Merriam Webster

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

Annotations already indicate readOnlyHint, idempotentHint, openWorldHint. Description adds beyond: explains BYO key for Anthropic, payment responsibility, return structure (per-model and combined view). No contradictions.

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

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

Five well-structured sentences, front-loaded with purpose and key details. No fluff, every sentence adds value.

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

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

Despite no output schema, description explains return format (per-model objects + combined view). Covers all parameters, use cases, and model options adequately.

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 with descriptions. Description adds value by stating default model for 'models' parameter, clarifying that _apiKey is only needed for Anthropic, and explaining how 'context' disambiguates.

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?

Purpose is crystal clear: 'Probe one or more LLMs for what they know...and score visibility'. Specific verb 'probe', resource 'LLMs', and output 'visibility score'. Distinguishes from siblings by focusing on LLM knowledge and scoring.

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: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. Provides guidance on optional parameters (_apiKey, context). Lacks explicit when-not-to-use vs alternatives, but context signals list many 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 indicate read-only, idempotent, non-destructive. Description adds behavioral details: routes to one of 5,008 tools, returns structured answer with citation URIs, works on every tier, one fast call. 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?

Front-loaded with key instruction 'PREFER OVER WEB SEARCH'. Description is informative but slightly long; however, each part adds value. Could be more concise but still 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?

No output schema, but description explains return value: 'structured answer with stable pipeworx:// citation URIs'. Covers context of use, limitations (step up to grounded or deep_research), and examples. Complete for a query-routing 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 has 100% coverage with all param aliases documented as the 'question'. Description provides examples but no additional semantic depth beyond what schema offers. 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?

Description clearly states it answers factual questions by routing to verified sources, lists extensive use cases, and distinguishes from siblings ask_pipeworx_grounded and deep_research. Specific verb 'ask' and resource 'Pipeworx' with clear scope.

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', 'START HERE for most questions', and gives step-up conditions for alternatives (ask_pipeworx_grounded for verbatim evidence, deep_research for multi-part). Examples illustrate appropriate queries.

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, idempotentHint, destructiveHint. The description adds key behavioral context: source-preference ranking, identical behavior to stable except routing improvements, and that it's a full working router. 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 focused paragraph, front-loaded with 'Beta version of ask_pipeworx'. Every sentence adds unique value: similarity, improvements, usage, fallback. No wasted words.

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

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

Given the tool's complexity (routing 5,011 tools), the description covers all critical aspects: beta nature, identical interface, specific improvement (source-preference), when to use, and that it's a full router without fallback. No output schema needed as response shape is same as stable.

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

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

Schema coverage is 100%, so the schema already explains the single essential parameter 'question' and its aliases. The description adds no additional parameter semantics beyond stating it has the same arguments as ask_pipeworx, which is already known. Score meets 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's a beta version of ask_pipeworx, a universal router with 5,011 tools, and explicitly differentiates it by mentioning candidate routing improvements and source-preference ranking. It distinguishes from stable ask_pipeworx and ask_pipeworx_grounded 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?

The description advises using it 'exactly like ask_pipeworx when you want the newest routing' and notes results are compared against the stable router. It implicitly guides when to use (testing new routing) vs. when not (production), and mentions it's a full working router with no fallback.

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, idempotentHint, destructiveHint=false. The description adds valuable behavioral details: the refusal mechanism (four possible refusal reasons), the cost of an extra LLM call, and that it extracts answer only from tool result. 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?

Well-structured: front-loaded with purpose, then mechanics, then use cases. Every sentence adds value. Concisely explains complex behavior in a focused manner.

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 (answer, evidence, confidence, source, fetched_at, refusal_reason) and the refusal reasons. Combined with the annotations and schema, the description is complete enough for an agent to understand and use the tool correctly.

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

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

Schema description coverage is 100%. The description mentions that question accepts natural language and lists aliases (q, text, input, query, prompt), but does not add significant meaning beyond what the schema already provides. Score at baseline.

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

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

The description clearly states the tool's purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It explains the process (picks tool, fetches data, extracts answer from result only) and distinguishes it from the sibling ask_pipeworx by noting the extra LLM call and refusal mechanism.

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

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

Explicitly tells when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with examples (financial verdicts, legal claims, medical lookups, public statements). Also provides a when-not: 'prefer ask_pipeworx for casual lookups.'

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

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

The description extensively discloses behaviors beyond annotations: low-confidence resolution short-circuits, closed/dead market handling, wide-spread tradeability warnings, resolution-rule risk, parent event extraction, and response shapes. Annotations already indicate read-only, idempotent, non-destructive, and the description adds rich behavioral details 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 very long and packed with details, but it is structured with clear sections (classifiers, fan-out examples, response shapes, safety, etc.) and front-loads the core purpose. It earns its length due to complexity, but could be more concise.

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

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

Despite no output schema, the description comprehensively covers all relevant aspects: input formats, resolution logic, classifier categories, fan-out examples, response fields (market, analysis, evidence), resolver contract, parent event extraction, news handling, safety mechanisms, and resolution-rule risk. It is fully complete for an agent to understand how to use and interpret results.

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

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

Schema coverage is 100%, but the description adds significant context: for 'market' it explains acceptable formats (slug, URL, question), for 'depth' it describes 'quick' vs 'thorough' meaning, and for 'include_raw' it explains when to use true vs false. This adds value beyond the schema's parameter descriptions.

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

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

The description states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies the verb 'research' and the resource 'Polymarket bet', and distinguishes from sibling tools like ask_pipeworx or deep_research by focusing on betting decisions with market resolution, classification, and fan-out.

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 provides use cases: 'Use for 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z'. It gives clear context for when to use the tool, but does not explicitly state when not to use or provide 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 readOnlyHint=true, destructiveHint=false, etc. The description adds valuable behavioral context: it discloses the data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), fiscal year handling, sorting by primary metric, and return of pipeworx:// citation URIs. No contradictions with annotations.

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

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

The description is lengthy but efficiently packed with useful information. It front-loads trigger phrases and key action, then details data sources and output. Minor redundancy could be trimmed, but structure is logical 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 has no output schema, the description compensates by explaining return data (paired data + citation URIs) and sorting behavior. It covers entity-specific metrics and fiscal year handling. All parameters are fully explained, and no additional context is needed for a tool of this complexity.

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

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

Schema coverage is 100%, providing baseline of 3. The description significantly enhances both parameters: for 'type' it explains the exact metrics pulled per entity type, and for 'values' it specifies tickers/CIKs for company and names for drug, with min/max items. This adds rich meaning beyond the schema's brief 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 side-by-side comparison of 2-5 entities (companies or drugs) in one parallel call. It explicitly distinguishes from sibling tools by stating 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' which differentiates it from entity_profile and similar single-entity 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 clear trigger phrases (e.g., 'compare X and Y', 'which is bigger', 'head to head') and advises to prefer this tool over sequential lookups. However, it does not explicitly state conditions when not to use this tool or mention alternatives beyond the preference note.

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

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

Disclosures align with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint). Adds details on processing time (15-60s, thorough up to 90s), gap recovery, contradictions, hop field, citation URIs, and semantic excerpting. 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?

Long but well-organized with clear sectioning and explanatory details. Each sentence adds value; could be slightly condensed but remains efficient given 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 complexity of the tool (multi-hop, gap recovery, contradictions), the description covers all necessary aspects: prerequisites, use cases, depth behavior, return format (findings packet), and limitations. No output schema but return values are explained.

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

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

Schema covers both parameters (question, depth) with 100% description. The tool description adds significant value: details depth options (quick=3, standard=5, thorough=8 facets) and behavior per depth, enhancing interpretation beyond schema.

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

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

The description clearly states it performs grounded multi-source research across 1318 structured data sources in one call, and distinguishes from siblings like ask_pipeworx for single lookups or breaking news. Examples clarify use cases.

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

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

Explicitly defines when to use deep_research (broad/multi-part questions over structured data) and when to use alternatives (ask_pipeworx for single lookups or breaking news). Also notes account requirements and paid tier for "thorough" depth.

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

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

Annotations already provide readOnlyHint=true and idempotentHint=true. The description adds value by clarifying that results include full input schemas with curated examples and are 'ready to call directly, no second schema lookup needed,' which goes 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 extremely concise: two sentences plus a domain list. It front-loads the purpose and usage, then immediately states return value. Every sentence adds value with no waste.

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

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

For a simple discovery tool with 100% schema coverage and good annotations, the description fully explains what the tool does, when to use it, and what it returns. No output schema is needed, and the description covers all necessary 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 description coverage is 100%, so baseline is 3. The description mentions that 'query' accepts natural language and lists aliases, but this is already in the schema. No additional meaning is added 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: 'Find tools by describing the data or task.' It uses a specific verb (find) and resource (tools), and distinguishes itself from sibling tools by being a meta-discovery tool that returns ready-to-call results.

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: 'Call this FIRST when you have many tools and want to see the option set.' It also provides concrete examples of when to use (e.g., 'browse, search, look up, or discover what tools exist for') and implies it's not for direct answers.

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 read-only, idempotent, open-world, and non-destructive. The description adds value by detailing the data sources (SEC, XBRL, USPTO, GDELT, GLEIF) and the specific fields returned, including a soft-fail note for patents. It also explains the parallel fan-out behavior. No contradictions with annotations.

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

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

The description is a single dense paragraph that is well-structured: it starts with natural language query examples, then states the core purpose and preference, lists data sources and returned fields, and ends with parameter usage guidance. Every sentence adds value; there is no filler.

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

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

Given the complexity of aggregating multiple data sources and the absence of an output schema, the description covers the essential returned fields and sources. It notes constraints (US public company only, patents soft-fail, name unsupported). It could mention error handling or ordering of news/fundamentals, but it is sufficiently 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 coverage is 100% (both parameters described in schema and description). The description adds meaning beyond the schema by providing concrete examples ('AAPL', '0000320193'), clarifying that names are not supported (directing to resolve_entity), and explaining the type parameter constraint (only 'company' for now).

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

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

The description clearly defines the tool as providing a holistic cross-source profile of a US public company in a single parallel call. It distinguishes itself from chaining separate lookups by explicitly stating 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups'. It also specifies the output components (CIK, filings, fundamentals, patents, news, LEI).

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: prefer this tool for holistic profiles over chaining alternatives. It also gives when-not-to-use: names are not supported, and 'use resolve_entity first if you only have a name'. It lists alternative tools implicitly by mentioning the separate lookups that should not be chained.

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

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context by mentioning deletion of sensitive data, confirming destructive nature, without contradicting annotations.

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

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

Two sentences, front-loaded with action, followed by usage guidance and pairing. 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 covers purpose, usage, and tool relationships. It could be more complete by mentioning return behavior or error handling for missing keys, but is adequate for a simple deletion 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 'Memory key to delete'. The description does not add additional meaning beyond the schema, so 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 uses specific verb 'Delete' and resource 'a previously stored memory by key', and distinguishes from siblings by mentioning pairing with 'remember' and 'recall'.

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

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

The description provides explicit contexts for use: 'when context is stale, the task is done, or you want to clear sensitive data'. It implies alternatives by referencing 'remember' and 'recall', but lacks explicit when-not-to-use statements.

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 meaningful context: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This clarifies the internal process without contradicting annotations.

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

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

The description is a single paragraph of 4 sentences, concise and front-loaded with the main purpose. Every sentence adds value: purpose, process, output format, and use cases. No wasted words.

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

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

Given no output schema, the description explains the output as 'a single text blob ready to drop at site-root/llms.txt.' It also covers use cases and the extraction process. While it omits error handling or timing, the tool is simple and the description is sufficient for an AI agent.

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

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

Schema coverage is 100% with clear descriptions for both parameters (url and max_links). The description adds an example for url and mentions default/max for max_links, but does not significantly enhance understanding beyond the schema. 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 the tool's purpose: generating an llms.txt file for any URL. It specifies the output format and target audience (AI crawlers like ChatGPT, Claude, Perplexity). This distinguishes it from sibling tools like ai_visibility_check or scan_competitor_ai_presence, which have different functions.

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

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

The description provides explicit use cases: indexing a client's site, drafting own project's llms.txt, or auditing a competitor. It does not give negative guidance (when not to use), but the specificity of the tool makes the use case clear. The absence of alternatives is acceptable due to unique functionality.

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

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

Annotations already declare read-only and non-destructive; description adds return fields and parameter detail, reinforcing safe behavior. 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, front-loaded with action, no redundant 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 optional boolean param and no output schema, description fully covers purpose, usage, and return 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 covers 100% of parameter description; description adds no additional meaning beyond the schema's own description.

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

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

Clearly states it lists active subscriptions, with specific fields returned. Distinct from siblings like subscribe and unsubscribe.

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

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

Explicitly advises use cases: review before adding more or find id to cancel. No ambiguity.

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

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

Annotations provide no safety hints (destructiveHint false, etc.). Description adds critical behavioral info: rate-limited to 5 per identifier per day, free, doesn't count against quota, team reads daily, signal affects roadmap. 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?

Concise at 4 sentences. Front-loads purpose and use cases. Every sentence earns its place: no redundancy, no fluff.

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

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

Given 3 params with nested objects and no output schema, description covers all necessary guidance: type, context (optional), message format. Rate limit and quota are mentioned. No gaps for a feedback submission tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds significant value beyond schema: explains each enum value with examples, advises message specificity (which tool, error, data missing), and sets length expectation (1-2 sentences, 2000 chars). Context object usage is also implied.

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

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

Description starts with a clear verb-resource pair ('Tell the Pipeworx team something is broken, missing, or needs to exist') and enumerates specific use cases (bug, feature, data_gap, praise). This clearly distinguishes it from sibling tools, which are for queries, actions, or 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?

Explicitly states when to use (wrong data, missing tool, praise) and what not to do (don't paste end-user prompt). Also mentions rate limits and quota impact, guiding appropriate usage.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds useful context: derived from analytics engine, no PII, caching window of 5min-1h. 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?

Four sentences, each adding unique value. Front-loaded with purpose, then usage, then behavioral details. No redundant or filler content.

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

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

Despite no output schema, description explains return structure (top tools, top packs, call volume) and caching behavior. Sufficient for a simple aggregation tool with one parameter.

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 the single optional parameter with enum and description. Description adds value by explaining that shorter windows surface current trends and longer windows show steady-state demand.

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 returns top tools, top packs, and total call volume over a specified window. Uses specific verbs and resource, distinguishing it from sibling tools like ask_pipeworx 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 three concrete use cases (discovering hot data, confirming canonical tools, aligning use case), but does not explicitly mention when not to use or list 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description goes far beyond by detailing the algorithmic checks (monotonicity, partition sum), semantic anchor (Jaccard similarity threshold), partition filter (placeholder slugs), and the fill check step (pricing against live CLOB depth). This provides rich behavioral context not captured in annotations.

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

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

The description is well-structured with capitalized section headers (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and front-loaded with the main purpose and mode selection. It is slightly verbose with detailed numerical thresholds (e.g., 'deviations >3pp', '≥0.30 Jaccard similarity'), which could be streamlined, but overall 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 complexity of the tool (multiple modes, algorithmic checks, output format), the description is exceptionally thorough. It covers all input scenarios, the logic of the checks, the response structure (opportunities[], partition_check fields, fill check results), and even references a sibling tool for custom sizing. No output schema exists, so the description fully shoulders the burden of explaining return values.

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 substantial meaning beyond the schema descriptions. It explains the expected input formats (slugs, URLs, seed questions), how each mode processes the input, and what the tool will do with the parameters (e.g., walks child markets, searches related events). This goes far beyond a simple baseline.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes: no args (trending_scan), event mode, and topic mode, each with specific behaviors and use cases.

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

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

The description provides explicit guidance on when to use each mode: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', and 'topic (for cross-event scanning)'. It explains the rationale for each mode and advises against trading when fill check shows non-realizable edge. It also references a 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?

The description adds extensive behavioral context beyond annotations: caching behavior ('Cached 1h at the KV level'), model family details, output structure (by_segment, diagnostics), and special handling for Fed bets. Annotations already mark the tool as read-only and idempotent, and the description reinforces this 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 very detailed and long (several paragraphs), which, while informative, sacrifices conciseness. It could be structured more tightly by separating high-level purpose from model-specific technical details. The front-loading is good (purpose first), but overall length reduces clarity.

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

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

Given 9 parameters and no output schema, the description covers all critical aspects: return structure (by_segment, diagnostics), model behaviors, caching, parameter trade-offs, and edge cases (Fed bets). It provides enough detail for an agent to use the tool correctly without additional context.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful practical guidance for each parameter (e.g., for min_liquidity: 'Set to 5000 to drop thin-book opportunities...'), improving semantics beyond mere schema descriptions. No parameters lack elaboration.

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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It uses a specific verb ('Scan') and resource ('top Polymarket markets'), and distinguishes from siblings like polymarket_arbitrage by focusing on edge detection and the 'what should I bet on today' 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 explains when to use the tool ('Built for "what should I bet on today"') and provides guidance on parameter knobs (min_liquidity, max_spread_pp) for filtering. It also explicitly excludes Fed bets from ranking with reasoning. However, it does not directly contrast with sibling tools like polymarket_arbitrage, leaving some ambiguity about when to use which.

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

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

Annotations already declare read-only and idempotent nature. The description adds useful context about data bounds (60-day snapshot TTL, gaps meaning no scan) and response details, going 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 structured with a clear purpose statement, response breakdown, and limits. While verbose, it is well-organized and front-loaded, earning a high score for 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 response fields (tracked, expired, snapshot_dates) and limitations. It is fully complete for a read-only telemetry tool.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds minor defaults and clamping ranges but does not significantly enhance parameter 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 it provides edge persistence and decay telemetry from daily snapshots, answering specific trading questions about edge duration and trend. It effectively distinguishes from sibling tools like polymarket_edges by focusing on historical analysis.

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

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

While the description implies usage for historical edge analysis, it lacks explicit guidance on when to use this tool versus alternatives like polymarket_edges. No when-not or alternative suggestions are provided.

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

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds operational details: walks ladder, returns specific fields, warns about partial fills and directional risk. No contradictions; enhances transparency 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 lengthy but well-structured with clear sections for single-market and basket modes. All sentences contribute necessary detail given tool complexity; only minor redundancy could be trimmed.

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

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

Given no output schema, the description fully explains return values (top_of_book, vwap_fill_price, etc.) and risks. Covers both modes, parameter behavior, and when to use. 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 significant meaning: explains size_usd interpretation differs per mode and side defaults are context-dependent. This adds value 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 the tool performs a 'realizable-vs-theoretical edge check against live CLOB order-book depth', distinguishing it from siblings like polymarket_arbitrage by specifying two modes (single-market and basket) and explicitly stating when it should be used before acting on other 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 says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500', and explains why (theoretical overround not capturable on thin books, partial fills create unhedged directional risk).

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

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description reveals key behaviors: it auto-fetches events for topic mode, explains the response structure (leg-by-leg prices, top_spreads_pp), and discloses edge cases with compatibility warnings and temporal alignment. This provides full transparency.

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

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

The description is well-organized with clear sections (two modes, response, safety fields) and front-loads the core concept. While thorough, it could be slightly more concise without losing substance, but every sentence contributes meaningfully.

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 3 parameters and no output schema, the description covers all necessary aspects: what the tool does, how to invoke it, what the response contains, and how to interpret safety fields and edge cases. 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?

The schema already describes each parameter well (100% coverage). The description adds value by explaining how the parameters interact (topic auto-fetches, explicit overrides) and providing context for their usage, going 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 computes cross-venue spreads between Kalshi and Polymarket for the same resolving question. It specifies the two modes (topic shortcuts and explicit tickers) and distinguishes itself from sibling tools like polymarket_arbitrage by highlighting the 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?

The description explicitly explains when to use each mode, provides examples, and warns that pre-mapped topics may not always yield tradable spreads. It also details the safety fields (compatibility_warning, temporal_alignment) that guide the agent on when the output is meaningful.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds scoping info (user identifier) and relationship to paired tools, providing valuable 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?

Two sentences, front-loaded with the core action, no wasted words. Efficient and clear.

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

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

Given annotations (readOnly, idempotent) and no output schema, the description fully covers retrieval, listing, scoping, and relationship to siblings. No further details needed.

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

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

Schema coverage is 100%, so baseline 3. Description adds meaning by explaining the key parameter's optionality and listing behavior, which the schema's description also covers but the description clarifies the dual behavior.

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 values saved via 'remember' or lists all keys, distinguishing it from sibling tools like 'remember' and 'forget'. It provides specific examples and scope.

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 gives use cases and context for when to use the tool, and mentions alternatives (remember, forget). It lacks explicit 'when not to use', 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=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral details: events carry specific fields, the mark_read parameter flags events as read to affect subsequent calls, and polling is suitable. This provides additional context beyond annotations without contradiction.

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

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

The description is concise with three sentences. The first sentence clearly states the purpose, the second details return fields and filtering, and the third covers mark_read behavior and polling. Every sentence adds value without redundancy or 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 the tool has 5 optional parameters and no output schema, the description adequately explains what is returned (source, citation_uri, raw payload), filtering, and the mark_read effect. It also mentions an alternative HTTP endpoint. Lacking some details on pagination or sorting, but overall sufficient 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%, so each parameter is already described. The description adds meaningful context: e.g., filter type example 'sec_8k', 'ISO timestamp' for since, and explains that mark_read:true makes next calls show only newer events. This enhances understanding 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 starts with 'Pull fired events from your subscription feed,' clearly stating the tool's purpose. It specifies that it returns the most recent alerts with fields like source, citation_uri, and raw event payload, and mentions filtering options. This distinguishes it from sibling tools like list_subscriptions or subscribe, which serve different functions.

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

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

The description notes that 'Polls work fine' and mentions an alternative HTTP endpoint, providing some usage context. However, it does not explicitly state when to use this tool versus sibling tools like list_subscriptions or when not to use it. The guidance is implied but not comprehensive.

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=false. Description adds behavioral details beyond annotations: fan-out to multiple sources, fallback mechanism (GDELT→GNews), soft-fail for USPTO, and return structure (changes grouped by source, total_changes count, citation URIs). 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?

Description is packed with information and front-loads the purpose. It is slightly long but every sentence adds value. Efficient for the complexity involved.

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 (multiple data sources, fallbacks, parameter nuances) and no output schema, the description covers the main points: what each source provides, how parameters work, and the return format. It could mention exact fields in the changes, but overall it is thorough.

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 meaningful context: 'since' accepts ISO date or relative shorthand with examples, 'type' is only 'company' today, 'value' can be ticker or CIK. This adds value beyond the schema.

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

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

Description clearly states the tool returns a change feed for a company in the last N days, fanning out to SEC EDGAR, GDELT/GNews, and USPTO. It distinguishes itself from sibling 'entity_profile' by explicitly contrasting use cases.

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

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

Description provides explicit guidance: 'Use entity_profile instead when you want the static profile...' and gives example queries like 'What's new with X' and common relative time windows.

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 (idempotentHint=true, destructiveHint=false), the description adds critical behavioral context: key-value scoping by identifier, persistent memory for authenticated users, 24-hour retention for anonymous sessions. No contradiction.

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

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

Three varied-length sentences, front-loaded with purpose, no redundancy. Every sentence adds necessary context without waste.

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

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

Given the simple tool (2 required params, no output schema), the description adequately covers purpose, usage, and persistence behavior. However, it lacks information about the return value (e.g., confirmation or stored value), which is a minor gap for completeness.

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

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

Schema coverage is 100% with clear parameter descriptions. The description adds minimal value beyond the schema (e.g., example formats like 'subject_property'), meeting 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 'Save data the agent will need to reuse later' with a specific verb (save) and resource (data). It distinguishes from siblings by explicitly mentioning recall and forget for retrieval and 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?

The description advises using the tool when discovering 'something worth carrying forward' to avoid re-lookup. It pairs with recall and forget, but lacks explicit when-not scenarios, e.g., for transient data.

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 read-only, idempotent, and non-destructive behavior. The description adds valuable context about internal cascading lookups and auto-disambiguation, going beyond annotations.

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

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

The description is concise, front-loaded with examples, and every sentence adds value. It is well-organized 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 fully covers return values for each entity type, including citations. It addresses potential ambiguity and provides complete context for agent usage.

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

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

Schema coverage is 100%, but the description enriches parameter meaning by detailing what each type returns (e.g., ticker, CIK, RxCUI) and acceptable input formats (ticker, CIK, name for company; brand/generic for drug).

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 identifiers, provides example queries, and distinguishes from siblings by focusing on ID resolution. It covers specific use cases and output for each entity type.

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

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

Explicitly says 'Use FIRST whenever you have a name but need an ID', providing strong when-to-use guidance. It also lists supported types and replaces multiple lookups, making the usage context 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 readOnly, openWorld, idempotent, non-destructive behavior. The description adds valuable behavioral detail: it probes each entity with ai_visibility_check, returns a ranked list with score, confidence, and signal density. 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 4 sentences, front-loaded with the main action. It is concise but could be slightly tighter (e.g., 'Probes each entity with ai_visibility_check and ranks' is a bit redundant with the first sentence). Still, it is efficient and well-structured.

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

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

Given no output schema, the description adequately describes the return format (ranked list with score, confidence, signal density). It also explains the probe mechanism and required entity array constraints (2-8 entities). Complete coverage for a tool with this complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra meaning by stating that the first entity is treated as the 'subject' for narrative, which goes beyond the schema's description. This justifies a 4.

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

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

The description states a specific verb+resource: 'Compare AI visibility across multiple entities side-by-side.' It explicitly distinguishes from sibling 'ai_visibility_check' by noting it probes each entity and ranks results, clearly differentiating the aggregate tool from its single-entity counterpart.

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

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

The description provides clear context: 'Useful for competitive AI-marketing audits' and an example question. While it doesn't explicitly state when not to use or name alternatives, the sibling tool 'ai_visibility_check' is implied as the single-entity alternative, and the use case is well-illustrated.

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, etc. Description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia may time out on first measurement (5-30s), sources_failed lists failures. No contradiction with annotations.

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

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

Description is concise and well-structured, starting with the core purpose and then detailing the components. Every sentence adds essential information without fluff. It's front-loaded and efficiently uses words.

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

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

No output schema, so description fully explains return values: summary block fields, per-advisory detail, links, alternative versions. Also mentions partial failures and graceful degradation. For a tool with two simple parameters, this is complete and actionable.

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 value by clarifying that 'package' accepts scoped names (e.g., '@types/node') and that 'version' defaults to latest when omitted, improving usability 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 the purpose as a composite check for adding an npm package, specifying exact resources checked (deps.dev, bundlephobia) and the data returned. It also distinguishes from siblings by noting NPM-only limitation and mentioning deps.dev:version 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 tells when to use this tool: when agent asks about safety/popularity/size/cost of npm packages. Also specifies when not to use: for other ecosystems, use deps.dev:version directly. Partial failure behavior is described, giving clear 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 indicate readOnlyHint=true and idempotentHint=true. The description adds significant detail: embedding model (BGE-base-en), chunking (500-char overlapping windows), similarity metric (cosine), and truncation behavior (200K char cap with flag). This surpasses basic annotation disclosure.

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 tightly written sentences. First sentence defines the core action, second provides usage context and return format, third covers technical details and pairing. No wasted words; front-loaded with 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, the description clearly states what is returned (top-N passages with character offsets and similarity scores) and covers limitations (cap, truncation flag). It integrates well with sibling tools, making it fully self-contained.

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 value by explaining the embedding process and providing illustrative query examples, enhancing understanding beyond the schema's 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: 'Semantic search INSIDE a fetched record.' It specifies the action (search within text), the resource (fetched record), and distinguishes from siblings like ask_pipeworx_grounded by highlighting the retrieval of passages with offsets.

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

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

Explicitly states when to use: 'Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter.' It also provides pairing guidance with ask_pipeworx_grounded and mentions a 200K char cap, offering clear context.

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

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

Annotations declare readOnlyHint=false and destructiveHint=false, so description adds behavioral context like requiring an account, delivery constraints, and webhook signing. 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 dense paragraph but well-organized: purpose, prerequisites, type enumeration, delivery options. While slightly long, every sentence adds value and it is front-loaded with the main action.

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

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

Given the tool's complexity (3 params, nested objects, no output schema), the description covers requirements, type examples, and delivery constraints. It doesn't describe the return format beyond 'subscription id', but that's acceptable.

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 detail: examples for each type (e.g., sec_8k items meaning, fred_series series_id) and delivery channel specifics (phone verification, 10/day cap, webhook secret once). This goes 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 'Create a proactive monitoring subscription', specifying the verb and resource. It distinguishes from siblings like list_subscriptions and unsubscribe by focusing on creation.

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

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

The description includes prerequisites (Pipeworx OAuth account) and supported types, giving clear context. However, it does not explicitly state when not to use this tool versus alternatives, though the context implies it.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context about the output format (category-bucketed, with tool+argument shapes) and that it is drawn from a live catalog, enhancing transparency 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 well-structured with synonyms, function explanation, and usage instructions. It is slightly long but every sentence adds value; could be marginally more concise for a quick scan.

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

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

Given no output schema, the description adequately explains the return type (categorized examples with tool calls). It covers parameter usage, output content, and positioning within the toolset, making it complete for its role.

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 meaning by explaining the effect of omitting versus providing topic, listing example focus areas, and specifying that omitting yields a full spread. This goes well beyond the schema description.

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

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

The description clearly states the tool's purpose as an onboarding entry point that returns categorized example questions with exact tool+argument shapes. It includes synonyms like 'What can I ask Pipeworx?' and distinguishes itself from siblings by being the introductory discovery tool.

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

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

The description explicitly instructs to 'Use this FIRST when you do not yet know what Pipeworx can do' and explains when to pass the topic parameter versus calling without arguments. This provides strong, clear guidance on when to use the tool.

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

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

Adds critical detail: row is deactivated not deleted, historical events stay via recent_alerts. Annotations only say destructiveHint=false, so description adds value.

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

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

Two sentences, front-loaded with action and resource, 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?

Tool is simple with one required param and no output schema. Description fully explains behavior, ownership, and impact on history.

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?

Single parameter 'id' with schema description 'Subscription id (uuid) returned by subscribe.' Description adds nothing beyond schema; schema coverage is 100%, so baseline 3.

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

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

Description states 'Cancel a subscription by id' with a specific verb (cancel) and resource (subscription). Differentiates from sibling 'subscribe' by direction.

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

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

States 'you can only cancel your own subscriptions' which guides usage and implies when not to use. Lacks explicit alternatives or when-not scenarios beyond ownership.

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, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context: it explains the auto-routing between structured and grounded pipelines, the return structure (verdict, actual value, citation, reasoning), and that it replaces multiple sequential calls. 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 fairly long but every sentence adds value. It front-loads typical trigger phrases, explains two pipelines, and outlines return values. It is well-structured and not redundant, though slightly verbose.

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

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

Given the tool has only 2 parameters, no output schema, and moderate complexity, the description is very complete. It explains the return values (verdict, actual value, citation, reasoning), the two processing paths, and the tool's efficiency benefit. No obvious 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%, so baseline is 3. The description adds meaning by explaining the tolerance_pct parameter: its default is implied by claim wording, and it provides examples for override (1-2 for hallucination detection). This supplements 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 validates natural-language claims against authoritative sources, using trigger phrases like 'fact check' and 'verify'. It distinguishes between company-financial claims (via SEC EDGAR) and other claims (via grounded pipeline), making the purpose very specific and distinct 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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the two claim categories, providing clear context. However, it does not explicitly mention when not to use it or name alternative sibling tools like 'ask_pipeworx_grounded' for comparison.

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