Data Baltimore

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

Description adds behavioral context beyond annotations (readOnlyHint, idempotentHint): it explains that probing costs (Anthropic requires user's key), returns per-model details, and provides a combined view. 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?

Two efficient sentences: first defines core function and output, second adds defaults, optional parameters, and use cases. 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 details the return structure (per-model score, confidence, signals, raw_response + combined view). Combined with parameter details and use cases, it's fully sufficient for an agent to select and invoke correctly.

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

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

Schema coverage is 100%, but the description adds value by explaining default model usage, the need for _apiKey with Anthropic, and the role of 'context' for disambiguation. This goes beyond the parameter descriptions alone.

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

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

The description clearly identifies the action ('probe'), the resource ('one or more LLMs'), and the output ('score visibility 0-100 per model'). It distinguishes itself from siblings like 'scan_competitor_ai_presence' by focusing on general visibility scoring across multiple models, making it a specific and differentiated 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 provides explicit context: default model (free Workers AI), optional Anthropic probing with BYO key, and use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). While it doesn't explicitly state when not to use or list alternatives, the context is clear enough for 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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that it returns structured answers with stable citation URIs and works on every tier with one fast call, providing useful behavioral context beyond 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?

Description is dense but well-structured with an imperative first sentence, clear guidance, and examples. Could be slightly more concise, but every sentence adds value.

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

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

Given the complexity (single required parameter, no output schema), the description fully covers the tool's purpose, usage, and output characteristics. It is complete and leaves no ambiguity about when and how to use it.

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

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

Schema coverage is 100% with all parameters as aliases for 'question'. Description adds that the input is a natural language request, complementing the schema. Baseline score of 3 is appropriate as schema already documents parameters.

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

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

The description clearly states the tool answers factual questions about current/historical data from structured sources, routes to many tools, and distinguishes from siblings like ask_pipeworx_grounded and deep_research.

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

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

Provides explicit guidance: 'PREFER OVER WEB SEARCH', lists when to use (factual questions) and when to step up to alternatives (grounded, deep research). Includes example queries and contextual signals.

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 safety hints (readOnly, idempotent). The description adds substantial behavioral context: return structure with answer/evidence/confidence/refusal reasons, explicit refusal categories, and the extra LLM call cost. This goes well 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-organized and front-loaded with the key purpose, but it is somewhat lengthy. Each sentence earns its place, but conciseness could be slightly improved without losing detail.

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

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

Despite having no output schema, the description fully specifies the return format and failure modes. It covers all necessary behavioral and usage context for a high-stakes tool, compensating for the missing schema.

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

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

Schema coverage is 100%, so baseline is 3. The description only restates that the parameter is a natural language question, without adding new semantics beyond the schema's aliases. No additional 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?

The description clearly states the tool is a hallucination-resistant answer mode for high-stakes reads, specifying it routes to the right tool and extracts answers only from tool results. It distinguishes itself from sibling 'ask_pipeworx' by noting the extra LLM call cost and appropriate 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?

Explicit guidance on when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' Also advises preferring 'ask_pipeworx' for casual lookups, providing clear alternatives.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds context about return value usage, but no additional behavioral traits 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, no filler, front-loaded with purpose. 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 is a simple list operation with clear annotations and no output schema, the description fully explains its purpose, usage, and output coupling with baltimore_query.

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 meaning by explaining the service parameter's allowed values (short names or full path) and the behavior when omitted.

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 layers of a Baltimore ArcGIS service (for discovery)', specifying verb, resource, and context. Distinguishes from siblings like baltimore_query.

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

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

Explicit guidance on how to use the service parameter (short name or full path, omit to list known services) and what the output is used for (layer id + name for baltimore_query).

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

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

Description aligns with annotations (readOnly, idempotent, non-destructive) and adds value by noting epoch dates are converted to ISO. No contradictions.

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

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

Three concise sentences, each essential: purpose, sibling guidance, date conversion note. 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?

Covers purpose, parameters, and sibling context. Lacks description of return format, but that is standard ArcGIS response; otherwise well-informed for agent use.

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

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

Schema coverage is 100%, but description adds meaning with examples (service short names, default limit 100 max 2000, where default '1=1') and clarifies date conversion.

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 the tool queries Baltimore ArcGIS layers by service path and layer id, lists parameters (where, out_fields, order_by, limit), and differentiates from siblings (baltimore_layers, baltimore_recent).

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

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

Provides explicit guidance on when to use this tool vs alternatives (baltimore_layers for discovery, baltimore_recent for common layers) and gives examples for service parameter (short names, full path).

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, idempotent behavior. Description adds specifics: returns newest-first, converts epoch dates to ISO, and supports optional ArcGIS where filter, enhancing transparency 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?

Three concise sentences, front-loaded with purpose, followed by usage preference and technical details. 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?

Adequately covers return format, filtering, dataset names, and cross-references sibling tools. Lacks explicit mention of data source (ArcGIS) but inferred. Sufficient 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 covers all parameters with descriptions (100% coverage). Description adds context on return format and where filter usage, slightly elevating beyond 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 returns recent records from a Baltimore open dataset by friendly name, specifically 'crime' and '311', and distinguishes from sibling tools like baltimore_layers and baltimore_query.

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 preferring this tool over web search for specific queries, and directs to use baltimore_layers and baltimore_query for other layers, providing clear when-to-use and alternative 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?

The description extensively discloses behavior beyond annotations: fan-out logic, classifiers, response shapes for all fields, resolver contract with confidence levels, parent event extraction, news backfill details, safety short-circuits for low-confidence and closed markets, wide-spread handling, and resolution-rule risk parsing. No contradiction with annotations (readOnlyHint, idempotentHint).

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 dense, but structured with labeled sections (e.g., RESPONSE SHAPES, RESOLVER CONTRACT). Not concise, but organized for reference. Could be more front-loaded with critical behavioral notes, but the detail is valuable.

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

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

Without an output schema, the description compensates by detailing all response fields and edge cases: resolver contract with alternatives and suggestions, parent event extraction, news backfill flags, safety mechanisms, and cancellation rule parsing. Covers all anticipated agent needs for a complex research tool.

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

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

Schema covers all 3 parameters with descriptions, baseline 3. The description adds significant behavioral semantics: explains how market input is resolved (slug/URL/question), depth affects source count, include_raw controls response size. However, it doesn't document parameter structure beyond schema.

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

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

The description clearly states the tool's purpose: research a Polymarket bet by pulling Pipeworx data. It specifies input types (slug, URL, question text) and outputs (evidence packet + market-vs-model comparison). It distinguishes from siblings like polymarket_arbitrage and polymarket_edges by focusing on data gathering rather than edge calculation.

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

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

The description explicitly lists use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It provides context for when to use, but lacks explicit when-not-to-use or alternative sibling references, though siblings are available.

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

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds value by detailing fiscal year handling, sorting, and that it replaces 8-15 sequential lookups. 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 trigger phrases and packed with information. Every sentence adds value, though slightly verbose. Efficiently 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, description explains return format (paired data + citation URIs), sorting, and entity-type-specific details. Covers all necessary context for accurate 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%, but description adds significant meaning: for 'company' it specifies specific financial data pulled from SEC EDGAR/XBRL; for 'drug' it specifies adverse-event counts, FDA approvals, trial counts. Provides examples and constraints.

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

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

The description clearly states that the tool performs side-by-side comparison of 2-5 companies or drugs, with explicit trigger phrases like 'Compare X and Y' and 'X vs Y'. It distinguishes from siblings by recommending over sequential single-pack lookups.

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

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

Provides explicit preference over sequential lookups and examples of when to use ('which is bigger', 'rank these companies'). Lacks explicit when-not-to-use guidance, but strong positive guidance earns a 4.

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: account needed, paid tier for thorough, parallel decomposition, output format (findings packet with gaps), expected time (15-60s), and note about empty gaps for non-catalog topics. 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?

Front-loaded with crucial info (account, alternative tool). Though verbose, every sentence serves a purpose. Could be slightly more concise but remains 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?

The description fully explains tool behavior, output (findings packet with evidence, confidence, source, citation, gaps), constraints (paid plan, time, data sources), and relationships to siblings. No output schema exists, so description compensates well.

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

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

Both parameters are described in schema (100% coverage). The description adds value by explaining depth enum values (quick=3, standard=5, thorough=8) and clarifying question as natural language, going slightly 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 multi-source structured research in one call, distinguishing it from siblings like ask_pipeworx (single lookup) and noting it is not open-web search. The verb 'research' and resource 'Pipeworx's structured data sources' are specific and unique.

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

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

Explicitly specifies when to use (broad/multi-part questions over structured data) and when not (single lookup → ask_pipeworx; breaking news → ask_pipeworx). Also mentions account requirement and paid plan 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 declare readOnlyHint and idempotentHint true, so the description adds context beyond that: it explains that results include full schemas with curated examples and that results are ready to call directly. There is no contradiction with annotations.

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

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

The description is slightly lengthy but well-structured, with clear front-loading of purpose and usage. Every sentence adds value; the style is efficient without being overly 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?

Despite no output schema, the description explains exactly what is returned (top-N tools with names, descriptions, and schemas). It covers when to call, what to expect, and how to use the results, making it fully complete for an agent.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description mentions that query accepts aliases, but that is also in the schema. It adds no new parameter details beyond what is already in the structured 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 uses a specific verb 'Find tools by describing...' and clearly states it is for browsing and discovering tools. It lists diverse domains and distinguishes itself from sibling tools by being a meta-search tool, not a domain-specific one.

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

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

The description explicitly says 'Use when you need to browse, search, look up, or discover what tools exist for...' and advises to 'Call this FIRST when you have many tools available and want to see the option set.' While it provides clear context, it lacks explicit guidance on when not to use it.

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

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds valuable behavioral context: data sources fanned out, patents API sunset (soft-fail), news fallback, and output structure. Does not cover potential rate limits or error handling, but adds significant 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?

The description is dense but not overly long, front-loaded with example queries. It covers all key points in a single paragraph. Some structure (e.g., bullet points) could improve readability, but it remains concise and informative.

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

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

Given the tool's complexity (multiple sources, fallbacks, API sunset) and no output schema, the description provides sufficient context: lists outputs, mentions patent API sunset, news fallback. It could detail failure modes or pagination, but overall complete for an agent to understand expected behavior.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds extra meaning: clarifies that 'type' only supports 'company', and 'value' requires ticker or CIK (not names). This goes beyond the schema's enum and description for '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?

The description clearly states the tool's purpose: 'full cross-source profile of a US public company'. It provides example queries and lists specific outputs (CIK, filings, fundamentals, etc.), distinguishing it from siblings by explicitly preferring it over chaining single-source lookups for holistic views.

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

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

Explicitly states when to use ('when the user asks for a holistic view') and when not to ('names not supported – use resolve_entity first'). Also specifies acceptable parameter inputs (ticker or zero-padded CIK), providing clear guidance for correct invocation.

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 readOnlyHint=false. The description adds context on why to delete (stale context, sensitive data) but does not contradict annotations.

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

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

Three concise sentences with no redundant information. Every sentence adds value: action, usage guidance, and related tools.

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

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

For a simple tool with one parameter, the description covers purpose, usage, and pairing. Annotations handle destructive behavior. No missing 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% with a clear parameter description. The tool description does not add additional semantics beyond the schema, justifying a baseline score 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 the action ('Delete') and the resource ('previously stored memory by key'). It distinguishes itself from siblings by specifying deletion as opposed to remembering or recalling.

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

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

Explicitly provides when to use the tool ('context is stale, the task is done, or you want to clear sensitive data') and suggests pairing with 'remember' and 'recall'.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. Description adds behavioral detail: fetches page, extracts title/description/key links, emits standard markdown format, and outputs a single text blob. 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 two sentences plus a list of use cases, no wasted words, front-loaded with the main action. 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 explicitly states output format (single text blob ready for site-root/llms.txt). Covers process and use cases completely for a simple two-parameter tool.

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

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

Schema covers both parameters with descriptions (100% coverage). Description reiterates 'URL' and implies 'max_links' through mention of link limits, but does not add significant new 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?

Description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb, resource, and scope. It differentiates from siblings by highlighting specific use cases like client indexing, personal projects, or competitor auditing.

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 clear use cases (indexing, drafting, auditing), but does not explicitly exclude alternative tools or state when not to use. Sibling tool 'scan_competitor_ai_presence' may be related but not referenced.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds behavioral detail: returns specific fields, and implies only active subscriptions by default (with optional include_inactive). 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, no wasted words. Purpose, return fields, and usage guidance are front-loaded and clearly presented.

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 fields sufficiently. Single optional parameter is well-documented. Context is complete for the tool's simplicity.

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

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

Schema coverage is 100%. Description mentions include_inactive with meaning already stated in the schema ('Include cancelled subscriptions'). No additional semantic value added 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?

Clearly states it lists the caller's active subscriptions, specifies returned fields (id, type, params, etc.), and gives use cases (review monitoring, find ID to cancel). This distinguishes it from sibling tools like subscribe and unsubscribe.

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

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

Provides explicit when-to-use context: 'review what you're monitoring before adding more or to find an id to cancel.' Does not mention when not to use or alternatives, but context is clear for a simple read 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?

Annotations are all false, so the description carries full burden. It discloses rate limits (5 per identifier per day), that it's free, and that feedback affects the roadmap. It does not contradict annotations.

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

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

The description is concise, front-loaded with purpose, and each sentence adds value: use cases, writing guidelines, rate limit, cost. No wasted words.

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

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

For a simple feedback tool with no output schema, the description covers all necessary aspects: purpose, usage, constraints, and rate limits. It is complete for an agent to decide when and how to invoke it.

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

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

Schema coverage is 100%, baseline 3. The description adds value by specifying how to write feedback (describe in terms of tools/packs) and not to paste prompts, which provides guidance 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: sending feedback about bugs, features/data gaps, or praise. It differentiates from sibling tools like ask_pipeworx by specifying that feedback is directed at the Pipeworx team rather than asking questions.

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 scenarios (bug, feature/data_gap, praise) and advises against pasting end-user prompts. While it implies alternatives (e.g., ask_pipeworx for queries), it does not explicitly list when not to use this tool.

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

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

Annotations already declare read-only, open-world, idempotent, non-destructive. The description adds that it's self-aggregating from CF analytics-engine, no PII, and cached 5min-1h. This provides useful context beyond annotations. No contradiction.

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

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

The description is three sentences with clear structure: function, use cases, technical details. No redundant phrases; each sentence adds value.

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

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

Given low complexity (1 optional param, no output schema, rich annotations), the description covers purpose, usage, behavior, parameters, and caching. It is sufficiently complete for an agent to select and invoke 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% with the 'window' parameter described. The description adds interpretive guidance on window selection (hot vs. steady-state), improving semantics beyond the schema's enum and 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 returns top tools, top packs, and total call volume over a recent window, using specific verbs and resource. It distinguishes from sibling tools like ask_pipeworx or discover_tools by focusing on aggregated trending data from other AI agents.

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

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

Three explicit use cases are provided (discovering hot data, confirming canonical tools, aligning use case). Window guidance for hot vs. steady-state is given. However, no explicit when-not-to-use or alternatives among siblings are mentioned.

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

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

Annotations indicate read-only, open-world, idempotent, non-destructive. Description adds significant behavioral context: partition checks, Jaccard similarity threshold (0.30), placeholder filter (>20% returns null), fill check against CLOB depth, and references polymarket_fill_risk for custom sizing. No contradictions.

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

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

Description is thorough but slightly lengthy. However, it is well-structured with clear sections and front-loaded with the requirement. Every sentence earns its place, but could be more concise without losing essential details.

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 details the response structure (opportunities[], partition_check, fill_check). It covers edge cases, similarity thresholds, placeholder filtering, and references related tools. Complete for the complexity.

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

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

Schema coverage is 100% with descriptions for both parameters. Description enriches meaning by explaining the difference between event and topic modes, accepting URLs for event, and how the tool uses the topic parameter for related event search. Adds value beyond schema.

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

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

The description clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes two modes (event and topic) and specifies the resource (Polymarket markets). Very specific verb and resource, effectively differentiating from 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 that one of `event` or `topic` is required, with call with no args failing. Provides guidance on when to use each mode: event recommended for specific markets, topic for cross-event scanning, and explains that cross-event catches patterns missed by single-event. No ambiguity.

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

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

Annotations already show read-only, idempotent, and nondestructive traits. The description adds extensive behavioral details: three model families with explicit methodologies, response structure, diagnostics including filter_skips, caching behavior (1h KV), and tradeable-edge knobs. Completely transparent with 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 excessively long with dense technical details jammed into a single paragraph. While well-organized with parenthetical segments, it would benefit from shorter, front-loaded sentences to improve agent parsing. Not concise enough 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 9 parameters, no output schema, and high complexity, the description covers all necessary aspects: output structure (by_segment, diagnostics, fed_note), edge metrics, model methodologies, filters, and caching. It fully enables an agent to understand, invoke, and interpret results without external documentation.

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. Beyond the schema, the description explains how parameters like min_liquidity and max_spread_pp act as 'Tradeable-edge filters' and clarifies the role of min_partition_leg_kelly in the partition_overround context, adding operational meaning.

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

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

The description clearly states the tool scans Polymarket markets for discrepancies with Pipeworx data, explicitly targeting 'what should I bet on today.' It distinguishes itself from siblings by focusing on edge discovery through multiple model families, not just arbitrage or tracking.

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

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

The description implies usage for daily betting decisions ('what should I bet on today') and mentions caching and diagnostics, but does not explicitly contrast with sibling tools like polymarket_arbitrage or polymarket_edge_tracker. More explicit when-to-use guidance would improve clarity.

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 response structure (tracked, expired, snapshot_dates), explains decay calculation source (daily closes, not intraday), and mentions history limits (60-day TTL, snapshot enablement).

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

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

The description is well-structured with a summary, a motivating question, then detailed response format. It front-loads key points, though it is fairly long for the amount of 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?

Given the lack of an output schema, the description thoroughly explains the response arrays (tracked, expired, snapshot_dates) and their fields, plus limitations. It covers the necessary information for an agent to understand what the tool returns.

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

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

Schema coverage is 100% with descriptions for both parameters. The tool description adds default values and ranges, but does not substantially expand beyond schema information.

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

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

The description clearly states it provides edge persistence and decay telemetry from daily snapshots, answering a specific question about edge age and shrinkage. It distinguishes itself from sibling tools like polymarket_edges by focusing on temporal tracking.

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

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

The description gives context on when to use the tool (to understand edge persistence and its implications for trading), but does not explicitly exclude use cases or mention alternatives like polymarket_edges for raw 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?

Disclosures beyond annotations: describes ladder walking, returned fields (top_of_book, vwap_fill_price, slippage_pp, etc.), basket mode's theoretical vs realizable sum, and risks of partial fills. 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?

Well-structured with capitalized mode sections and bulleted return fields. Slightly long but justified by complexity. 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?

Complete for a complex tool with dual modes and many return fields, no output schema. Lists all returned fields and explains basket logic thoroughly.

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 meaning: explains size_usd interpretation per mode, auto side logic in basket, and default values. Adds significant value beyond schema.

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

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

The description starts with a specific verb+resource ('Realizable-vs-theoretical edge check against live CLOB order-book depth'), clearly distinguishes two modes (single-market vs basket), and contrasts with 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?

Explicitly states when to use ('BEFORE acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500') and explains why partial fills cause unhedged directional risk, providing clear context for alternatives.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds substantial behavioral context beyond that: it explains auto-fetching logic, compatibility warnings, temporal alignment checks, and how skipped cross types work. 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 lengthy but well-structured with clear section headers (TWO MODES, RESPONSE, SAFETY FIELDS). Every sentence provides meaningful information, though some redundancy could be trimmed. It is front-loaded with the core purpose and modes.

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 comprehensively explains the response structure (leg-by-leg prices, spread array, safety fields). It covers parameter usage, edge cases (temporal alignment, compatibility), and the significance of skipped types. This is exceptionally complete for a complex tool.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds value by explaining the two modes (topic vs explicit), listing valid topic values, and clarifying that explicit parameters override topic-mapped sides. This goes beyond the schema's basic 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 provides cross-venue spread between Kalshi and Polymarket for the same resolving question. It differentiates itself from siblings (no other spread tools) and details two modes (topic shortcuts vs explicit pairing), with response structure. The verb 'spread' and resource 'cross-venue' are specific.

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 topic mode vs explicit mode and warns that pre-mapped topics often yield compatibility warnings. It provides context for when spreads are meaningful (temporal alignment, matching bet shapes), but does not explicitly state when to avoid the tool entirely or list alternatives beyond the sibling set.

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 and idempotentHint, so the tool's safety is known. The description adds scoping details and the listing behavior when key is omitted. Does not contradict annotations.

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

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

Three sentences, front-loaded with main purpose, no fluff. Efficiently conveys action, usage, scope, and pairing with siblings.

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 essential aspects: what, how, when, scope, and pairings. No output schema, but the description sufficiently explains the retrieval/list behavior. For a simple tool with one optional parameter and strong annotations, 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%, and description mostly echoes the schema's description of the 'key' parameter. It adds only minor clarification that omitting key lists all keys, which is already implied by schema's required:false. 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 two actions: retrieving a specific value by key or listing all keys. It uses a specific verb ('Retrieve'/'list') and resource ('value saved via remember'/'saved keys'), and distinguishes from sibling tools like 'remember' (save) and 'forget' (delete).

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

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

Provides explicit when-to-use guidance ('look up context the agent stored earlier... without re-deriving it from scratch'), mentions scoping ('Scoped to your identifier'), and directly names alternatives ('Pair with remember to save, forget to delete').

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 as safe read operations. Description adds details about return fields, mark_read side-effect, and alternative access method, enhancing transparency 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 four sentences long, front-loaded with the purpose, then return details, then filtering/flag behavior, then polling note. Every sentence is informative with no redundancy or filler.

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

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

With full schema coverage and informative annotations, the description completes the picture by explaining return fields and side-effects. No output schema exists, but the description covers what is returned adequately for a read-only retrieval 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?

All 5 parameters are documented in the schema (100% coverage). The description supplements with examples like type 'sec_8k', ISO timestamp format for since, and behavior of mark_read, adding practical value beyond the schema.

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

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

The description clearly states the tool pulls fired events from the subscription feed, using specific verbs and showing what is returned (source, citation_uri, raw payload). It is distinct from sibling tools like list_subscriptions or subscribe.

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

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

Provides explicit guidance on filtering by type and since, setting mark_read, and notes that polling works fine. Also mentions an alternative URL for scripts/dashboards. Lacks explicit when-not-to-use statements but gives sufficient 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, idempotent, read-only operation. Description adds detailed source fan-out (SEC, GDELT/GNews, USPTO), fallback logic, USPTO sunset soft-fail, and return structure (grouped changes, total_changes, citation URIs). No contradictions.

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

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

Description is information-dense but slightly long. However, every sentence adds value and structure is logical: examples, definition, source details, parameter hints, sibling pointer.

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 format, sources, and fallback behaviors. Lacks pagination or result limits, but the 'one parallel call' phrasing implies single response. Comprehensive enough for AI to invoke 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 already covers 100% of parameters with descriptions. Description additionally clarifies since format (ISO or relative shorthand with examples) and value as ticker or CIK, adding practical usage context beyond schema.

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

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

Description begins with concrete example queries and defines tool as a change feed for companies over time windows. It explicitly distinguishes from sibling tool entity_profile, making purpose very clear.

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

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

Provides explicit when-to-use guidance via example queries and states to use entity_profile for static profiles. Offers parameter best practices (e.g., '30d' or '1m') and explains fallback behavior for data sources.

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, readOnlyHint, destructiveHint), the description adds key behavioral details: key-value storage scoped by identifier, persistent memory for authenticated users, 24-hour retention for anonymous sessions. No contradictions.

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

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

The description is three sentences, front-loaded with the main purpose, and every sentence provides essential information without redundancy. It is concise 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 explains how to retrieve (recall) and delete (forget) data, covering the full lifecycle. It addresses different user scopes and retention, making it complete for a memory 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 description doesn't need to elaborate on parameters. However, it adds value by providing example keys and values (e.g., "subject_property", "target_ticker"), which helps the agent understand usage 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 saves data for reuse across conversations and sessions, with specific examples of what to store (resolved ticker, target address, etc.). It differentiates from sibling tools by 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 specifies when to use the tool (when discovering something worth carrying forward) and pairs it with recall/forget. It does not explicitly state when not to use it, but the guidance is clear enough for most 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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that each call cascades through multiple internal endpoints, replaces 2-3 manual lookups, and includes auto-disambiguation and citation URI generation, giving 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?

The description is front-loaded with key usage guidance and structured with clear examples for each entity type. It is slightly lengthy but every sentence adds value; could be trimmed slightly 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?

For a simple two-parameter tool with no output schema, the description thoroughly explains return values for each type, auto-disambiguation, and internal cascading behavior. It delivers sufficient context for an LLM to use the tool effectively.

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

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

Schema coverage is 100% so baseline 3. The description adds value by explaining each type's return fields (e.g., ticker+CIK+company_name+citation URI for company) and input formats (ticker, CIK, name). This provides richer semantics than the schema alone.

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

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

The description clearly states the tool resolves user-spoken names to canonical identifiers, with examples like ticker, CIK, RxCUI. It distinguishes from siblings by specifying it should be used first when needing an ID, and details two supported entity types with distinct outputs.

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

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

The description explicitly says 'Use FIRST whenever you have a name but need an ID' and provides query examples. It does not mention when not to use it or list alternatives like other lookup tools, but the sibling context and clear purpose make usage appropriate.

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, idempotent, read-only. Description adds that it internally probes with ai_visibility_check and returns ranked list with score, confidence, signal density. No contradictions.

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

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

Three sentences, front-loaded with purpose, efficient and no extraneous 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, description adequately explains output shape (ranked list with score, confidence, signal density). Covers input, process, and result.

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

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

All 4 parameters have schema descriptions (100% coverage). Description adds value: first entity treated as subject for narrative, explains context usage, and clarifies models parameter 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 compares AI visibility across multiple entities side-by-side, distinguishes from sibling tools like ai_visibility_check (single entity).

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

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

Explicitly describes competitive AI-marketing audit usage, but does not explicitly state when not to use or list alternatives; sibling context implies single-entity or general comparison use cases.

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 external service fan-out, timing specifics (5-30s for bundlephobia), graceful degradation, and 'sources_failed' field. Adds significant context beyond annotations.

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

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

Description is dense but well-organized: begins with summary, lists return fields, specifies scope and edge cases. Could be streamlined but remains efficient.

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

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

Despite no output schema, description enumerates all key return fields (summary block, advisories, links, alternatives) and covers behavior (defaults, partial failures). Fully adequate for a 2-param 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 adds value: explains scoped packages are accepted, version defaults to latest, and implies usage pattern. Slightly above 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 it's a composite check for npm packages, listing specific data sources (deps.dev, bundlephobia) and return fields. Unambiguous verb and resource, distinct from 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 tells when to use: 'is X safe / popular / small' or 'what does adding lodash cost me'. Also notes NPM-only v1 and explains partial failure handling.

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

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

Discloses truncation at 200K chars, embedding model details (BGE-base-en, cosine, 500-char windows), and that results include character offsets. These go beyond the readonly/idempotent 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?

Concise yet comprehensive. Front-loaded with core function, every sentence adds value, 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 rich annotations and full schema coverage, the description covers use case, pairing, technical details, truncation, and return format (though no output schema, the description explains it sufficiently).

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

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

Schema description coverage is 100%, but the description adds examples and clarifies that 'text' has a 200K char limit and 'query' is natural-language. Adds value beyond schema.

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

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

Clearly states it performs semantic search inside a fetched record, using clear verbs and resource specification. It distinguishes from sibling 'ask_pipeworx_grounded' by explaining the pairing.

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 ('when the record is too big to cram into the prompt') and mentions an alternative tool ('Pairs with ask_pipeworx_grounded').

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

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

The description adds significant behavioral context beyond annotations: account requirement, delivery channel behavior, rate limits (10/day SMS), and webhook signing. However, the annotation idempotentHint=true contradicts the description's implication that each call creates a new subscription (non-idempotent). This is flagged as a contradiction.

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

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

The description is well-structured with a clear upfront purpose, followed by requirements, types, and delivery details. While comprehensive, the webhook HMAC detail could be slightly trimmed for conciseness, but it remains 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?

The description covers all aspects: purpose, requirements, type-specific parameters, delivery channels with limitations, and return value. For a complex tool with no output schema, it provides sufficient context for correct usage.

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

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

Schema coverage is 100%, and the description enriches every parameter with type-specific examples, required fields, and validation rules (e.g., E.164 phone, HTTPS webhook). It adds value beyond the schema.

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

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

The description clearly states it creates a proactive monitoring subscription and returns a subscription id. The verb 'subscribe' and resource 'alerts' are explicit. 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 specifies when to use (live-data event streams), requirements (Pipeworx OAuth account), and delivery options. It lacks explicit when-not-to-use statements but context from siblings implies alternatives. The prerequisites and caps are well-covered.

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. Description adds that the tool returns example questions with exact tool+argument shapes and is drawn from a live catalog. No contradictions. Adds context beyond annotations.

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

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

Description is slightly long but well-structured with examples and purpose clearly front-loaded. Every sentence adds value; no wasted content. Could be slightly tighter but 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?

For a tool with 1 optional parameter, no output schema, and good annotations, the description fully covers purpose, usage, and return type. All relevant information is present.

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

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

Schema has 100% coverage with description for topic. Description adds usage context: 'pass topic to focus' and lists example values. Adds meaning beyond schema alone.

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

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

Description clearly states it is the onboarding entry point that returns category-bucketed example questions. Uses specific verbs ('returns', 'call') and resource ('example questions'). Distinguishes 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?

Explicitly states 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. Also explains optional topic argument to focus on specific areas. Provides clear guidance on when and how 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 non-destructive mutation. The description adds valuable context: 'The row is deactivated (not deleted) so its historical events stay available via recent_alerts.' This clarifies the nature of the 'non-destructive' behavior and ownership enforcement, enhancing 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?

Two concise sentences with no redundancy. Key information is front-loaded: action, ownership, and preservation behavior. 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?

For a simple tool with one parameter and no output schema, the description covers purpose, ownership, and side effects. It lacks explicit mention of return value but references behavioral outcomes sufficiently for agent decision-making.

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

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

Schema coverage is 100% with 'id' described as 'Subscription id (uuid) returned by subscribe.' The description adds minimal value beyond the schema, merely stating 'by id.' Baseline of 3 is appropriate as schema handles parameter documentation adequately.

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

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

The description clearly states 'Cancel a subscription by id,' using a specific verb and resource. It distinguishes from sibling tools like 'subscribe' (create) and 'list_subscriptions' (view) by specifying the cancel action and unique behaviors like ownership enforcement.

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 implicit guidance: it mentions ownership enforcement (only own subscriptions) and that deactivation preserves history via 'recent_alerts.' However, it does not explicitly state when not to use this tool or recommend alternatives beyond referencing 'recent_alerts' for history.

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, etc.), the description adds significant behavioral context: the data source (SEC EDGAR + XBRL), the return format (verdict types, extracted structure, citation, percent delta), and efficiency gains. 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 multi-sentence but front-loaded with key phrases and use cases. Each sentence adds value, though slightly verbose. It is well-structured and easy to parse.

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

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

Given the single parameter, rich annotations, and no output schema, the description fully covers the tool's operation: purpose, usage context, domain limitation, return value structure, and efficiency benefit. 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 description coverage is 100% with a clear description of the 'claim' parameter and examples. The description provides similar examples but does not add new semantics beyond what the schema already offers. 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: natural-language claim verification against authoritative sources, with specific verbs like 'fact check', 'verify the claim'. It specifies the resource (company-financial claims via SEC EDGAR + XBRL) and distinguishes itself from sibling tools by being a specialized replacement for 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' and provides example triggers. It also delimits the domain ('v1 supports company-financial claims') which serves as a when-not-to-use guide. No explicit alternatives are mentioned, but the domain restriction is sufficient.

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