Coinbase Exchange

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds important behavioral details: returns per-model {score, confidence, signals, raw_response} + combined view, default model is free, Anthropic requires BYO key. No contradictions.

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

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

The description is concise (three sentences) and front-loaded with the core purpose. Every sentence adds value, 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?

Despite no output schema, the description fully explains the return structure (per-model scores, confidence, signals, raw response, combined view). The parameter semantics and usage guidelines are complete for an AI-marketing audit 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% and the description adds context beyond the schema: it explains the default model, that models array is optional, and how _apiKey is used. However, it doesn't add much nuance to the parameters themselves beyond what's in the schema.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and scores visibility (0-100). It distinguishes itself from siblings by focusing on AI-brand visibility audits, which is unique among the listed sibling tools.

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

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

The description provides clear use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and mentions default model and optional API key. It lacks explicit when-not-to-use or alternatives, but the context 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 the tool is read-only, open-world, and idempotent. The description adds that it routes questions, fills arguments, and returns stable pipeworx:// citation URIs, providing additional behavioral context without contradicting annotations.

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

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

The description is detailed but well-structured, starting with a strong directive, then listing sources, usage criteria, and examples. While slightly verbose, every sentence adds value and is front-loaded with the most critical guidance.

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 and no output schema, the description adequately explains the tool's behavior (routing, citation URIs, structured answers) and covers a wide range of use cases. It stands alone without needing additional context from annotations or 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?

With 100% schema coverage (single 'question' parameter described as 'Your question or request in natural language'), the description adds no further detail about the parameter's format or constraints. The schema description is sufficient, so a baseline score of 3 is appropriate.

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

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

The description explicitly states the tool routes factual questions to 2,793 tools across 605 sources, returning structured answers with citations. It distinguishes itself from web search with a clear 'PREFER OVER WEB SEARCH' directive and provides numerous examples, making the purpose unmistakable.

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: for factual questions about real-world entities, events, or numbers, especially those requiring authoritative structured data. It lists example query types ('what is', 'look up', 'find', etc.) and concrete examples, guiding the agent effectively.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context: it resolves markets, classifies bets, fans out to packs, and returns summarized evidence. It also discloses response size implications of the include_raw parameter. 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 of about 6 sentences. It front-loads purpose and usage, but could be more structured (e.g., bullet points) for easier scanning. It earns its length by packing essential 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?

Given the complexity of three parameters, no output schema, and related sibling tools, the description covers input formats, behavioral details, use cases, and performance characteristics. It explains the return as 'evidence packet plus market-vs-model comparison', which suffices. Minor gap: no explicit mention of output structure beyond that comparison.

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 3. The description adds value by explaining parameter semantics: market input formats (slug, URL, question text), depth values (quick vs thorough), and the reasoning behind include_raw (response size management). This goes 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 verb 'Research' and the resource 'a Polymarket bet', and details the internal process (resolving market, classifying, fanning out packs, returning evidence packet and comparison). It distinguishes its role as a core demo product that integrates data, implicitly differentiating from sibling tools like polymarket_arbitrage.

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

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

The description explicitly lists use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. It does not mention alternative tools or when to avoid, but provides clear context for when to invoke. A perfect score would require explicit non-use guidance.

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

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

Description adds substantial context beyond annotations: specifies data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), notes it replaces 8–15 sequential calls, and mentions return of paired data with citation URIs. 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?

Single dense paragraph with front-loaded purpose. Every sentence adds necessary detail without redundancy.

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

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

Given no output schema, it describes the return format (paired data + URIs) and covers the tool's complexity (multiple data sources, replaces many sequential calls). Fully complete for agent selection.

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?

Though schema covers 100%, the description explains the meaning of 'values' per type (tickers/CIKs vs drug names) and lists the specific data fields returned (revenue, net income, etc.). This adds significant value.

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

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

The description clearly states the tool compares 2–5 companies or drugs side by side, specifying the exact data sources for each type. It distinguishes itself from sibling tools by focusing on multi-entity comparison.

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

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

It explicitly lists user utterances that trigger use ('compare X and Y', 'X vs Y', etc.) and provides examples. Lacks explicit when-not-to-use or alternative tools, but the context is clear.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, which cover safety and idempotency. The description adds no additional behavioral details (e.g., return structure, rate limits), but the annotations carry most of the burden.

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 words) with no extraneous information. It front-loades the action and resource, but could be slightly more descriptive without losing conciseness.

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

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

Given the tool has no parameters, an output schema exists, and annotations are rich, the description is adequate but minimal. It does not explain what constitutes a 'currency' or the format of the list, leaving some ambiguity.

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 tool has zero parameters, and the input schema covers 100% of the structure via examples. The description does not need to add parameter meaning. Baseline for 0 parameters is 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 'List currencies' clearly states a verb ('List') and a resource ('currencies'), indicating the tool retrieves a list. It distinguishes from the sibling 'currency' tool (which likely handles individual currencies). However, it does not specify the scope or format of the list.

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 no guidance on when to use this tool versus alternatives like 'currency' or other tools. There is no mention of prerequisites, context, or exclusions.

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 as true and destructiveHint as false. The description adds no additional behavioral context (e.g., that it retrieves a single currency by ID). No contradiction, but fails to leverage description to add 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?

At two words, it is under-specified rather than concise. The description should front-load critical information, but here it provides no actionable 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?

Despite having an output schema and informative annotations, the description fails to explain what the tool returns or how to use the currency_id parameter. For a single-parameter tool, it is incomplete.

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 0% and the description does not explain the single parameter 'currency_id'. The phrase 'Single currency.' gives no hints about the parameter's semantics, format, or examples beyond the schema's own examples.

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 'Single currency.' is extremely vague. It does not specify the action (e.g., retrieve, fetch, convert) or the resource context. It fails to differentiate from sibling tool 'currencies' which likely lists all currencies.

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?

No guidance on when to use this tool versus alternatives like 'currencies' or product tools. The description provides no context for appropriate 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds that it returns the top-N most relevant tools with names and descriptions, providing 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 front-loaded with purpose but includes a long list of domains, which is informative but slightly verbose. Still efficient overall.

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

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

Output schema is absent, but description clearly states what is returned: top-N most relevant tools with names and descriptions. No other gaps.

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

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

Schema coverage is 100% with both parameters described. The description provides example queries and specifies default and max for limit, adding 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 it finds tools by describing data or task, listing many domains. It distinguishes from sibling tools by advising to call this FIRST when multiple tools are available, making it a 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?

Explicitly says 'Use when you need to browse, search, look up, or discover what tools exist for' and gives specific examples. Also advises to call this FIRST when you have many tools and want to see the option 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 indicate read-only, open world, idempotent. Description adds specifics: returns SEC filings, fundamentals, patents, news, LEI with 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?

Packed with value, but the first sentence could be slightly tighter (e.g., 'Get a comprehensive company profile in one call'). Every sentence earns its place, but minor verbosity.

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 (aggregates many sources) and no output schema, the description clearly lists what is returned. No missing critical context for an agent 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 coverage is 100%. Description adds context: type is only 'company', value can be ticker or zero-padded CIK, and warns names won't work (pointing to resolve_entity).

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?

Opens with a clear verb+resource statement ('Get everything about a company in one call'), explicitly distinguishes itself as an aggregated profile tool replacing many others, and provides concrete example queries.

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?

Tells exactly when to use (user asks for profile, or avoid calling many tools), and importantly states when NOT to use: names not supported, must use resolve_entity first. This is explicit 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 provide destructiveHint=true and readOnlyHint=false. Description confirms deletion and adds context about clearing sensitive data, but does not add significant behavioral details beyond annotations.

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

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

Two concise sentences with no fluff. Front-loaded with purpose, then usage context.

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

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

Complete for a simple 1-param tool. Covers purpose, usage, and sibling tools. No output schema 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% with key described. Description mentions 'by key' but adds no extra semantic 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 states 'Delete a previously stored memory by key', which is a specific verb+resource. It distinguishes from siblings '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?

Explicitly states when to use: when context is stale, task is done, or clear sensitive data. Also mentions 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 (readOnlyHint, idempotentHint, etc.) already indicate safety. The description adds behavioral detail: it fetches the page, extracts title/description/key links, and outputs markdown. This goes beyond annotations and is accurate.

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

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

Three sentences, front-loaded with the core purpose. Every sentence adds value: what, how, use cases. No redundancy.

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

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

For a simple tool with 2 params and no output schema, the description explains the output format and usage scenarios. It could mention error handling or limitations (e.g., if site is inaccessible), but overall it's sufficiently complete.

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

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

Schema coverage is 100%, so the schema already documents both parameters. Description doesn't add new meaning beyond restating that url is the site to summarize and max_links is optional. 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?

Description explicitly states the tool generates an llms.txt file for a URL for AI crawlers. It mentions specific use cases (getting site indexed, drafting, auditing) which clearly distinguish it from sibling tools like ai_visibility_check or scan_competitor_ai_presence.

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 lists concrete useful scenarios (client site indexing, own project, competitor audit), providing clear context for when to use. It doesn't explicitly state when not to use, but the examples cover common 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?

Description discloses rate limits (5 per identifier per day), free usage, and that feedback doesn't count against tool-call quota. Annotations already provide readOnlyHint and destructiveHint, but description adds roadmap impact and team reading habits.

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

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

Single well-structured paragraph. Front-loaded with purpose, then usage guidelines, then constraints. Every sentence adds value. No redundant or vague statements.

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 simple purpose (submit feedback), the description covers all needed aspects: what, when, how, constraints. No output schema is needed. Completeness is high.

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

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

Schema coverage is 100% with good descriptions. Description adds extra context: message should be specific and in terms of Pipeworx tools/packs. Reinforces enum meanings but does not repeat schema entirely.

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 is for telling the Pipeworx team about something broken, missing, or needing existence. It enumerates specific use cases (bug, feature, data_gap, praise) and distinguishes itself from sibling tools (e.g., ask_pipeworx, discover_tools) as a feedback channel.

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: for bugs, missing features/data, praise. Provides clear negative guidance: don't paste the end-user prompt. Mentions rate limits and quota exemption. No alternative tools are needed for feedback, so no omission.

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. The description adds value by explaining data derivation (CF analytics-engine), privacy (no PII), and caching behavior (5min-1h), which are beyond annotation scope.

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 efficiently structured with a clear lead sentence followed by bullet-like use cases and technical notes. Slightly longer than minimal but each sentence contributes unique information.

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

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

Despite no output schema, the description explains what is returned (top tools, packs, count) and caching. It covers all necessary aspects for agent understanding and invocation.

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

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

Only one parameter (window) with full schema coverage. Description adds context on the meaning of shorter vs longer windows, enhancing beyond the enum description.

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

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

The description clearly states it returns top tools, packs, and total call volume over recent windows. It uniquely positions the tool among siblings by focusing on trending/aggregate data rather than individual queries.

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

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

Explicitly lists three distinct use cases (discovering hot data, confirming canonical tools, assessing alignment). Does not provide explicit when-not or alternatives, but the sibling list implies other tools for specific 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 indicate read-only, idempotent, and non-destructive behavior. The description adds context about searching, grouping, and checking monotonicity, plus the return format. 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 well-structured, using sections for modes and examples. Every sentence provides unique information, and it is concise without unnecessary repetition.

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

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

Given the lack of output schema, the description explains the return format (ranked opportunities with reasoning). It does not cover error conditions or limitations, but overall provides sufficient context for a search/analysis 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. The description adds value by defining the two modes, explaining parameter usage with examples, and clarifying cross-event scenarios.

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

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

The description clearly states it finds arbitrage opportunities by checking monotonicity violations, and distinguishes two modes (event and topic). However, it does not explicitly differentiate from sibling tools like 'polymarket_edges' or 'polymarket_kalshi_spread'.

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 each mode (event vs topic) and gives a concrete example where cross-event mode is necessary. It lacks explicit guidance on when not to use the tool or alternatives.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds rich detail: scanning top markets, grouping by asset, fetching price history once, computing model probability, ranking by |edge|, and returning top N with trade direction. 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 front-loaded with the main purpose and algorithm summary, but is somewhat lengthy. It includes necessary details like model type and data sources without being overly verbose. Minor trimming could improve conciseness.

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

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

No output schema is present, yet the description adequately explains the return format (top N ranked by edge magnitude with suggested trade direction). It covers the process, parameter usage, and algorithm steps, making it complete for a tool with 9 parameters.

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

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

Schema coverage is 100% with detailed descriptions. The description adds value by explaining the algorithm context (e.g., slippage subtracted before ranking, Kelly sizing logic), which enhances understanding 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?

The description clearly states the tool scans high-volume Polymarket markets to find where Pipeworx data disagrees with market price, specifically for discovering betting opportunities. It distinguishes from siblings by focusing on edge-based opportunities rather than arbitrage or general research.

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

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

The description explicitly frames the tool for the 'what should I bet on today' question and contrasts it with manual browsing. It implies usage for discovering edge opportunities, but does not explicitly list when not to use or compare to sibling tools like polymarket_arbitrage.

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 context beyond annotations: it explains the typical delta range (2-25pp), the data source (cross-venue), and the output format (leg-by-leg prices and spread). No contradictions with annotations.

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

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

The description is a single well-structured paragraph, front-loaded with the primary purpose, followed by explanation and modes. 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?

The description covers the two modes, output format, and parameter interaction. However, it does not explicitly state parameter combination requirements (e.g., at least one of topic or explicit pair) or error cases. Still fairly complete given no required parameters.

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

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

Schema coverage is 100%, but the description adds value by explaining the two modes, providing example topic values, and clarifying that explicit parameters override topic-mapped sides. This goes beyond the schema definitions.

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

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

The description clearly defines the tool as a cross-venue spread between Kalshi and Polymarket, explaining the price difference rationale. It explicitly distinguishes from siblings like polymarket_arbitrage by focusing on cross-venue comparison.

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

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

The description explains the two modes (topic and explicit) and gives examples, but does not explicitly state when not to use it or mention alternatives. However, the context is clear enough for 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=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, adequately covering safety and side-effect profile. The description adds no additional behavioral context, but it does not contradict 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?

At two words, the description is underspecified and not appropriately sized. Conciseness should not sacrifice substance; this lacks the minimal detail needed for tool selection.

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?

Even though an output schema exists, the description does not explain what the tool returns or its core purpose. For a tool with a single parameter and many siblings, this is insufficiently complete.

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

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

Schema description coverage is 0%, and the description does not clarify the meaning or format of the single required parameter 'product_id'. While the input schema provides examples (e.g., 'BTC-USD'), the description fails to add any semantic 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 'Single product.' is extremely vague and does not specify what action the tool performs (e.g., retrieve, list, search). It fails to differentiate from sibling tools like 'product_book' or 'product_candles' which clearly indicate distinct operations.

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?

No guidance on when to use this tool vs. alternatives. There is no mention of context, prerequisites, or exclusions, leaving the agent unable to decide between 'product' and similar tools.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds no behavioral context beyond what's in the structured fields, such as whether it returns a snapshot or continuous data, or any side effects.

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 (one word), but this sacrifices clarity and completeness. Every sentence should earn its place; here, the single word is insufficient and fails to convey critical information about the tool's functionality.

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 two parameters, an output schema, and many sibling tools, the description is grossly incomplete. It omits any explanation of what an order book is, how level is used, or how this differs from related tools like product or product_ticker.

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 input schema has 0% description coverage, so the description must explain parameters. It does not. The single word 'Orderbook.' provides no meaning for product_id or level. The examples in the schema partially compensate, but the description itself adds zero value to parameter understanding.

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

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

The description 'Orderbook.' is a single word that vaguely indicates the tool relates to an order book, but it lacks a specific verb and resource. It does not explicitly state what the tool does (e.g., retrieve, get, fetch) and is not differentiated from siblings like product_ticker or product_candles.

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?

There is no guidance on when to use this tool versus alternatives. The description offers no context about prerequisites, conditions, or exclusions. Siblings like product_trades or product_stats have similar names but no differentiation is 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 provide safety and idempotency hints, but description adds no behavioral context beyond what is already declared.

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 minimal but lacks substance; it is under-specified rather than 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 having 4 parameters and an output schema, the description provides no detail on inputs, outputs, or behavior, making it inadequate 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 description coverage is 0%, yet description fails to explain parameters like start, end, product_id, or granularity.

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 'OHLC candles' is vague; it does not specify an action (e.g., get, retrieve) and does not distinguish from sibling tools like product, product_ticker, or product_trades.

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?

No guidance on when to use this tool versus alternatives; lacks context on filtering, date ranges, or granularity.

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 convey readOnlyHint, idempotentHint, and destructiveHint. The description adds no behavioral details such as pagination, response size, or scoping beyond stating it lists trading pairs.

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 short sentence, which is concise. However, it could be slightly more structured without increasing length, e.g., specifying the scope or return 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 no parameters, an output schema, and rich annotations, the description is minimally complete. It lacks context about the nature of the pairs (e.g., all vs. filtered, order) which could be relevant 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?

There are no parameters, and schema coverage is 100%. The description appropriately does not add parameter detail since none exist. Baseline for 0 params is 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 'List trading pairs' clearly states the action and resource. It is concise and unambiguous, but does not differentiate from the sibling tool 'product' or contextualize its 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?

There is no guidance on when to use this tool vs. siblings like 'product', 'product_book', or 'product_trades'. The agent lacks context for selection.

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

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

Annotations already state readOnlyHint, so the description adds value by specifying the 24-hour time window. This is useful behavioral context beyond annotations.

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

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

Extremely brief (2 words), but at the cost of clarity. It is front-loaded but does not earn its place as it fails to convey essential information.

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

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

Given the single parameter and many sibling tools, the description is too minimal to provide complete context. The output schema exists but the description does not explain the tool's scope 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?

With 0% schema description coverage, the description must explain parameters but does not. It does not mention product_id or its format, leaving the agent with no added insight.

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 says '24h stats,' which indicates the tool provides statistics but is vague about which specific stats. It does not clearly differentiate from siblings like product_ticker or product_candles.

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?

No guidance on when to use this tool over other product tools. The description lacks context about appropriate scenarios or exclusions.

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

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

Annotations already provide readOnlyHint, idempotentHint, openWorldHint, and destructiveHint. The description adds no behavioral details (e.g., data freshness, rate limits) beyond what is declared 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 extremely concise—six words that convey core functionality with no unnecessary information. It is front-loaded and efficient.

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

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

For a simple read-only ticker tool with an output schema available, the description sufficiently states what data is returned. No further clarification is needed for 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?

The sole parameter product_id has no description in the schema (0% coverage). The description does not explain its purpose or accepted values, focusing only on output. This fails to add 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 'Best bid/ask + last trade' clearly states the tool returns the best bid, best ask, and last trade price for a product. This distinguishes it from sibling tools like product_book (order book) and product_trades (trade history), aligning with its name product_ticker.

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?

No guidance is provided on when to use this tool versus alternatives such as product_book, product_candles, or product_stats. The description only states the output, not context for selection.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and openWorldHint, so the description does not need to reiterate safety. However, it adds no behavioral context beyond that, such as pagination details, rate limits, or any side effects. The phrase 'Recent trades' implies time ordering but lacks specifics.

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 (3 words), but this sacrifices essential clarity. It is front-loaded but insufficient, lacking detail needed for a tool with multiple parameters and an output schema. Efficiency should not come at the cost of completeness.

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 4 parameters, complex pagination options, and an output schema, the description fails to provide necessary context. It does not explain what constitutes a 'recent trade', how pagination works, or the relationship between before/after and timestamp fields. The output schema exists, so return values are covered, but parameter usage and tool behavior are under-specified.

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 0%, requiring the description to compensate, but it does not explain any of the 4 parameters (after, limit, before, product_id). The examples in the schema provide some context, but the description itself adds zero meaning to the parameters.

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

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

The description 'Recent trades' vaguely indicates the tool returns recent trades for a product, but lacks a specific verb like 'get' or 'list'. It does not differentiate from sibling tools such as product_book or product_candles, which may have similar 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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or scenarios where another tool might be more appropriate, such as filtering by user or workspace.

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, idempotent, and non-destructive. The description adds context about scoping to identifier and pairing with remember/forget, which goes beyond annotations. No contradiction.

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

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

Three sentences, each serving a purpose: main action, usage, scope and pairing. Front-loaded and efficient. 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 simplicity of the tool (1 optional param, good annotations, no output schema needed), the description fully covers behavior, usage, and integration with siblings. No gaps.

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

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

Schema coverage is 100% with a clear description for the 'key' parameter. The description reinforces omitting key to list all, but adds little new meaning. Baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves a value by key or lists all keys if omitted, specifying the verb and resource. It distinguishes from siblings by mentioning pairing with remember and forget, and scoping to identifier.

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 guidance on when to use (look up stored context without re-deriving) and how to use (omit key to list all). It implicitly suggests not using for new information. Explicit mention of when not to use would improve, but it's clear.

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

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

Annotations already declare safe, read-only, idempotent behavior. The description adds valuable behavioral context: parallel fan-out to three sources, accepted date formats, and return structure (changes, count, URIs). This goes beyond what annotations provide.

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

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

The description is well-structured: starts with core purpose, then usage examples, then technical details. It is concise and front-loaded, though it could be slightly tighter without losing meaning.

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 format and multi-source nature. It lacks edge case handling (e.g., no results) but covers the essential scope for an agent 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 covers all parameters with 100% description coverage. The description adds light guidance (e.g., recommended values for 'since') but does not significantly extend understanding beyond the schema.

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

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

The description begins with a clear question identifying the tool's purpose ('What's new with a company'), provides specific example queries, and names the three data sources (SEC, GDELT, USPTO). It implicitly distinguishes itself from static tools like entity_profile by focusing on temporal updates.

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 usage scenarios with natural language examples and mentions monitoring use cases. However, it does not specify when not to use the tool or contrast with sibling tools like entity_profile for static information.

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

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

Annotations already mark idempotentHint=true and destructiveHint=false. Description adds context: persistence duration (24-hour for anonymous, persistent for authenticated) and scoping by identifier. 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 well-structured sentences: purpose, usage examples, and behavioral details. Front-loaded with core function. 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?

Completely addresses purpose, usage, behavior, and pairing with siblings for a simple 2-parameter tool. No missing information for an agent to use effectively.

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

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

Schema coverage is 100% with clear descriptions. Description marginally adds context by mentioning key-value pair and scoping but doesn't add significant new meaning beyond schema.

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

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

Clearly states the purpose: save data for reuse later. Provides concrete examples (resolved ticker, target address) and explicitly distinguishes from sibling tools 'recall' and 'forget'.

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

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

Descriptively says when to use: when discovering something worth carrying forward. Implicitly excludes transient data. Mentions pairing with recall/forget but no explicit when-not-to-use.

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

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

Annotations already declare readOnly, idempotent, nondestructive. Description adds behavioral context: returns IDs plus pipeworx:// citation URIs, provides examples. No contradiction; adds value beyond annotations.

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

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

Well-structured, front-loaded with purpose. Each sentence is informative. Slightly verbose with examples but not excessive. Efficient use of words.

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

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

No output schema, but description explains return values (IDs plus citation URIs). Covers input formats and usage context. Complete 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 coverage is 100%, but description adds meaning: explains how to use parameters (e.g., 'for company: ticker, CIK, or name'), gives examples, and clarifies enum options. More helpful than bare 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 the tool resolves entity names to canonical identifiers (CIK, ticker, RxCUI, LEI). Verb 'look up' and resource 'canonical/official identifier' are specific. Distinguishes from siblings by noting it replaces 2-3 lookups and is a prerequisite for other tools.

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

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

Explicitly states when to use: 'when a user mentions a name and you need the CIK...' and gives directive: 'Use this BEFORE calling other tools that need official identifiers.' Also identifies alternatives by stating it replaces multiple lookup calls.

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 readOnly=true, idempotent, openWorld. The description adds behavioral details: probing each entity via ai_visibility_check, ranking by score, returning score/confidence/signal density. No contradiction.

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

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

Two sentences plus a clarifying bullet. Front-loaded with main action. Every sentence adds value, no waste.

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

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

Despite no output schema, the description explains what the tool returns (ranked list with score, confidence, signal density). It covers purpose, method, and output 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 coverage is 100% so baseline is 3. The description adds minimal extra: 'First entry treated as subject for narrative' for entities. Otherwise it mostly restates schema info.

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

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

The description clearly states the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks them, and surfaces most/least recognized. It distinguishes from sibling tools like ai_visibility_check and compare_entities.

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

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

The description explicitly says it's useful for competitive AI-marketing audits and provides an example question. It implies when to use this vs the singular ai_visibility_check, but does not explicitly state when not to use or mention alternatives beyond the example.

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 'Server time.' adds no behavioral traits beyond the annotations (readOnlyHint, openWorldHint, idempotentHint). It does not disclose any side effects, required permissions, or return format. With annotations already covering safety, the description contributes minimal 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 extremely concise (two words), front-loaded, and wastes no space. However, it is a fragment rather than a full sentence, slightly reducing structure quality.

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 parameters, annotations cover safety, and an output schema exists, the description is borderline adequate. It could be improved by mentioning the return format or timezone, but it is minimally sufficient for a trivial 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?

There are no parameters, and the input schema is empty. The description does not need to add parameter details, and it correctly indicates the tool requires no input. Score baseline of 4 for zero 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 'Server time.' clearly indicates the resource (server time) but lacks a verb like 'Get' or 'Fetch'. It distinguishes itself from sibling tools like 'currencies' or 'product' by being uniquely about time, though no explicit differentiation is stated.

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?

No guidance is provided on when to use this tool versus alternatives like 'currencies' or 'product'. There is no mention of context, prerequisites, or typical 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?

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds return value details (verdicts, citation, percent delta) and explains it replaces multiple sequential calls.

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?

Every sentence adds value: purpose, usage triggers, domain limitation, and return structure. 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 single-parameter tool with rich annotations and no output schema, the description explains all necessary aspects: function, when to use, domain scope, and return structure.

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

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

Schema coverage is 100% with a good description of the 'claim' parameter. The tool description provides examples but does not add new semantic meaning beyond the schema.

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

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

The description clearly states the tool's function: 'Fact-check, verify, validate, or confirm/refute a natural-language factual claim'. It uses specific verbs and identifies the resource (claim). Sibling tools are diverse, and this tool stands out as a claim-verification tool.

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

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

Provides explicit usage context: 'Use when an agent needs to check whether something a user said is true' with example queries. Still, it does not mention when not to use or alternative tools among siblings.

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