashboards). Also explains filtering by type/since and the mark_read flag behavior. Could be more explicit about when not to use, but 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?

Beyond annotations (readOnlyHint, idempotentHint), the description details the fan-out to multiple sources (SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail), fallback logic, and return structure (changes by source, total count, citation URIs), adding rich behavioral context.

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

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

The description is well-structured: example queries first, then data sources, parameter details, return format, and sibling comparison. Every sentence contributes meaningful information without redundancy.

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

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

Despite no output schema, the description covers return structure (changes grouped by source, total_changes, URIs), source behaviors, parameter options, and fallback logic, fully addressing the tool's complexity.

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

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

Schema coverage is 100%, but the description adds value: 'since' examples and recommendation, 'value' clarification (ticker or CIK), 'type' constraint (only 'company'), enhancing understanding beyond schema descriptions.

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

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

The description clearly states the tool's purpose: providing a change feed for a company (e.g., 'latest on Y', 'updates on Acme') via a single parallel call. It distinguishes from sibling '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?

It provides explicit when-to-use examples ('What's new with X', 'latest on Y'), recommends using '30d' or '1m' for typical monitoring, and directly contrasts with 'entity_profile' for static profile needs, offering a clear alternative.

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

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

Adds key behavior beyond annotations: scoped by identifier, persistent vs 24-hour retention for anonymous users. Annotations indicate idempotent and non-destructive; description aligns and provides extra context.

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

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

Concise and well-structured. Front-loaded with purpose, then usage, then behavior. Every sentence adds value, no redundancy.

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

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

For a simple key-value write tool without output schema, the description covers input, behavior, persistence, and pairing with sibling tools. Complete and sufficient.

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

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

Schema description coverage is 100% with clear examples. The description reinforces usage but adds minimal new information about the parameters beyond what schema already provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool saves data for later reuse, using a key-value pair. It distinguishes from sibling tools recall and forget, making the purpose unambiguous.

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

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

Provides specific scenarios when to use (resolved ticker, target address, etc.) and pairs with recall/forget. Lacks explicit when-not-to-use guidance, but still clear and helpful.

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

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

Adds significant detail beyond annotations: cascading multiple lookups, auto-disambiguation for company, specific return fields (ticker, CIK, citation URI; RxCUI, ingredient, brand). Annotations already provide readOnlyHint, idempotentHint, etc., and description aligns and enriches.

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: starts with example queries, then usage instruction, then detailed supported types. Every sentence is informative and earned. Slightly long but not verbose.

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

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

Covers parameter usage and return values comprehensively despite no output schema. Includes examples, supported types, and internal behavior. Lacks mention of error handling or rate limits, but acceptable given annotations.

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

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

Schema has 100% coverage with basic descriptions. Description adds value by explaining permissible values for 'value' (ticker, CIK, name for company; brand/generic for drug) and clarifies the 'type' enum. Enhances schema without redundancy.

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

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

Description clearly states the tool resolves user-spoken names to canonical identifiers, with specific example queries (ticker, CIK, RxCUI). It explicitly instructs 'Use FIRST whenever you have a name but need an ID', distinguishing it from sibling tools like compare_entities 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?

Provides clear context: use when you have a name but need an ID. Includes examples of queries. Does not explicitly state when not to use or list alternatives, but the context is sufficient for agent selection.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description explains that it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It adds behavioral context not captured by 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, mechanism, and example use case. It is concise and well-structured 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 explicitly states return values (ranked list with score, confidence, signal density). It explains the probing mechanism and parameter context, making it complete for this tool's complexity.

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

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

Schema description coverage is 100%, so the description adds marginal value. It clarifies the entities parameter role (first is subject) and provides examples for context and apiKey, but does not significantly extend schema info.

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 'Compare AI visibility across multiple entities side-by-side', using a specific verb and resource. It distinguishes from sibling tools like ai_visibility_check by focusing on multi-entity comparison.

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

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

The description provides clear usage context: 'Useful for competitive AI-marketing audits: does Claude know about us as well as our competitors?'. It implies when to use but does not explicitly state when not to use or list alternatives.

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

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

Annotations already indicate safe read-only behavior. The description adds critical behavioral details: partial failures degrade gracefully, bundlephobia first measurement can take 5-30 seconds, and failed sources are listed. No contradictions with annotations.

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

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

The description is front-loaded with the main purpose and uses a colon to introduce details. It is somewhat lengthy but every part adds value. Could be slightly more concise but overall well-structured.

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

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

Despite no output schema, the description lists all return fields, partial failure behavior, and ecosystem constraints, making it highly complete for a composite tool. The agent can confidently use it without guessing output.

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

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

Schema coverage is 100% with parameter descriptions. The description adds context for scoped packages and default version behavior, which is helpful but not extensive. Baseline 3 plus slight improvement yields 4.

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

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

The description clearly states the tool's purpose as a composite check for adding npm packages, mentioning fan-out across deps.dev and bundlephobia. It lists specific return fields and distinguishes itself from sibling tools like 'scan_competitor_ai_presence' by focusing on npm packages.

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 to use when an agent asks about safety, popularity, or size. It also notes the npm-only scope and directs other ecosystems to a different tool. However, it does not provide explicit exclusion scenarios.

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

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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds value beyond annotations by detailing the embedding model (BGE-base-en), window size (500-char), character limit (200K chars), truncation flag, and return fields (character offsets, similarity scores). No contradictions.

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

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

The description is a single, well-structured paragraph that front-loads the main action. Every sentence adds value (usage context, technical details, pairing with sibling). 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 return values (passages, offsets, similarity scores). It covers constraints (200K char cap, truncation flag), model details, and pairing advice. For a complex tool, this is 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%, but the description adds meaning beyond the schema: for 'text' it specifies max 200K chars, for 'limit' it states range (1-20) and default (5), and for 'query' it provides natural-language examples. This enriches the 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 it performs semantic search inside a fetched record, using a specific verb+resource ('semantic search INSIDE a fetched record'). It distinguishes from siblings by mentioning it pairs with ask_pipeworx_grounded, and the context signals list siblings with different purposes.

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: 'Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter…' It also mentions pairing with ask_pipeworx_grounded, providing 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 indicate readOnlyHint=false, idempotentHint=true, destructiveHint=false. The description adds behavioral details beyond annotations: subscription creation requires OAuth, webhook signing secret is returned once, and delivery channels have specific constraints (e.g., SMS cap, verification). No contradictions with annotations. The description enriches the behavioral model.

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 main purpose and then elaborates on types, delivery channels, and requirements. It is concise yet comprehensive; every sentence adds value. Slightly dense but 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 complexity (3 parameters, nested objects, no output schema), the description covers purpose, parameters, supported types, delivery options, prerequisites, and limitations (SMS cap, verification). It explicitly states the return value: 'Returns the new subscription id.' It could mention more about error scenarios, but overall it is complete enough.

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

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

Schema coverage is 100%, but the description adds significant meaning: for params, it gives detailed examples for each type (e.g., 'sec_8k: {ticker:"AAPL", items?:["5.02","1.01"]}'). For delivery, it explains the feed is always on and adds email, SMS, webhook specifics (signing secret, auto-disable). This substantially aids parameter usage.

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

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

The description clearly states the tool's purpose: 'Create a proactive monitoring subscription to a live-data event stream.' It specifies the verb (create), resource (subscription), and context (live-data event stream). It lists supported types and delivery channels, distinguishing it from siblings like 'unsubscribe' and 'list_subscriptions'.

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

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

The description provides explicit usage context: 'Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions).' It details when to use each type and delivery channel, including rate limits (10/day cap for SMS) and prerequisites (phone verification). It could be more explicit about when not to use, but the context is clear enough for selection.

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

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) already indicate safe, non-destructive behavior. The description adds that it returns 'category-bucketed example questions... with exact tool+argument shape drawn from live catalog,' and that calling with no arguments gives the full spread. No contradictions with annotations.

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

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

Description is 4-5 sentences, front-loaded with the key purpose 'What can I ask Pipeworx?' Every sentence contributes value: purpose, parameter usage, and tie to sibling tools. No unnecessary fluff.

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

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

For a simple read-only tool with one optional parameter and no output schema, the description fully explains output format (category-bucketed questions), parameter effect, and use case. No gaps given the low complexity and rich annotations.

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

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

Schema coverage is 100% with the optional 'topic' parameter already described. The description adds usage examples ('Call with no arguments for the full spread, or pass topic') but does not provide fundamentally new meaning beyond the schema. Baseline of 3 is appropriate.

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

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

The description clearly states it is the 'onboarding entry point' that returns category-bucketed example questions with exact tool+argument shapes. It distinguishes from sibling tools like 'discover_tools' by positioning itself as the first tool to call when the agent doesn't know what Pipeworx can do.

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

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

Explicitly says to use it 'FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains the effect of the topic parameter (focus on a category). No explicit when-not-to-use, 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?

Adds significant behavioral context beyond annotations: ownership enforcement, deactivation instead of deletion, and preservation of historical events. Annotations already indicate non-destructive (destructiveHint=false) and write operation (readOnlyHint=false), but description elaborates on the exact behavior.

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

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

Two efficient sentences with front-loaded action and added constraints. No redundant information; every word contributes to understanding the tool's purpose and behavior.

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 all needed aspects: action, parameter, constraints, and side effects. The sibling list provides context for related 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% with a single parameter 'id' described as 'Subscription id (uuid) returned by subscribe.' The description mentions 'by id' but adds no further semantics beyond what the schema provides. Baseline 3 applies.

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

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

The description clearly states the verb 'cancel' and the resource 'subscription by id', directly addressing the tool's function. It distinguishes from siblings like 'subscribe' and 'list_subscriptions' by specifying the action and target.

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 mentions ownership enforcement and the effect on the row (deactivated, not deleted). While it doesn't explicitly state when not to use, the context from sibling tools and the clear action make it straightforward.

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

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

The description adds behavioral context beyond annotations: it returns a verdict with specific types (confirmed, refuted, etc.), uses SEC EDGAR + XBRL, and replaces 4-6 sequential calls. Annotations already indicate readOnly, openWorld, idempotent, non-destructive, and the description aligns with these.

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 every sentence adds value. It is front-loaded with natural-language examples and clearly structured. Minor redundancy could be trimmed, but overall effective.

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

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

Given the tool's complexity and no output schema, the description adequately covers return values (verdict, structured form, actual value, citation, delta) and limitations (v1 supports company-financial claims). It provides sufficient context for agent decision-making.

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

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

The schema covers the 'claim' parameter with 100% description coverage. The description enhances it by specifying the domain (company-financial) and providing natural-language examples, adding meaning beyond the schema.

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

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

The description clearly states the tool verifies natural-language factual claims against authoritative sources, specifically for company-financial data. It provides example queries and distinguishes from siblings by noting it replaces multiple sequential calls.

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

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also specifies the domain (company-financial claims for public US companies). However, it does not explicitly mention when not to use or alternatives, though the sibling tools are listed.

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